Further, AI systems face added challenges in meeting compliance requirements across multiple interrelated subject matters, such as risk management, data governance, transparency, human oversight, and cybersecurity measures. Each of the evaluated areas demands specialized knowledge, making the documentation process complex and resource-intensive. Documenting compliance across these diverse and interrelated areas involves coordinating efforts across multiple teams, maintaining up-to-date records, and ensuring that all aspects of the AI system adhere to regulatory requirements, which can be a challenging and ongoing task. For example, effective risk management depends on accurate data governance to ensure that data used for risk assessments is reliable and compliant with privacy regulations, while robust cybersecurity measures are necessary to protect the data from breaches, thereby supporting both risk management and data governance efforts. Since the areas are interrelated, duplicating work wastes valuable resources such as CPU usage, storage capacity, and human effort, as multiple teams may redundantly process the same data, run similar compliance checks, and maintain overlapping documentation, leading to inefficiencies and increased operational costs.

Moreover, to monitor compliance of AI systems, decisions made by AI systems must be understood and interpreted by humans, which implicitly means that decisions and their rationale should be continuously stored and managed. The requirement presents several challenges, particularly in the context of complex AI models that can operate as “black boxes,” where the decision-making process is not easily interpretable. From the user's perspective, the AI model functions as a “black box,” where the input is fed into the system, and the output prediction is produced without visibility into the underlying logic. The opaque nature of AI systems makes it difficult to trace how specific decisions are made (especially with complex documentation), thereby complicating the identification of gaps in compliance.

As such, the inventors have further developed a system to use generative AI models, such as the LLMs in the above-described data generation platform, to identify and remediate gaps in artificial intelligence use cases. The system (1) uses an existing inventory and/or (2) uses the regulatory definitions to create an inventory of the organization's model use cases (i.e., observed model use cases). A model use case can be a specific application or scenario in which AI technologies are used (or can be used). The model use case can include a set of features detailing the context, objectives, and requirements of the AI system (e.g., a description of the problem being addressed, the context of the model use case, the data inputs and outputs, the AI models and algorithms used, the expected benefits or improvements). The inventory can include any applicable tools used by the organization, including third-party tools (e.g., from downstream suppliers), and can be identified through a Retrieval-Augmented Generation (RAG) search of operational data. The system can identify areas where additional information is required and formulate a request for additional information.

For each of the identified AI use cases in the inventory, the system examines the AI use case based on the applicable regulations (e.g., the EU AI Act, California SB-1047) using rule-based systems and/or one or more AI models. The AI use case is classified into a risk category based on the regulations. The classification can be performed through a rule-based system and/or neural network that analyzes the input data (i.e., the AI use case and the applicable regulations) and maps the AI use case to the predefined risk categories. Using the identified compliance requirements, the system identifies a set of gaps in compliance for each of the AI use cases. Gaps represent situations where an expected compliance requirement of the regulations is either absent or not functioning properly. To address the identified gaps, action plans are created outlining tasks to remediate the gaps. The system can, for example aggregate documentation required by the compliance requirements to remediate the gap and generate a timestamped compliance report that satisfies the regulations applicable to the AI use case.

Internally, the system can identify new gaps from automatically refreshed assessments and generate an alert based on the gap (e.g., generating an exceptional trigger for potential “Prohibited” or “High-Risk” AI use cases). In some implementations, the system converts existing documentation (e.g., previously aggregated documentation) into new documentation that meets new regulatory requirements. The system can analyze existing documentation in conjunction with the text of relevant regulations to classify the risk category (while disregarding the preassigned risk rating provided by other regulations). Subsequently, the system utilizes the existing documentation to generate new documentation that complies with the updated regulatory requirements.

Unlike conventional approaches that rely on manual processes, static documentation, and periodic audits, the disclosed systems and methods can generate an inventory, identified through a RAG search of operational data, ensuring that all relevant AI use cases are accounted for, thereby reducing the compliance burden and ensuring that even previously overlooked AI systems are included in the analysis. Further, unlike conventional methods that struggle with varying definitions of “AI system” across different regulations, the disclosed system examines each identified AI use case based on applicable regulations using rule-based systems and/or AI models. By classifying AI use cases into risk categories based on the particular regulations, the system ensures that each use case is evaluated according to the specific requirements of the relevant regulatory framework. This dynamic classification and gap identification process allows for real-time adjustments and continuous compliance, reducing the risk of regulatory breaches and associated penalties. Additionally, the system implements a risk management system that continuously evaluates AI systems throughout their lifecycle. By generating timestamped compliance reports and aggregating the required documentation, the system provides a dynamic and automated solution for continuous evaluation, thereby reducing the risk of non-compliance. The system addresses the challenges in meeting compliance requirements across multiple interrelated subject matters by using a single system of generative AI model(s) across the various subject matters, which reduces the need for duplicative work. The system's ability to aggregate documentation and generate compliance reports ensures that all interrelated areas are addressed cohesively, which allows organizations to allocate resources more effectively and reduce operational overhead.

Further, unlike conventional methods that struggle with the opaque nature of complex AI models, the system consolidates the compliance requirements and operational data into a set of gaps in compliance and/or compliance actions that are automatically executed. By mapping compliance requirements to the operational data of the AI systems, such as model inputs and outputs, decision-making processes, and performance metrics, the system can pinpoint specific areas where compliance gaps exist. For instance, if a regulation requires detailed documentation of data sources and processing methods, the system can automatically check the existing documentation against this requirement and flag any discrepancies as compliance gaps.

The methods disclosed herein cause a reduction in greenhouse gas emissions compared to traditional methods for operating models. Every year, approximately 40 billion tons of CO²are emitted around the world. Power consumption by digital technologies account for approximately 4% of this figure. Further, conventional user device and application settings can sometimes exacerbate the causes of climate change. For example, the average U.S. power plant expends approximately 500 grams of carbon dioxide for every kWh generated. The implementations disclosed herein for conserving hardware, software, and network resources can mitigate climate change by reducing and/or preventing additional greenhouse gas emissions into the atmosphere. For example, identifying and remediating gaps in AI use cases using generative AI models as described herein reduces electrical power consumption compared to traditional methods. In particular, automating computer-executable compliance and monitoring tasks reduces the need for extensive manual intervention and redundant processes, which can consume additional computational power and energy. Continuously monitoring compliance reduces the need for periodic, resource-heavy audits and assessments, which can traditionally require substantial computational power to process large volumes of data. Spikes in power consumption can lead to higher greenhouse gas emissions. Power plants may need to ramp up production quickly to meet sudden increases in demand, and may rely on less efficient and more polluting sources of energy. For example, instead of conducting quarterly or annual compliance reviews that involve extensive data processing and analysis, the disclosed system can perform compliance monitoring on an ongoing basis to process smaller, incremental data updates, leading to a more consistent energy consumption.

Moreover, in the U.S., datacenters are responsible for approximately 2% of the country's electricity use, while globally they account for approximately 200 terawatt Hours (TWh). Transferring 1 GB of data can produce approximately 3 kg of CO². Each GB of data downloaded thus results in approximately 3 kg of CO²emissions or other greenhouse gas emissions. The storage of 100 GB of data in the cloud every year produces approximately 0.2 tons of CO²or other greenhouse gas emissions. The continuous monitoring and dynamic compliance management described herein enables the system to detect and address compliance issues as they arise, rather than waiting for the next scheduled review. The proactive approach not only ensures that compliance is maintained more effectively but also reduces the likelihood of significant non-compliance issues that would require extensive corrective actions. By addressing potential problems early, the system lowers the need for resource-intensive remediation efforts, further conserving energy, and obviates the need for wasteful CO²emissions. Further, keeping AI models in compliance with environmental regulations ensures that the AI systems themselves adhere to standards that lower their environmental impact. By continuously monitoring and remediating gaps in the energy efficiency of AI operations, the system helps reduce the carbon footprint associated with data processing and storage. Compliance with environmental regulations can include requirements for energy efficiency, waste reduction, and sustainable resource use. By meeting these requirements, the system contributes to broader environmental goals, such as reducing greenhouse gas emissions and conserving natural resources. Therefore, the disclosed implementations mitigate climate change and the effects of climate change by reducing the amount of data stored and downloaded in comparison to conventional network technologies.

Attempting to create a system to dynamically identify and remediate gaps in compliance for AI use cases in view of the available conventional approaches created significant technological uncertainty. Creating such system required addressing several unknowns in conventional approaches in AI compliance management, such as how to interpret regulations and apply the regulations to the AI use case. AI regulations vary significantly across different jurisdictions and industries, making it challenging to create a system that can accurately interpret and apply these complex and variable regulatory standards. Similarly, conventional approaches in AI compliance management did not provide methods of continuously learning and adapting to new regulatory changes and updates.

Conventional approaches rely on periodic reviews and audits, which are not sufficient for the dynamic nature of AI systems. In view of regulations such as the EU AI act and the California Senate Bill 1047 that require continuous compliance, conventional approaches were insufficient because the conventional approaches did not continuously tracking compliance status and identifying gaps as they arise, but rather during scheduled audits. For example, a conventional system may manually review logs, data processing workflows, and model outputs. The process can include extracting data from various sources, such as databases, log files, and application programming interfaces (APIs), and then manually cross-referencing the data against regulatory requirements. The manual process is not only time-consuming but also prone to human error, and it fails to capture compliance issues that may arise between audits. Conversely, the disclosed system determines how to dynamically meet the requirements of regulations by integrating with the AI models and data pipelines in real-time, using APIs and event-driven architectures to capture data as it is processed. For instance, the system can automatically scan and interpret regulatory texts, mapping the requirements to specific data processing activities and model behaviors. Further, the system can identify unusual patterns in data access or processing that may suggest a breach of compliance, and can automatically execute computer-executable tasks to remediate breaches in compliance.

To overcome the technological uncertainties, the inventors systematically evaluated multiple design alternatives. For example, the inventors tested various machine learning algorithms to determine which would be most effective for dynamic compliance monitoring given the variable data in an AI use case. The inventors experimented with a rule-based approach where predefined rules were manually coded to map AI use cases to specific regulatory criteria. The method involved creating an extensive database of rules that corresponded to different regulations and manually updating these rules as new regulations emerged. Additionally, the inventors explored a template-based approach, where compliance templates were created for different types of AI applications, and these templates were used to guide the compliance monitoring process.

However, the rule-based approach proved to be inflexible and difficult to maintain. As regulations evolved, the manual updating of rules becomes increasingly cumbersome and error-prone, leading to delays in compliance updates and potential gaps in regulatory coverage. Similarly, the template-based approach lacked the granularity needed to address the specific nuances of different AI use cases due to the variability in regulations. The templates were too generic, resulting in either overly broad compliance checks that can generate numerous false positives or overly narrow checks that can miss compliance issues.

Thus, the inventors experimented with different methods for dynamically identifying and remediating gaps in compliance. For example, the inventors tested various machine learning models to analyze regulatory texts (e.g., using NLP) and automatically extract relevant compliance criteria to create a system that could adapt to new regulations in real-time. The system can map the extracted criteria to specific AI use cases using, for example, classification models, such as support vector machines (SVM) and random forests, to categorize AI use cases based on their risk profiles and regulatory requirements. The system can use the criteria to continuously monitor AI applications, to identify deviations from expected behavior that could indicate compliance issues.

While the current description provides examples related to LLMs, one of skill in the art would understand that the disclosed techniques can apply to other forms of machine learning or algorithms, including unsupervised, semi-supervised, supervised, and reinforcement learning techniques. For example, the disclosed data generation platform can evaluate model outputs from support vector machine (SVM), k-nearest neighbor (KNN), decision-making, linear regression, random forest, naïve Bayes, or logistic regression algorithms, and/or other suitable computational models.

In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of implementations of the present technology. It will be apparent, however, to one skilled in the art that implementation of the present technology can be practiced without some of these specific details.

The phrases “in some implementations,” “in several implementations,” “according to some implementations,” “in the implementations shown,” “in other implementations,” and the like generally mean the specific feature, structure, or characteristic following the phrase is included in at least one implementation of the present technology and can be included in more than one implementation. In addition, such phrases do not necessarily refer to the same implementations or different implementations.

Overview of the Data Generation Platform

FIG. 1 shows an illustrative environment 100 for evaluating machine learning model inputs (e.g., language model prompts) and outputs for model selection and validation, in accordance with some implementations of the present technology. For example, the environment 100 includes the data generation platform 102, which is capable of communicating with (e.g., transmitting or receiving data to or from) a data node 104 and/or third-party databases 108a-108n via a network 150. The data generation platform 102 can include software, hardware, or a combination of both and can reside on a physical server or a virtual server (e.g., as described in FIG. 3) running on a physical computer system. For example, the data generation platform 102 can be distributed across various nodes, devices, or virtual machines (e.g., as in a distributed cloud server). In some implementations, the data generation platform 102 can be configured on a user device (e.g., a laptop computer, smartphone, desktop computer, electronic tablet, or another suitable user device). Furthermore, the data generation platform 102 can reside on a server or node and/or can interface with third-party databases 108a-108n directly or indirectly.

The data node 104 can store various data, including one or more machine learning models, prompt validation models, associated training data, user data, performance metrics and corresponding values, validation criteria, and/or other suitable data. For example, the data node 104 includes one or more databases, such as an event database (e.g., a database for storage of records, logs, or other information associated with LLM-related user actions), a vector database, an authentication database (e.g., storing authentication tokens associated with users of the data generation platform 102), a secret database, a sensitive token database, and/or a deployment database.

An event database can include data associated with events relating to the data generation platform 102. For example, the event database stores records associated with users' inputs or prompts for generation of an associated natural language output (e.g., prompts intended for processing using an LLM). The event database can store timestamps and the associated user requests or prompts. In some implementations, the event database can receive records from the data generation platform 102 that include model selections/determinations, prompt validation information, user authentication information, and/or other suitable information. For example, the event database stores platform-level metrics (e.g., bandwidth data, central processing unit (CPU) usage metrics, and/or memory usage associated with devices or servers associated with the data generation platform 102). By doing so, the data generation platform 102 can store and track information relating to performance, errors, and troubleshooting. The data generation platform 102 can include one or more subsystems or subcomponents. For example, the data generation platform 102 includes a communication engine 112, an access control engine 114, a breach mitigation engine 116, a performance engine 118, and/or a generative model engine 120.

A vector database can include data associated with vector embeddings of data. For example, the vector database includes a numerical representations (e.g., arrays of values) that represent the semantic meaning of unstructured data (e.g., text data, audio data, or other similar data). For example, the data generation platform 102 receives inputs such as unstructured data, including text data, such as a prompt, and utilize a vector encoding model (e.g., with a transformer or neural network architecture) to generate vectors within a vector space that represents meaning of data objects (e.g., of words within a document). By storing information within a vector database, the data generation platform 102 can represent inputs, outputs, and other data in a processable format (e.g., with an associated LLM), thereby improving the efficiency and accuracy of data processing.

An authentication database can include data associated with user or device authentication. For example, the authentication database includes stored tokens associated with registered users or devices of the data generation platform 102 or associated development pipeline. For example, the authentication database stores keys (e.g., public keys that match private keys linked to users and/or devices). The authentication database can include other user or device information (e.g., user identifiers, such as usernames, or device identifiers, such as medium access control (MAC) addresses). In some implementations, the authentication database can include user information and/or restrictions associated with these users.

A sensitive token (e.g., secret) database can include data associated with secret or otherwise sensitive information. For example, secrets can include sensitive information, such as application programming interface (API) keys, passwords, credentials, or other such information. For example, sensitive information includes personally identifiable information (PII), such as names, identification numbers, or biometric information. By storing secrets or other sensitive information, the data generation platform 102 can evaluate prompts and/or outputs to prevent breaches or leakage of such sensitive information.

A deployment database can include data associated with deploying, using, or viewing results associated with the data generation platform 102. For example, the deployment database can include a server system (e.g., physical or virtual) that stores validated outputs or results from one or more LLMs, where such results can be accessed by the requesting user.

The data generation platform 102 can receive inputs (e.g., prompts), training data, validation criteria, and/or other suitable data from one or more devices, servers, or systems. The data generation platform 102 can receive such data using communication engine 112, which can include software components, hardware components, or a combination of both. For example, the communication engine 112 includes or interfaces with a network card (e.g., a wireless network card and/or a wired network card) that is associated with software to drive the card and enables communication with network 150. In some implementations, the communication engine 112 can also receive data from and/or communicate with the data node 104, or another computing device. The communication engine 112 can communicate with the access control engine 114, the breach mitigation engine 116, the performance engine 118, and the generative model engine 120.

In some implementations, the data generation platform 102 can include the access control engine 114. The access control engine 114 can perform tasks relating to user/device authentication, controls, and/or permissions. For example, the access control engine 114 receives credential information, such as authentication tokens associated with a requesting device and/or user. In some implementations, the access control engine 114 can retrieve associated stored credentials (e.g., stored authentication tokens) from an authentication database (e.g., stored within the data node 104). The access control engine 114 can include software components, hardware components, or a combination of both. For example, the access control engine 114 includes one or more hardware components (e.g., processors) that are able to execute operations for authenticating users, devices, or other entities (e.g., services) that request access to an LLM associated with the data generation platform 102. The access control engine 114 can directly or indirectly access data, systems, or nodes associated with the third-party databases 108a-108n and can transmit data to such nodes. Additionally or alternatively, the access control engine 114 can receive data from and/or send data to the communication engine 112, the breach mitigation engine 116, the performance engine 118, and/or the generative model engine 120.

The breach mitigation engine 116 can execute tasks relating to the validation of inputs and outputs associated with the LLMs. For example, the breach mitigation engine 116 validates inputs (e.g., prompts) to prevent sensitive information leakage or malicious manipulation of LLMs, as well as validate the security or safety of the resulting outputs. The breach mitigation engine 116 can include software components (e.g., modules/virtual machines that include prompt validation models, performance criteria, and/or other suitable data or processes), hardware components, or a combination of both. As an illustrative example, the breach mitigation engine 116 monitors prompts for the inclusion of sensitive information (e.g., PII), or other forbidden text, to prevent leakage of information from the data generation platform 102 to entities associated with the target LLMs. The breach mitigation engine 116 can communicate with the communication engine 112, the access control engine 114, the performance engine 118, the generative model engine 120, and/or other components associated with the network 150 (e.g., the data node 104 and/or the third-party databases 108a-108n).

The performance engine 118 can execute tasks relating to monitoring and controlling performance of the data generation platform 102 (e.g., or the associated development pipeline). For example, the performance engine 118 includes software components (e.g., performance monitoring modules), hardware components, or a combination thereof. To illustrate, the performance engine 118 can estimate performance metric values associated with processing a given prompt with a selected LLM (e.g., an estimated cost or memory usage). By doing so, the performance engine 118 can determine whether to allow access to a given LLM by a user, based on the user's requested output and the associated estimated system effects. The performance engine 118 can communicate with the communication engine 112, the access control engine 114, the performance engine 118, the generative model engine 120, and/or other components associated with the network 150 (e.g., the data node 104 and/or the third-party databases 108a-108n).

The generative model engine 120 can execute tasks relating to machine learning inference (e.g., natural language generation based on a generative machine learning model, such as an LLM). The generative model engine 120 can include software components (e.g., one or more LLMs, and/or API calls to devices associated with such LLMs), hardware components, and/or a combination thereof. To illustrate, the generative model engine 120 can provide users' prompts to a requested, selected, or determined model (e.g., LLM) to generate a resulting output (e.g., to a user's query within the prompt). As such, the generative model engine 120 enables flexible, configurable generation of data (e.g., text, code, or other suitable information) based on user input, thereby improving the flexibility of software development or other such tasks. The generative model engine 120 can communicate with the communication engine 112, the access control engine 114, the performance engine 118, the generative model engine 120, and/or other components associated with the network 150 (e.g., the data node 104 and/or the third-party databases 108a-108n).

Engines, subsystems, or other components of the data generation platform 102 are illustrative. As such, operations, subcomponents, or other aspects of particular subsystems of the data generation platform 102 can be distributed, varied, or modified across other engines. In some implementations, particular engines can be deprecated, added, or removed. For example, operations associated with breach mitigation are performed at the performance engine 118 instead of at the breach mitigation engine 116.

Suitable Computing Environments for the Data Generation Platform

FIG. 2 shows a block diagram showing some of the components typically incorporated in at least some of the computer systems and other devices 200 on which the disclosed system (e.g., the data generation platform 102) operates in accordance with some implementations of the present technology. In various implementations, these computer systems and other device(s) 200 can include server computer systems, desktop computer systems, laptop computer systems, netbooks, mobile phones, personal digital assistants, televisions, cameras, automobile computers, electronic media players, web services, mobile devices, watches, wearables, glasses, smartphones, tablets, smart displays, virtual reality devices, augmented reality devices, etc. In various implementations, the computer systems and devices include zero or more of each of the following: input components 204, including keyboards, microphones, image sensors, touch screens, buttons, track pads, mice, compact disc (CD) drives, digital video disc (DVD) drives, 3.5 mm input jack, High-Definition Multimedia Interface (HDMI) input connections, Video Graphics Array (VGA) input connections, Universal Serial Bus (USB) input connections, or other computing input components; output components 206, including display screens (e.g., liquid crystal displays (LCDs), organic light-emitting diodes (OLEDs), cathode ray tubes (CRTs), etc.), speakers, 3.5 mm output jack, lights, light emitting diodes (LEDs), haptic motors, or other output-related components; processor(s) 208, including a CPU for executing computer programs, a GPU for executing computer graphic programs and handling computing graphical elements; storage(s) 210, including at least one computer memory for storing programs (e.g., application(s) 212, model(s) 214, and other programs) and data while they are being used, including the facility and associated data, an operating system including a kernel, and device drivers; a network connection component(s) 216 for the computer system to communicate with other computer systems and to send and/or receive data, such as via the Internet or another network and its networking hardware, such as switches, routers, repeaters, electrical cables and optical fibers, light emitters and receivers, radio transmitters and receivers, and the like; a persistent storage(s) device 218, such as a hard drive or flash drive for persistently storing programs and data; and computer-readable media drives 220 (e.g., at least one non-transitory computer-readable medium) that are tangible storage means that do not include a transitory, propagating signal, such as a floppy, CD-ROM, or DVD drive, for reading programs and data stored on a computer-readable medium. While computer systems configured as described above are typically used to support the operation of the facility, those skilled in the art will appreciate that the facility can be implemented using devices of various types and configurations and having various components.

FIG. 3 is a system diagram illustrating an example of a computing environment 300 in which the disclosed system operates in some implementations of the present technology. In some implementations, environment 300 includes one or more client computing devices 302a-302d, examples of which can host graphical user interfaces associated with client devices. For example, one or more of the client computing devices 302a-302d includes user devices and/or devices associated with services requesting responses to queries from LLMs. Client computing devices 302 operate in a networked environment using logical connections through network 304 (e.g., the network 150) to one or more remote computers, such as a server computing device (e.g., a server system housing the data generation platform 102 of FIG. 1). In some implementations, client computing devices 302 can correspond to device 200 (FIG. 2).

In some implementations, server computing device 306 is an edge server that receives client requests and coordinates fulfillment of those requests through other servers, such as server computing devices 310a-310c. In some implementations, server computing devices 306 and 310 comprise computing systems. Though each server computing device 306 and 310 is displayed logically as a single server, server computing devices can each be a distributed computing environment encompassing multiple computing devices located at the same or at geographically disparate physical locations. In some implementations, each server computing device 310 corresponds to a group of servers.

Client computing devices 302 and server computing devices 306 and 310 can each act as a server or client to other server or client devices. In some implementations, server computing devices (306, 310a-310c) connect to a corresponding database (308, 312a-312c). For example, the corresponding database includes a database stored within the data node 104 (e.g., a sensitive token database, an event database, or another suitable database). As discussed above, each server computing device 310 can correspond to a group of servers, and each of these servers can share a database or can have its own database (and/or interface with external databases, such as third-party databases 108a-108n). In addition to information described concerning the data node 104 of FIG. 1, databases 308 and 312 can warehouse (e.g., store) other suitable information, such as sensitive or forbidden tokens, user credential data, authentication data, graphical representations, code samples, system policies or other policies, templates, computing languages, data structures, software application identifiers, visual layouts, computing language identifiers, mathematical formulae (e.g., weighted average, weighted sum, or other mathematical formulas), graphical elements (e.g., colors, shapes, text, images, multimedia), system protection mechanisms (e.g., prompt validation model parameters or criteria), software development or data processing architectures, machine learning models, AI models, training data for AI/machine learning models, historical information, or other information.

Though databases 308 and 312 are displayed logically as single units, databases 308 and 312 can each be a distributed computing environment encompassing multiple computing devices, can be located within their corresponding server, or can be located at the same or at geographically disparate physical locations.

Network 304 (e.g., corresponding to the network 150) can be a local area network (LAN) or a wide area network (WAN) but can also be other wired or wireless networks. In some implementations, network 304 is the Internet or some other public or private network. Client computing devices 302 are connected to network 304 through a network interface, such as by wired or wireless communication. While the connections between server computing device 306 and server computing device 310 are shown as separate connections, these connections can be any kind of LAN, WAN, wired network, or wireless network, including network 304 or a separate public or private network.

Example Implementations of Models in the Data Generation Platform

FIG. 4 shows a diagram of an AI model, in accordance with some implementations of the present technology. AI model 400 is shown. In some implementations, AI model 400 can be any AI model. In some implementations, AI model 400 can be part of, or work in conjunction with, server computing device 306 (FIG. 3). For example, server computing device 306 can store a computer program that can use information obtained from AI model 400, provide information to AI model 400, or communicate with AI model 400. In other implementations, AI model 400 can be stored in database 308 and can be retrieved by server computing device 306 to execute/process information related to AI model 400, in accordance with some implementations of the present technology.

In some implementations, AI model 400 can be a machine learning model 402. Machine learning model 402 can include one or more neural networks or other machine learning models. As an example, neural networks can be based on a large collection of neural units (or artificial neurons). Neural networks can loosely mimic the manner in which a biological brain works (e.g., via large clusters of biological neurons connected by axons). Each neural unit of a neural network can be connected with many other neural units of the neural network. Such connections can be enforcing or inhibitory in their effect on the activation state of connected neural units. In some implementations, each individual neural unit can have a summation function that combines the values of all its inputs together. In some implementations, each connection (or the neural unit itself) can have a threshold function such that the signal must surpass the threshold before it propagates to other neural units. These neural network systems can be self-learning and trained, rather than explicitly programmed, and can perform significantly better in certain areas of problem solving, as compared to traditional computer programs. In some implementations, neural networks can include multiple layers (e.g., where a signal path traverses from front layers to back layers). In some implementations, backpropagation techniques can be utilized by the neural networks, where forward stimulation is used to reset weights on the “front” neural units. In some implementations, stimulation and inhibition for neural networks can be more free-flowing, with connections interacting in a more chaotic and complex fashion.

As an example, with respect to FIG. 4, machine learning model 402 can take inputs 404 and provide outputs 406. In one use case, outputs 406 can be fed back to machine learning model 402 as input to train machine learning model 402 (e.g., alone or in conjunction with user indications of the accuracy of outputs 406, labels associated with the inputs, or other reference feedback information). In another use case, machine learning model 402 can update its configurations (e.g., weights, biases, or other parameters) based on its assessment of its prediction (e.g., outputs 406) and reference feedback information (e.g., user indication of accuracy, reference labels, or other information). In another use case, where machine learning model 402 is a neural network, connection weights can be adjusted to reconcile differences between the neural network's prediction and the reference feedback. In a further use case, one or more neurons (or nodes) of the neural network can require that their respective errors are sent backward through the neural network to them to facilitate the update process (e.g., backpropagation of error). Updates to the connection weights can, for example, be reflective of the magnitude of error propagated backward after a forward pass has been completed. In this way, for example, the machine learning model 402 can be trained to generate better predictions.

As an example, where the prediction models include a neural network, the neural network can include one or more input layers, hidden layers, and output layers. The input and output layers can respectively include one or more nodes, and the hidden layers can each include a plurality of nodes. When an overall neural network includes multiple portions trained for different objectives, there may or may not be input layers or output layers between the different portions. The neural network can also include different input layers to receive various input data. Also, in differing examples, data can be input to the input layer in various forms, and in various dimensional forms input to respective nodes of the input layer of the neural network. In the neural network, nodes of layers other than the output layer are connected to nodes of a subsequent layer through links for transmitting output signals or information from the current layer to the subsequent layer, for example. The number of the links can correspond to the number of the nodes included in the subsequent layer. For example, in adjacent fully connected layers, each node of a current layer can have a respective link to each node of the subsequent layer, noting that in some examples such full connections can later be pruned or minimized during training or optimization. In a recurrent structure, a node of a layer can be again input to the same node or layer at a subsequent time, while in a bi-directional structure, forward and backward connections can be provided. The links are also referred to as connections or connection weights, referring to the hardware-implemented connections or the corresponding “connection weights” provided by those connections of the neural network. During training and implementation, such connections and connection weights can be selectively implemented, removed, and varied to generate or obtain a resultant neural network that is thereby trained and that can be correspondingly implemented for the trained objective, such as for any of the above example recognition objectives.

Mapping Gaps in Controls to Operative Standards Using the Data Generation Platform

FIG. 5 is an illustrative diagram illustrating an example environment 500 of a platform for automatically managing guideline compliance, in accordance with some implementations of the present technology. Environment 500 includes user 502, platform 504, data provider 506, AI model proxy 508, LLM 510, data cache 512, prompt store 514, and execution store log 516. Platform 504 is implemented using components of example devices 200 and computing devices 302 illustrated and described in more detail with reference to FIG. 2 and FIG. 3, respectively. Likewise, implementations of example environment 500 can include different and/or additional components or can be connected in different ways.

User 502 interacts with the platform 504 via, for example, a user interface. Platform 504 can be the same as or similar to data generation platform 102 with reference to FIG. 1. Users 502 can input data, configure compliance parameters, and manage guideline compliance performance through an intuitive interface provided by the platform. The platform 504 can perform a variety of compliance management tasks, such as compliance checks and regulatory analyses.

Data provider 506 supplies the platform 504 with the data used in the management, which can include regulatory guidelines, compliance requirements, organizational guidelines, and other relevant information. The data supplied by data provider 506 can be accessed via an application programming interface (API) or database that contains policies, obligations, and/or controls in operative standards. In some implementations, the data supplied by data provider 506 contains the publications (e.g., regulatory guidelines, compliance requirements, organizational guidelines) themselves. The structured repository of data provider 506 allows platform 504 to efficiently retrieve and use the data in different management processes. In some implementations, data provider 506 includes existing mappings associated with the operative standards. For example, the pre-established mappings can be between the operative standards and gaps (e.g., issues). In another example, the pre-established mappings can be between the operative standards and publications. Using the existing relationships, the platform 504 can more efficiently map particular identified gaps to the relevant operative standards. For example, if a newly identified gap is similar to or the same as a previously identified gap (e.g., shares similar scenario attributes, metadata tags) within the pre-existing mappings, the platform 504 can use the pre-existing mapping of the previously identified gap to more easily identify the mapping for the newly identified gap.

AI model proxy 508 is an intermediary between the platform and the large language model (LLM) 510. AI model proxy 508 facilitates the communication and data exchange between the platform 504 and the LLM 510. AI model proxy 508, in some implementations, operates as a plugin to interconnect the platform 504 and the LLM 510. The AI model proxy 508, in some implementations, includes distinct modules, such as data interception, inspection, or action execution. In some implementations, containerization methods such as Docker are used within the AI model proxy 508 to ensure uniform deployment across environments and minimize dependencies. LLM 510 analyzes data input by user 502 and data obtained from data provider 506 to identify patterns and generate compliance-related outputs. The AI model proxy 508, in some implementations, enforces access control policies to safeguard sensitive data and functionalities exposed to the LLM 510. For example, the AI model proxy 508 can sanitize the data received from the platform 504 using encryption standards, token-based authentication, and/or role-based access controls (RBAC) to protect sensitive information. The data received can be encrypted to ensure that all sensitive information is transformed into an unreadable format, accessible only through decryption with the appropriate keys. Token-based authentication can be used by generating a unique token for each user session or transaction. The token acts as a digital identifier by verifying the user's identity and granting access to specific data or functions within the system. Additionally, RBACs can restrict data access based on the user's role within the organization. Each role can be assigned specific permissions to ensure that users only access data relevant to the users' responsibilities.

In some implementations, AI model proxy 508 employs content analysis to discern between the sensitive and non-sensitive by identifying specific patterns, keywords, or formats indicative of sensitive information. In some implementations, the list of indicators of sensitive information is generated by an internal generative AI model within the platform 504 (e.g., with a command set that resembles “generate a plurality of examples of PII”). The generative AI model can be trained on a dataset containing examples of sensitive data elements, such as personally identifiable information (PII), financial records, or other confidential information. Once the AI model has been trained, the AI model can generate indicators (e.g., specific patterns, keywords, or formats) of sensitive information based on the model's learned associations. For example, gap data that includes sensitive financial information such as account numbers, transaction details, and personal information of stakeholders can be identified and subsequently removed and/or masked.

Data cache 512 can store data for a period of time to reduce the time required to access frequently used information. Data cache 512 ensures that the system can quickly retrieve necessary data without repeatedly querying the data provider 506, thus improving the overall efficiency of platform 504. In some implementations, a caching strategy is implemented that includes cache eviction policies, such as least recently used (LRU) or time-based expiration, to ensure that the cache remains up-to-date and responsive while optimizing memory usage. LRU allows the data cache 312 to keep track of which data items have been accessed most recently. When the data cache 312 reaches maximum capacity and needs to evict an item (e.g., data packets) to make room for new data, the data cache 312 will remove the least recently used item. Time-based expiration involves setting a specific time duration for which data items are considered valid in the data cache 312. Once this duration expires, the data item is automatically invalidated and removed from the data cache 312 to preserve space in the data cache 312.

Prompt store 514 contains predefined prompts that guide the LLM 510 in processing data and generating outputs. Prompt store 514 is a repository for pre-existing prompts that are stored in a structured and accessible format (e.g., using distributed databases or NoSQL stores), which allows for efficient retrieval and utilization by the AI model 506. In some implementations, the prompts are preprocessed to remove any irrelevant information, standardize the format, and/or organize the prompts into a structured database schema. In some implementations, prompt store 514 is a vector store where the prompts are vectorized and stored in a vector space model, and each prompt is mapped to a high-dimensional vector representing the prompt's semantic features and relationships with other prompts. In some implementations, the prompts are stored using graph databases such as Neo4j™ or Amazon Neptune™. Graph databases represent data as nodes and edges, allowing for the modeling of relationships between prompts to demonstrate the interdependencies. In some implementations, the prompts are stored in a distributed file system such as Apache Hadoop™ or Google Cloud Storage™. These systems offer scalable storage for large volumes of data and support parallel processing and distributed computing. Prompts stored in a distributed file system can be accessed and processed by multiple nodes simultaneously, which allows for faster retrieval and analysis by the system. For example, the details of a particular gap, such as relevant metrics, severity level, and/or specific publication references, can be used to structure a prompt for the LLM 510 by inserting the details into appropriate places in the predefined prompt.

Execution store log 516 records some or all actions and processes executed by the platform 504. Execution store log 516 can serve as an audit trail, providing a history of compliance activities and decisions made by the platform 504. Each logged entry in execution store log 515 can include details such as timestamps, user identifiers, specific actions performed, and relevant contextual information. Execution store log 516, in some implementations, can be accessed via the platform 504 via an API.

FIG. 6 is an illustrative diagram illustrating an example environment 600 of the platform using guidelines and gaps in controls to generate mapped gaps, in accordance with some implementations of the present technology. Environment 600 includes guidelines 602, operative standards 604, gaps 606, platform 608, and mapped gaps 610. Platform 608 is the same as or similar to platform 504 with reference to FIG. 5. Implementations of example environment 500 can include different and/or additional components or can be connected in different ways.

Guidelines 602 can include publications of regulations, standards, and policies that organizations adhere to. Guidelines 602 serve as the benchmark against which compliance is measured. Guidelines 602 can include publications such as jurisdictional guidelines and organizational guidelines. Jurisdictional guidelines (e.g., governmental regulations) can include guidelines gathered from authoritative sources such as government websites, legislative bodies, and regulatory agencies. Jurisdictional guidelines can be published in legal documents or official publications and cover aspects related to the development, deployment, and use of AI technologies within specific jurisdictions. For example, the California Consumer Privacy Act (CCPA) in the United States mandates cybersecurity measures such as encryption, access controls, and data breach notification requirements to protect personal data. As such, AI developers must implement cybersecurity measures (such as encryption techniques) within the AI models they design and build to ensure the protection of sensitive user data and compliance with the regulations. Organizational guidelines include internal policies, procedures, and guidelines established by organizations to govern activities within the organization's operations. Organizational guidelines can be developed in alignment with industry standards, legal requirements, best practices, and organizational objectives. For example, organizational guidelines can require AI models to include certain access controls to restrict unauthorized access to the model's APIs or data and/or have a certain level of resilience before deployment.

In some implementations, guidelines 602 can any one of text, image, audio, video or other computer-ingestible format. For guidelines 602 that are not text (e.g., image, audio, and/or video), the guidelines 602 can first be transformed into text. Optical character recognition (OCR) can be used for images containing text, and speech-to-text algorithms can be used for audio inputs. For example, an audio recording detailing financial guidelines can be converted into text using a speech-to-text engine that allows the system to parse and integrate the text output into the existing guidelines 602. Similarly, a video demonstrating a particular procedure or protocol can be processed to extract textual information (e.g., extracting captions).

In some implementations, in cases where transforming to text is not feasible or desirable, the system can use vector comparisons to handle non-text inputs directly. For example, images and audio files can be converted into numerical vectors through feature extraction techniques (e.g., by using Convolutional Neural Networks (CNNs) for images and using Mel-Frequency Cepstral Coefficients (MFCCs) for audio files). The vectors represent the corresponding characteristics of the input data (e.g., edges, texture, or shapes of the image, or the spectral features of the audio file).

In some implementations, the guidelines 602 can be stored in a vector store. The vector store stores the guidelines 602 in a structured and accessible format (e.g., using distributed databases or NoSQL stores), which allows for efficient retrieval and utilization by the platform 608. In some implementations, the guidelines 602 are preprocessed to remove any irrelevant information, standardize the format, and/or organize the guidelines 602 into a structured database schema. Once the guidelines 602 are prepared, the guidelines 602 can be stored in a vector store using distributed databases or NoSQL stores. To store the guidelines 602 in the vector store, the guidelines 602 can be encoded into vector representations. The textual data of the guidelines 602 are transformed into numerical vectors that capture the semantic meaning and relationships between words or phrases in the guidelines 602. For example, the text is encoded into vectors using word embeddings and/or TF-IDF encoding. Word embeddings, such as Word2Vec or GloVe, learn vector representations of words based on the word's contextual usage in a large corpus of text data. Each word is represented by a vector in a high-dimensional space, where similar words have similar vector representations. TF-IDF (Term Frequency-Inverse Document Frequency) encoding calculates the importance of a word in a guideline relative to the word's frequency in the entire corpus of guidelines 602. For example, the system can assign higher weights to words that are more unique to a specific document and less common across the entire corpus.

In some implementations, the guidelines 602 are stored using graph databases such as Neo4j™ or Amazon Neptune™. Graph databases represent data as nodes and edges, allowing for the modeling of relationships between guidelines 602 to demonstrate the interdependencies. In some implementations, the guidelines 602 are stored in a distributed file system such as Apache Hadoop™ or Google Cloud Storage™. These systems offer scalable storage for large volumes of data and support parallel processing and distributed computing.

The vector store can be stored in a cloud environment hosted by a cloud provider, or a self-hosted environment. In a cloud environment, the vector store has the scalability of cloud services provided by platforms (e.g., AWS™, Azure™). Storing the vector store in a cloud environment entails selecting the cloud service, provisioning resources dynamically through the provider's interface or APIs, and configuring networking components for secure communication. Cloud environments allow the vector store to scale storage capacity without the need for manual intervention. As the demand for storage space grows, additional resources can be automatically provisioned to meet the increased workload. Additionally, cloud-based caching modules can be accessed from anywhere with an internet connection, providing convenient access to historical data for users across different locations or devices. Conversely, in a self-hosted environment, the vector store is stored on a private web server. Deploying the vector store in a self-hosted environment entails setting up the server with the necessary hardware or virtual machines, installing an operating system, and storing the vector store. In a self-hosted environment, organizations have full control over the vector store, allowing organizations to implement customized financial measures and compliance policies tailored to the organization's specific needs. For example, organizations in industries with strict data privacy and financial regulations, such as finance institutions, can mitigate security risks by storing the vector store in a self-hosted environment.

Operative standards 604 can be specific obligations derived from the guidelines to comply with the guidelines, and can encompass both specific actionable directives and general principles. In some examples, operative standards 604 can serve as actionable directives that organizations must adhere to in order to meet the requirements laid out in regulatory guidelines or industry best practices (e.g., guidelines 602). For example, an operative standard derived from a data protection guideline might mandate the adoption of a specific framework (e.g., General Data Protection Regulation (GDPR)) for handling personal data, outlining procedures for data access, encryption standards, and breach notification protocols. In another example, an operative standard can include prohibiting a certain action to be taken, such as transmitting confidential information to external sources. In further examples, operative standards 604 encompass the fundamental principles or benchmarks derived from guidelines that guide organizational practices and behaviors towards achieving desired outcomes. For example, in the context of ethical standards within a business, operative standards can include principles such as integrity, transparency, and accountability.

Gaps 606 are instances where the current controls or processes fall short of meeting the operative standards. Gaps 606 can be due to the absence of required controls or the inadequacy of existing controls. For example, in the context of data security, a gap may be identified if a company lacks a comprehensive data encryption policy despite regulatory requirements specifying encryption standards for sensitive information. In another example, though an organization may have implemented access controls for sensitive systems, a gap may be identified when the organization fails to regularly review and update user permissions as required by industry best practices, thereby leaving potential vulnerabilities unaddressed.

Gaps 606 can be managed through a systematic approach that incorporates self-reporting and comprehensive storage of attributes tailored to each scenario associated with the gap 606. A scenario of a gap 606 refers to a specific instance or situation where current controls or processes within an organization do not meet established operative standards 604. Each scenario associated with a gap 606 represents a distinct use case. For instance, a scenario can include a cybersecurity breach due to inadequate data encryption practices, or can include a compliance issue related to incomplete documentation of financial transactions. Each identified gap 606 can be documented with scenario attributes (e.g., metadata, tags) such as a descriptive title, severity level assessment (e.g., graded from 1 to 5, where 1 denotes severe and 5 signifies trivial), and/or tags linking the gap 606 to specific business units or regulatory requirements. The scenario attributes provide a clear understanding of the gap's impact and context. In some implementations, the platform 608 includes a user interface that allows users to input and edit the scenario attributes for each gap of gaps 606.

Platform 608 receives the guidelines, operative standards, and/or identified gaps, and generates mapped gaps 610. The mapped gaps correlate the identified gaps with the specific operative standards the identified gaps fail to meet. Methods of mapping the identified gaps with the specific operative standards are discussed with further reference to FIG. 7.

FIG. 7 is a flow diagram illustrating a process 700 of mapping identified gaps in controls to operative standards, in accordance with some implementations of the present technology. In some implementations, the process 700 is performed by components of example devices 200 and computing devices 302 illustrated and described in more detail with reference to FIG. 2 and FIG. 3, respectively. Particular entities, for example, LLM 510, are illustrated and described in more detail with reference to FIG. 5. Likewise, implementations can include different and/or additional steps or can perform the steps in different orders.

In act 702, the system determines a set of vector representations of alphanumeric characters represented by one or more operative standards containing a first set of actions configured to adhere to constraints in the set of vector representations. The set of vector representations of alphanumeric characters is the same as or similar to publications of guidelines 602 discussed with reference to FIG. 6. Methods of transforming different modes (e.g., text, image, audio, video) of guidelines into vector representations are discussed with reference to FIG. 6.

In some implementations, the system receives an indicator of a type of operation associated with the vector representations. The system identifies a relevant set of operative standards associated with the type of the vector representations. The system obtains the relevant set of operative standards, via an Application Programming Interface (API). For example, the system includes input channels or interfaces capable of receiving signals or data tags that denote the type (e.g., nature or purpose) of the vector representations being processed. The system can use an API to retrieve the relevant set of operative standards by implementing API endpoints or integration points that connect the system to a centralized repository or database housing the operative standards that can be tagged with associated metadata related to the type of the vector representation.

In some implementations, the AI model is a first AI model. The system can supply the set of vector representations or the original publications of the guidelines into a second AI model. In response, the system can receive from the second AI model, a set of summaries summarizing the set of vector representations, where at least one prompt in the set of prompts includes one or more summaries in the set of summaries. The set of summaries is a representation of the set of vector representations. The set of summaries, in some implementations, serves as a distilled and coherent representation of the textual content derived from set of vector representations. The set of summaries encapsulates the key themes, sentiments, or pertinent information embedded in the guidelines. The summarization process not only captures the essence of user sentiments but also allows for efficient comprehension and analysis. By condensing voluminous textual content into condensed summaries (e.g., the set of summaries), the system allows users to obtain a comprehensive and accessible understanding of the guidelines. For example, the prompt input into the second AI model can request a summary of the provided text or guidelines by including directives such as “Summarize the following text into key points,” or “Provide a concise summary capturing the main themes and most important information.” Additionally, the prompt can include context or specific aspects to focus on, such as “Provide the major regulatory requirements and the requirements' implications.” The prompt can also include definitions of particular terms, such as operative standards or controls.

In act 704, the system receives, via a user interface, an output generation request including an input for generation of an output using a large-language model (LLM). The input includes a set of gaps associated with one or more scenarios failing to satisfy the one or more operative standards of the set of vector representations. Examples of gaps are discussed with reference to gaps 606 in FIG. 6. Each scenario is associated with a unique identifier and corresponding metrics indicating one or more actions in the first set of actions absent from the scenario. Examples of scenarios are discussed with reference to FIG. 6. Each gap in the set of gaps includes a set of attributes defining the scenario including the unique identifier of the scenario, the corresponding metrics of the scenario, the corresponding vector representations associated with the scenario, a title of the scenario, a summary of the scenario, and/or a severity level of the scenario.

In some implementations, the set of attributes defining the scenario includes a binary indicator of the severity level of the scenario, a category of the severity level of the scenario, and/or a probability associated with the severity level of the scenario. For instance, a binary indicator can be set to ‘1’ for severe (indicating an issue that requires immediate attention) or ‘0’ for non-severe (where the issue is less urgent but still requires resolution). In another example, categories can range from ‘Low’ to ‘High’ severity, helping prioritize remedial actions based on the potential impact and risk associated with each scenario. In a further example, a high probability value can indicate that the compliance gap is highly likely to lead to regulatory fines or data breaches if not addressed promptly.

In act 706, using the received input, the system constructs a set of prompts for each gap in the set of gaps. The set of prompts for a particular gap includes the set of attributes defining the scenario, such as scenario identifiers, severity assessments (e.g., criticality level), summaries outlining the compliance issue, the first set of actions (e.g., actionable directives or general principles of FIG. 6) of the one or more operative standards, and/or the set of vector representations. In some implementations, the set of prompts for each gap in the set of gaps includes a set of pre-loaded query contexts defining one or more sets of alphanumeric characters associated with the set of vector representations. The pre-loaded query contexts include predefined templates, rules, or configurations that specify criteria for mapping gaps to operative standards. For example, the pre-loaded query context can include definitions of terms such as operative standards and/or gaps. The prompts serve as input to a large-language model (LLM), which is designed to process natural language inputs and generate structured outputs based on learned patterns and data.

In act 708, for each gap in the set of gaps, the system maps the gap to one or more operative standards of the set of vector representations. The system supplies the prompt of the particular gap into the LLM. Responsive to inputting the prompt, the system receives, from the LLM, a gap-specific set of operative standards including the one or more operative standards associated with the particular gap. In some implementations, the system can generate, for each gap-specific operative standard of the set of gap-specific operative standards, an explanation for each gap associated with how the one or more operative standards is mapped. The output of the LLM can be in the form of alphanumeric characters. In some implementations, responsive to inputting the prompt, the system receives, from the AI model, the gap-specific set of operative standards, and the corresponding sets of vector representations.

In some implementations, the prompt into the LLM includes a directive to provide a first explanation of why a particular gap should be mapped to a particular operative standard, and also a second explanation of why a particular gap should not be mapped to a particular operative standard. The prompt can further include a directive to provide why the first explanation or the second explanation is weighted more (e.g., why a certain mapping occurs). In some implementations, a human individual can approve or disapprove the mappings based on the first and/or second explanations. Allowing a human-in-the-loop (HITL) and generating a first and second explanation provides transparency to users of the platform regarding the generated mappings.

In act 710, the system generates for display at the user interface, a graphical representation indicating the gap-specific set of operative standards. The graphical representation includes a first representation of each gap in the set of gaps and a second representation of the corresponding gap-specific set of operative standards. In some implementations, each gap is visually represented to highlight its specific attributes, such as severity level, scenario identifier, and a summary detailing the gap. The graphical representations can use charts, diagrams, or visual frameworks that integrate color coding, icons, or annotations to denote severity levels, compliance progress, or overdue actions. Annotations within the graphical representation can offer additional context or explanations regarding each gap and its alignment with operative standards. Overlays can be used to indicate overdue actions, completed mappings, and/or compliance deadlines.

In act 712, using the gap-specific set of operative standards, the system generates a second set of actions for each gap in the set of gaps including one or more actions in the first set of actions indicated by the corresponding gap-specific set of operative standards. The second set of actions can modify a portion of the scenario in the corresponding gap to satisfy the one or more operative standards of the set of vector representations. For instance, actions may involve updating policies, enhancing security measures, implementing new protocols, and/or conducting training sessions to improve organizational practices and mitigate risks. Each action can be linked directly to the corresponding gap and its associated operative standards.

In some implementations, the set of prompts is a first set of prompts, and the gap-specific set of operative standards is a first set of operative standards. Using the received input, the system constructs a second set of prompts for each gap in the set of gaps. The second set of prompts for a particular gap includes the set of attributes defining the scenario and the set of vector representations. Using the second set of prompts, the system receives, from the LLM, a second set of operative standards for each gap in the set of gaps. Using the second set of operative standards, the system constructs a third set of prompts for each gap in the set of gaps. The third set of prompts for the particular gap includes the set of attributes defining the scenario and the first set of actions of the one or more operative standards. Using the third set of prompts, the system receives, from the LLM, a third set of operative standards for each gap in the set of gaps. The iterative approach of using multiple sets of prompts with the LLM enhances the system's capability to adapt and respond dynamically to previously generated mappings and thus contributes to a continuous improvement process where insights gained from each interaction cycle contribute to more refined strategies for achieving alignment of an organization with the operative standards.

In some implementations, the set of prompts is a first set of prompts. For each vector representation in the received set of vector representations, the system identifies a set of textual content representative of the set of vector representations. The system partitions the set of textual content into a plurality of text subsets of the set of textual content based on predetermined criteria. The predetermined criteria can include a length of each text subset and/or a complexity of each text subset. For example, the predetermined criteria can be token count or character limit to ensure uniformity and coherence in the division process. Chunking the textual content breaks down a large amount of textual content into manageable units. For token-based partitioning, the system calculates the number of linguistic units, or tokens, within the textual content. These tokens, in some implementations, encompass individual words, phrases, or even characters, depending on the specific linguistic analysis employed. The predetermined token count criterion sets a quantitative guideline, dictating the number of linguistic units encompassed within each chunk. In some implementations, when employing a character limit criterion, the system focuses on the total number of characters within the textual content character limit criterion, in some implementations, involves assessing both alphanumeric characters and spaces, providing a more fine-grained measure of the content's structural intricacies. The predetermined character limit establishes an upper threshold, guiding the system to create segments that adhere to the predefined character limit.

The system can receive user feedback related to deviations between the gap-specific set of operative standards and a desired set of operative standards. The system can iteratively adjust the sets of prompts to modify the gap-specific set of operative standards to the desired set of operative standards. The system can generate action plans, updating compliance strategies, and/or refine operational practices to enhance alignment with the set of vector representations. The system can generate a set of actions (e.g., a modification plan) that adjust the current attributes of the scenario to a desired set of attributes of the scenario. The system can identify the root cause of the difference between the attributes of the scenario and the desired set of attributes of the scenario. For example, the desired set of attributes of the scenario can include a certain action not found in the current attributes of the scenario (e.g., an anonymization procedure). The actions (e.g., the anonymization procedure) can be preloaded into the system.

Generating Actionable Items From Guidelines Using the Data Generation Platform

FIG. 8 is an illustrative diagram illustrating an example environment 800 of the platform identifying actionable items 810a-n from guidelines 802, in accordance with some implementations of the present technology. Environment 800 includes guidelines 802, platform 804, text subsets 806a-n, prompts 808a-n, and actionable items 810a-n. Guidelines 802 are the same as or similar to guidelines 602 with reference to FIG. 6. Platform 804 is the same as or similar to platform 504 with reference to FIG. 5. Implementations of example environment 800 can include different and/or additional components or can be connected in different ways.

Platform 804 can be a web-based application that hosts various use cases, such as compliance, that allows users to interact via a front-end interface. Inputs to the platform 804 can be guidelines 802 in various formats (e.g., text, Excel). Further examples of platform 804 are discussed with reference to platform 504 in FIG. 5. The backend of platform 804 can chunk (e.g., partition) the guidelines into text subsets 806a-n and vectorize the text subsets 806a-n. The vectorized representations of the text subsets 806a-n can be stored in a database accessible by the platform 804. The platform 804 can use an API call to send prompts to an AI model (such as an LLM), as described further in FIG. 5. The AI model processes the prompts and returns the output of actionable items to the backend of platform 804, which can format the output into a user-friendly structure.

Text subsets 806a-n refer to portions of the guidelines 802 that have been extracted or divided (e.g., based on specific criteria) into smaller segments. Each text subsets 806a-n can be categorized by topic, section, or other relevant factors. By breaking down large volumes of text into subsets, the platform can focus on specific parts of the guidelines. The structured approach additionally allows the platform to handle large volumes of regulatory text efficiently.

Prompts 808a-n are specific queries or instructions generated from the text subsets 806a-n that are formulated to direct the behavior and output of an AI model, such as identifying actionable items from the text subsets 806a-n of regulatory guidelines 802. For example, for text subset 806a, a corresponding prompt 808a is constructed. In some implementations, a prompt can include multiple text subsets. In some implementations, a single text subset can be associated with multiple prompts. Prompts 808a-n causes the AI model to identify particular attributes of the text subsets 806a-n, such as regulatory obligations or compliance requirements to dynamically generate meaningful outputs (e.g., actionable items). In some implementations, the prompts 808a-n can be generated using a second AI model. The second AI model can analyze the text subsets 806a-n or the guidelines 802 directly to identify features of the text subset such as context, entities, and the relationships between the features by, for example, breaking down the input into smaller components and/or tagging predefined keywords. The second AI model can construct prompts that are contextually relevant using the identified features. For instance, if the input pertains to compliance guidelines, the second AI model can identify sections within the guidelines and frame prompts that highlight the most relevant information (e.g., information directed towards compliance guidelines). The prompts can include specific questions or statements that direct the first AI model to focus on particular aspects, such as “What are the key compliance requirements for data protection in this guideline?”

The second AI model can, in some implementations, employ query expansion. Query expansion is a process that enhances the original query by including synonyms, related concepts, and/or additional contextually relevant terms to improve the comprehensiveness of the response. For example, if the initial prompt is “Identify key actionable items for data protection,” the second AI model can expand the query by including keywords such as “privacy regulations,” “data security measures,” and “information governance.” In some implementations, the second AI model can reference domain-specific thesauruses and/or pre-trained word embeddings to find synonyms and related terms to the identified elements.

Prompts 808a-n can include definitions, keywords, and instructions that guide the AI model in identifying relevant actionable items. For instance, definitions can clarify what constitutes an “actionable item” or “obligation.” Further, prompts 808a-n can specify keywords like “must,” “shall,” or “required.” The keywords may indicate mandatory actions or prohibitions that need to be identified as actionable items. For example, a prompt can instruct the AI model to flag any sentence containing the word “must” as it likely denotes a regulatory requirement. In another example, prompts 808a-n can direct the AI model to extract all instances of deadlines for compliance actions, descriptions of required documentation, or procedures for reporting to regulatory bodies. Instructions can also include formatting guidelines, ensuring that the extracted actionable items are presented in a consistent and usable format.

Actionable items 810a-n (e.g., directives, actions) are the specific tasks or requirements identified by the AI model from the guidelines, based on the analysis of text subsets 806a-n and prompts 808a-n. In some implementations, rather than being mere excerpts from the text subsets 806a-n, actionable items 810a-n can be distilled, comprehensive instructions that define specific measures or procedures to implement. For instance, an actionable item might outline the frequency and format of compliance reports required, specify the data to be included, and designate the department responsible for submission. Actionable items 810a-n are designed to translate regulatory text into actionable steps that organizations can directly operationalize. Actionable items 810a-n can include tasks such as reporting, record-keeping, compliance checks, and other regulatory actions.

Each actionable item can include metadata such as the responsible party within the organization, the type of customer or stakeholder affected, and/or other relevant identifiers. An AI model can use natural language processing (NLP) algorithms to parse through text subsets 806a-n to identify relevant phrases, keywords, and semantic structures (e.g., as instructed by the prompts 808a-n) that indicate actionable items 810a-n within the guidelines 802. Prompts 808a-n can direct the AI model by providing contextual cues and specific queries that direct the AI model to focus on particular guidelines or aspects of guidelines within guidelines 802.

Example Implementations of a Validation Engine of the Data Generation Platform

FIG. 9 is a block diagram illustrating an example environment 900 for using the guidelines input into the validation engine for determining AI compliance, in accordance with some implementations of the present technology. Environment 900 includes guidelines 902 (e.g., jurisdictional regulations 904, organization regulation 906, AI application-specific regulations 908), vector store 910, and validation engine 912. Validation engine can be the same as or similar to generative model engine 120 in data generation platform 102 discussed with reference to FIG. 1. Vector store 910 and validation engine 912 are implemented using components of example devices 200 and computing devices 302 illustrated and described in more detail with reference to FIG. 2 and FIG. 3, respectively. Likewise, implementations of example environment 900 can include different and/or additional components or can be connected in different ways.

Guidelines 902 can include various elements such as jurisdictional regulations 904, organizational regulations 906, and AI applications-specific regulations 908 (e.g., unsupervised learning, natural language processing (NLP), generative AI). Jurisdictional regulations 904 (e.g., governmental regulations) can include regulations gathered from authoritative sources such as government websites, legislative bodies, and regulatory agencies. Jurisdictional regulations 904 can be published in legal documents or official publications and cover aspects related to the development, deployment, and use of AI technologies within specific jurisdictions. Organizational regulations 906 includes internal policies, procedures, and guidelines established by organizations to govern AI-related activities within the organization's operations. Organizational regulations 906 can be developed in alignment with industry standards, legal requirements, and organizational objectives. AI application-specific regulations 908 include regulations that pertain to specific types of AI applications, such as unsupervised learning, natural language processing (NLP), and generative AI. Each type of AI application presents unique challenges and considerations in terms of compliance, ethical use, and/or regulatory adherence. For example, unsupervised learning algorithms, where the model learns from input data without labeled responses, may be subject to regulations that prevent bias and discrimination in unsupervised learning models. Natural language processing (NLP) technologies, which enable computers to understand, interpret, and generate human language, may be subject to specific regulations aimed at safeguarding user privacy. Generative AI, which autonomously creates new content, may focus on intellectual property rights, content moderation, and ethical use cases. AI developers may need to incorporate additional mechanisms for copyright protection, content filtering, and/or user consent management to comply with regulations related to generative AI technologies.

The guidelines 902 are stored in a vector store 910. The vector store 910 stores the guidelines 902 in a structured and accessible format (e.g., using distributed databases or NoSQL stores), which allows for efficient retrieval and utilization by the validation engine 912. In some implementations, the guidelines 902 are preprocessed to remove any irrelevant information, standardize the format, and/or organize the guidelines 902 into a structured database schema. Once the guidelines 902 are prepared, the guidelines 902 can be stored in a vector store 910 using distributed databases or NoSQL stores.

To store the guidelines 902 in the vector store 910, the guidelines 902 can be encoded into vector representations for subsequent retrieval by the validation engine 912. The textual data of the guidelines 902 are transformed into numerical vectors that capture the semantic meaning and relationships between words or phrases in the guidelines 902. For example, the text is encoded into vectors using word embeddings and/or TF-IDF encoding. Word embeddings, such as Word2Vec or GloVe, learn vector representations of words based on the word's contextual usage in a large corpus of text data. Each word is represented by a vector in a high-dimensional space, where similar words have similar vector representations. TF-IDF (Term Frequency-Inverse Document Frequency) encoding calculates the importance of a word in a guideline relative to the word's frequency in the entire corpus of guidelines 902. For example, the system can assign higher weights to words that are more unique to a specific document and less common across the entire corpus.

In some implementations, the guidelines 902 are stored using graph databases such as Neo4j™ or Amazon Neptune™. Graph databases represent data as nodes and edges, allowing for the modeling of relationships between guidelines 902 to demonstrate the interdependencies. In some implementations, the guidelines 902 are stored in a distributed file system such as Apache Hadoop™ or Google Cloud Storage™. These systems offer scalable storage for large volumes of data and support parallel processing and distributed computing. Guidelines 902 stored in a distributed file system can be accessed and processed by multiple nodes simultaneously, which allows for faster retrieval and analysis by the validation engine.

The vector store 910 can be stored in a cloud environment hosted by a cloud provider, or a self-hosted environment. In a cloud environment, the vector store 910 has the scalability of cloud services provided by platforms (e.g., AWS™, Azure™). Storing the vector store 910 in a cloud environment entails selecting the cloud service, provisioning resources dynamically through the provider's interface or APIs, and configuring networking components for secure communication. Cloud environments allow the vector store 910 to scale storage capacity without the need for manual intervention. As the demand for storage space grows, additional resources can be automatically provisioned to meet the increased workload. Additionally, cloud-based caching modules can be accessed from anywhere with an internet connection, providing convenient access to historical data for users across different locations or devices.

Conversely, in a self-hosted environment, the vector store 910 is stored on a private web server. Deploying the vector store 910 in a self-hosted environment entails setting up the server with the necessary hardware or virtual machines, installing an operating system, and storing the vector store 910. In a self-hosted environment, organizations have full control over the vector store 910, allowing organizations to implement customized security measures and compliance policies tailored to the organization's specific needs. For example, organizations in industries with strict data privacy and security regulations, such as finance institutions, can mitigate security risks by storing the vector store 910 in a self-hosted environment.

The validation engine 912 accesses the guidelines 902 from the vector store 910 to initiate the compliance assessment. The validation engine 912 can establish a connection to the vector store 910 using appropriate APIs or database drivers. The connection allows the validation engine 912 to query the vector store 910 and retrieve the relevant guidelines for the AI application under evaluation. Frequently accessed guidelines 902 are stored in memory, which allows the validation engine 912 to reduce latency and improve response times for compliance assessment tasks. In some implementations, only the relevant guidelines are retrieved based on the specific AI application under evaluation. For example, metadata tags, categories, or keywords associated with the AI application can be used to filter the guidelines 902.

The validation engine 912 evaluates the AI application's compliance with the retrieved guidelines 902, (e.g., using semantic search, pattern recognition, and machine learning techniques). For example, the validation engine 912 compares the vector representations of the different explanations and outcomes by calculating the cosine of the angle between the two vectors indicating the vectors' directional similarity. Similarly, for comparing explanations, the validation engine 912 can measure the intersection over the union of the sets of words in the expected and case-specific explanations.

FIG. 10 is a block diagram illustrating an example environment 1000 for generating validation actions to determine AI model compliance, in accordance with some implementations of the present technology. Environment 1000 includes training data 1002, meta-model 1010, validation actions 1012, cache 1014, and vector store 1016. Meta-model 1010 is the same as or similar to meta-model 402 illustrated and described in more detail with reference to FIG. 4. Meta-model 1010 is implemented using components of example devices 200 and computing devices 302 illustrated and described in more detail with reference to FIG. 2 and FIG. 3, respectively. Likewise, implementations of example environment 1000 can include different and/or additional components or can be connected in different ways.

The training data includes data from sources such as business applications 1004, other AI applications 1006, and/or an internal document search AI 1008. Business applications 1004 refer to software tools or systems used to facilitate various aspects of business operations and can include data related to, for example, loan transaction history, customer financial profiles, credit scores, and income verification documents. For example, data from a banking application can provide insights into an applicant's banking behavior, such as average account balance, transaction frequency, and bill payment history. Other AI applications 1006 can include, for example, credit scoring models, fraud detection algorithms, and risk assessment systems that can be used by lenders to evaluate loan applications. Data from AI applications 1006 refer to various software systems that utilize artificial intelligence (AI) techniques to perform specific tasks or functions. The data can include credit risk scores and fraud risk indicators. For example, an AI-powered credit scoring model can provide a risk assessment score based on an applicant's credit history, debt-to-income ratio, and other financial factors. The internal document search AI 1008 is an AI system tailored for searching and retrieving information from internal documents within an organization. For example, the internal document search AI 1008 can be used to retrieve and analyze relevant documents such as loan agreements, regulatory compliance documents, and internal policies. Data from internal documents can include, for example, legal disclosures, loan terms and conditions, and compliance guidelines. For example, the AI system can flag loan applications that contain discrepancies or inconsistencies with regulatory guidelines or internal policies.

The training data 1002 is fed into the meta-model 1010 to train the meta-model 1010, enabling the meta-model 1010 to learn patterns and characteristics associated with compliant and non-compliant AI behavior. Further discussion of Artificial Intelligence and training methods are discussed in FIG. 7. The meta-model 1010 leverages the learned patterns and characteristics to generate validation actions 1012, which serve as potential use-cases designed to evaluate AI model compliance. The validation actions 1012 can encompass various scenarios and use cases relevant to the specific application domain of the AI model under assessment. Further methods of creating validation actions are discussed in FIGS. 12-14.

In some implementations, the generated validation actions 1012 can be stored in a cache 1014 and/or a vector store 1016. The cache 1014 is a temporary storage mechanism for storing recently accessed or frequently used validation actions, and facilitates efficient retrieval when needed. On the other hand, the vector store 1016 provides a structured repository for storing vector representations of validation actions, enabling efficient storage and retrieval based on similarity or other criteria. The vector store 1016 stores the generated validation actions 1012 in a structured and accessible format (e.g., using distributed databases or NoSQL stores), which allows for efficient retrieval and utilization by the meta-model 1010. The generated validation actions 1012 can be preprocessed to remove any irrelevant information, standardize the format, and/or organize the generated validation actions 1012 into a structured database schema. Once the generated validation actions 1012 are prepared, the generated validation actions 1012 can be stored in a vector store 1016 using distributed databases or NoSQL stores.

In some implementations, the generated validation actions 1012 are stored using graph databases such as Neo4j™ or Amazon Neptune™. Graph databases represent data as nodes and edges, allowing for the modeling of relationships between generated validation actions 1012 to demonstrate the interdependencies. In some implementations, the generated validation actions 1012 are stored in a distributed file system such as Apache Hadoop™ or Google Cloud Storage™. The systems offer scalable storage for large volumes of data and support parallel processing and distributed computing. Generated validation actions 1012 stored in a distributed file system can be accessed and processed by multiple nodes simultaneously, which allows for faster retrieval and analysis by the meta-model 1010.

The vector store 1016 can be stored in a cloud environment hosted by a cloud provider, or a self-hosted environment. In a cloud environment, the vector store 1016 has the scalability of cloud services provided by platforms (e.g., AWS™, Azure™). Storing the vector store 1016 in a cloud environment entails selecting the cloud service, provisioning resources dynamically through the provider's interface or APIs, and configuring networking components for secure communication. Cloud environments allow the vector store 1016 to scale storage capacity without the need for manual intervention. As the demand for storage space grows, additional resources can be automatically provisioned to meet the increased workload. Additionally, cloud-based caching modules can be accessed from anywhere with an internet connection, providing convenient access to historical data for users across different locations or devices.

Conversely, in a self-hosted environment, the vector store 1016 is stored on a private web server. Deploying the vector store 1016 in a self-hosted environment entails setting up the server with the necessary hardware or virtual machines, installing an operating system, and storing the vector store 1016. In a self-hosted environment, organizations have full control over the vector store 1016, allowing organizations to implement customized security measures and compliance policies tailored to the organization's specific needs. For example, organizations in industries with strict data privacy and security regulations, such as finance institutions, can mitigate security risks by storing the vector store 1016 in a self-hosted environment.

The meta-model 1010 accesses the generated validation actions 1012 from the vector store 1016 to initiate the compliance assessment. The system can establish a connection to the vector store 1016 using appropriate APIs or database drivers. The connection allows the meta-model 1010 to query the vector store 1016 and retrieve the relevant vector constraints for the AI application under evaluation. Frequently accessed validation actions 1012 are stored in memory, which allows the system to reduce latency and improve response times for compliance assessment tasks.

In some implementations, only the relevant validation actions are retrieved based on the specific AI application under evaluation. For example, metadata tags, categories, or keywords associated with the AI application can be used to filter the validation actions 1012. The relevant validation actions can be specifically selected based on the specific context and requirements of the AI application being evaluated. For example, the system analyzes metadata tags, keywords, or categories associated with the validation actions 1012 stored in the system's database. Using the specific context and requirements of the AI application, the system filters and retrieves the relevant validation actions from the database.

Various filters can be used to select relevant validation actions. In some implementations, the system uses natural language processing (NLP) to parse through the text of the validation action 1012 and identify key terms, phrases, and clauses that denote regulatory obligations relevant to the AI application's domain. The specific terms related to the AI application's domain can be predefined and include, for example, “patient privacy” for healthcare sector applications. Using the specific terms related to the AI application's domain as a filter, the system can filter out the non-relevant validation actions. To identify the relevant validation actions from the validation actions 1012, the system can determine the specific terms to use as filters by calculating the similarity between vectors representing domain-specific terms (e.g., “healthcare”) and vectors representing other terms related to the domain (e.g., “patient privacy”), domain-specific terms can be identified based on the proximity of the other terms to known terms of interest. A similarity threshold can be applied to filter out terms that are not sufficiently similar to known domain-specific terms.

In some implementations, the system can tag relevant validation actions with attributes that help contextualize the relevant validation actions. The tags serve as markers that categorize and organize the validation actions 1012 based on predefined criteria, such as regulatory topics (e.g., data privacy, fairness, transparency) or jurisdictional relevance (e.g., regional regulations, industry standards). The tags provide a structured representation of the validation actions 1012 and allow for easier retrieval, manipulation, and analysis of regulatory content. The tags and associated metadata can be stored in a structured format, such as a database, where each validation action 1012 is linked to the validation action's 1012 corresponding tags and/or regulatory provisions.

The meta-model 1010 evaluates the AI application's compliance with the vector constraints through the use of validation actions 1012 (e.g., using semantic search, pattern recognition, and machine learning techniques). Further evaluation methods in determining compliance of AI applications are discussed with reference to FIGS. 12-14.

FIG. 11 is a block diagram illustrating an example environment 1100 for automatically implementing corrective actions on the AI model, in accordance with some implementations of the present technology. Environment 1100 includes training dataset 1102, meta-model 1104 (which includes validation models 1106A-D, validation actions 1108, AI application 1110), outcome and explanation 1112, recommendation 1114, and corrective actions 1116. Meta-model 1104 is the same as or similar to meta-model 1010 illustrated and described in more detail with reference to FIG. 10. Meta-model 1104 and AI application 1110 are implemented using components of example devices 200 and computing devices 302 illustrated and described in more detail with reference to FIG. 2 and FIG. 3, respectively. Likewise, implementations of example environment 1100 can include different and/or additional components or can be connected in different ways.

A training dataset 1102, which includes a collection of data used to train machine learning models, is input into the meta-model 1104. The meta-model 1104 is a comprehensive model that encompasses multiple sub-models tailored to address specific aspects of AI compliance. Within the meta-model 1104, various specialized models are included, such as a bias model 1106A (described in further detail with reference to FIG. 5), a toxicity model 1106B (described in further detail with reference to FIG. 6), an IP violation model 1106C (described in further detail with reference to FIG. 7), and other validation models 1106D. Each of the models is responsible for detecting and assessing specific types of non-compliant content within AI models. Upon processing the training dataset 1102, each model generates validation actions tailored to evaluate the presence or absence of specific types of non-compliant content. Further evaluation techniques in generating validation actions using the meta-model 1104 are discussed with reference to FIGS. 12-14.

The set of generated validation actions 1108 is provided as input to an AI application 1110 in the form of a prompt. The AI application 1110 processes the validation actions 1108 and produces an outcome along with an explanation 1112 detailing how the outcome was determined. Subsequently, based on the outcome and explanation 1112 provided by the AI application 1110, the system can generate recommendations 1114 for corrective actions. The recommendations are derived from the analysis of the validation action outcomes and aim to address any identified issues or deficiencies. For example, if certain validation actions fail to meet the desired criteria due to specific attribute values or patterns, the recommendations can suggest adjustments to those attributes or modifications to the underlying processes.

For a bias detection model, such as the ML model discussed in FIG. 5, if certain attributes exhibit unexpected associations or distributions, the system can retrain the tested AI model with revised weighting schemes to better align with the desired vector constraints. In a toxicity model, such as the ML model discussed in FIG. 6, the corrective actions can include implementing post-processing techniques in the tested AI model to filter out responses that violate the vector constraints (e.g., filtering out responses that include the identified vector representations of the alphanumeric characters). Similarly, in an IP rights violation model, such as the ML model discussed in FIG. 7, the corrective actions can include implementing post-processing techniques in the tested AI model to filter out responses that violate the IP rights (e.g., filtering out responses including the predetermined alphanumeric characters).

In some implementations, based on the outcomes and explanations, the system applies predefined rules or logic to determine appropriate corrective actions. The rules can be established by users and can consider factors such as regulatory compliance, risk assessment, and business objectives. For example, if an application is rejected due to insufficient income, the system can recommend requesting additional financial documentation from the applicant.

In some implementations, the system can use machine learning models to generate recommendations. The models learn from historical data and past decisions to identify patterns and trends that indicate a set of actions the AI model can take to comply with the vector constraints. By training on a dataset of past corrective actions and the outcomes, the machine learning models can predict the most effective recommendations for new cases. Further discussion of Artificial Intelligence and training methods are discussed in FIG. 7. The recommendations 1114 can be automatically implemented as corrective actions 1116 by the system. The automated approach streamlines the process of addressing identified issues and ensures swift remediation of non-compliant content within AI models, enhancing overall compliance and reliability.

FIG. 11 is a block diagram illustrating an example environment 1100 for automatically implementing corrective actions on the AI model, in accordance with some implementations of the present technology. Environment 1100 includes training dataset 1102, meta-model 1104 (which includes validation models 1106A-D, validation actions 1108, AI application 1110), outcome and explanation 1112, recommendation 1114, and corrective actions 1116. Meta-model 1104 is the same as or similar to meta-model 1010 illustrated and described in more detail with reference to FIG. 10. Meta-model 1104 and AI application 1110 are implemented using components of example devices 200 and computing devices 302 illustrated and described in more detail with reference to FIG. 2 and FIG. 3, respectively. Likewise, implementations of example environment 1100 can include different and/or additional components or can be connected in different ways.

A training dataset 1102, which includes a collection of data used to train machine learning models, is input into the meta-model 1104. The meta-model 1104 is a comprehensive model that encompasses multiple sub-models tailored to address specific aspects of AI compliance. Within the meta-model 1104, various specialized models are included, such as a bias model 1106A (described in further detail with reference to FIG. 5), a toxicity model 1106B (described in further detail with reference to FIG. 6), an IP violation model 1106C (described in further detail with reference to FIG. 7), and other validation models 1106D. Each of the models is responsible for detecting and assessing specific types of non-compliant content within AI models. Upon processing the training dataset 1102, each model generates validation actions tailored to evaluate the presence or absence of specific types of non-compliant content. Further evaluation techniques in generating validation actions using the meta-model 1104 are discussed with reference to FIGS. 12-14.

The set of generated validation actions 1108 is provided as input to an AI application 1110 in the form of a prompt. The AI application 1110 processes the validation actions 1108 and produces an outcome along with an explanation 1112 detailing how the outcome was determined. Subsequently, based on the outcome and explanation 1112 provided by the AI application 1110, the system can generate recommendations 1114 for corrective actions. The recommendations are derived from the analysis of the validation action outcomes and aim to address any identified issues or deficiencies. For example, if certain validation actions fail to meet the desired criteria due to specific attribute values or patterns, the recommendations can suggest adjustments to those attributes or modifications to the underlying processes.

For a bias detection model, such as the ML model discussed in FIG. 5, if certain attributes exhibit unexpected associations or distributions, the system can retrain the tested AI model with revised weighting schemes to better align with the desired vector constraints. In a toxicity model, such as the ML model discussed in FIG. 6, the corrective actions can include implementing post-processing techniques in the tested AI model to filter out responses that violate the vector constraints (e.g., filtering out responses that include the identified vector representations of the alphanumeric characters). Similarly, in an IP rights violation model, such as the ML model discussed in FIG. 7, the corrective actions can include implementing post-processing techniques in the tested AI model to filter out responses that violate the IP rights (e.g., filtering out responses including the predetermined alphanumeric characters).

In some implementations, based on the outcomes and explanations, the system applies predefined rules or logic to determine appropriate corrective actions. The rules can be established by users and can consider factors such as regulatory compliance, risk assessment, and business objectives. For example, if an application is rejected due to insufficient income, the system can recommend requesting additional financial documentation from the applicant.

In some implementations, the system can use machine learning models to generate recommendations. The models learn from historical data and past decisions to identify patterns and trends that indicate a set of actions the AI model can take to comply with the vector constraints. By training on a dataset of past corrective actions and the outcomes, the machine learning models can predict the most effective recommendations for new cases. Further discussion of Artificial Intelligence and training methods are discussed in FIG. 7. The recommendations 1114 can be automatically implemented as corrective actions 1116 by the system. The automated approach streamlines the process of addressing identified issues and ensures swift remediation of non-compliant content within AI models, enhancing overall compliance and reliability.

Identifying and Remediating Gaps in AI Use Cases Using the Data Generation Platform

FIG. 12 is a block diagram illustrating an example environment 1200 for identifying and remediating gaps in compliance for AI use cases using a generative AI model, in accordance with some implementations of the present technology. Environment 1200 includes computing device 1202, operational data 1204, inventory module 1206, model use case 1208, guidelines 1210, storage module 1212, compliance engine 1214, models 1216a, 1216b, 1216c, risk category 1218, compliance documentation 1220, and feedback loop 1222. Compliance engine 1214 is the same as or similar to generative model engine 120 illustrated and described in more detail with reference to FIG. 1. Likewise, implementations of example environment 1200 can include different and/or additional components or can be connected in different ways.

Computing device 1202 refers to any electronic device capable of processing data and executing instructions, such as a server, desktop computer, or mobile device. Computing device 1202 and compliance engine 1214 are implemented using components of example devices 200 and computing devices 302 illustrated and described in more detail with reference to FIG. 2 and FIG. 3, respectively. A computing device 1202 can process operational data 1204, which includes real-time and historical data related to the AI use cases, such as performance metrics, user interactions, and system logs. Operational data 1204 encompasses data generated and used by the AI systems within the organization. The data is collected from various sources and is used to monitor the performance and behavior of AI models. For instance, operational data 1204 can include user interaction logs from a chatbot AI, performance metrics from a predictive maintenance AI, and/or system logs from an image recognition AI.

In some implementations, the operational data 1204 is collected in real-time from various endpoints and sensors. For example, user interaction logs from a chatbot AI can be captured through API calls and stored in a centralized database (e.g., inventory module 1206). Similarly, system logs from an image recognition AI can be collected from server logs that record every instance of image processing and recognition events. In some implementations, the operational data 1204 is appended with additional contextual information before being sent to the inventory module 1206. For example, the operational data 1204 can be appended with metadata such as timestamps, user IDs, and location data to provide a context of the breadth of impact of the AI application. For example, user interaction logs from a chatbot AI can be tagged with user demographics or geographical coordinates of the deployment area, or system logs from an image recognition AI could be annotated with the types of images being processed.

The operational data 1204 is sent from computing device 1202 to inventory module 1206. Inventory module 1206 maintains a list of AI use cases (e.g., model use case 1208) within the organization. The inventory module 1206 categorizes and stores information about each model use case 1208, including its purpose, functionality, and associated operational data 1204. For example, the inventory module can list an AI use case for fraud detection in financial transactions, detailing the algorithms used, the data inputs (e.g., transaction records), and the expected outputs (e.g., fraud alerts). In some implementations, the operational data 1204 can include documents associated with a type, category, or degree of risk of particular organizational systems (e.g., model use case 1208). The documents can provide, for example, descriptions of the AI models, their intended use, performance metrics, validation results, and/or risk assessments. In some implementations, the documents can further include historical performance data, such as past incident reports of a particular organizational system. Additionally or alternatively, the operational data 1204 can include documents associated with a type, category, or degree of risk of new or non-implemented organizational systems, activities, and/or initiatives. The documents can be, for example, previously performed evaluations of the risks introduced by new systems within the organization (or even consideration of systems used by others, hypothetical systems, or systems not implemented by the organization). By incorporating these documents, the compliance engine 1214 can assess the potential impact of new activities on the overall risk profile and/or the risk profile of other existing systems.

The operational data 1204 can further include documents associated with each (or nearly all) computer systems in use by the organization, and can include internal documents and/or third-party documents. The documents can include information related to each computer system of the organization, such as the system's purpose, data inputs and outputs, software and hardware components, and/or any known associated risks (e.g., metadata tags that tag portions of operational data 1204 at a pre-loaded perceived risk level according to past organizational guidelines). Likewise, the operational data 1204 can include documents associated with computer systems of third parties, such as documents of the third party's software and/or hardware, which can be internal and/or external to the third party. The operational data 1204 of third party systems can include systems that are used to support the activities of the organization. Alternatively, the operational data 1204 of third party systems can include systems that are not used by the organization. In some implementations, the third-party system documents can further include third-party audit reports and/or compliance certifications. Including operational data 1204 associated with third-party systems enables the organization to ensure that the third-party systems meet the same regulatory and compliance standards as internal systems, mitigating the risk of regulatory breaches due to third-party components.

In some implementations, operational data 1204 can encompass both structured and unstructured data of the organization. Structured data can include organized information such as performance metrics, user interaction logs, and system logs stored in a structured (e.g., tabular) format within databases. Unstructured data, on the other hand, includes text, images, audio, and video files that do not follow a predefined schema. For instance, user feedback in the form of text reviews, images processed by an image recognition AI, and audio recordings from voice assistants, and so forth. Methods of extracting model use case 1208 from operational data 1204 is discussed in further detail with reference to FIG. 14.

The inventory module 1206 organizes the operational data 1204 into one or more model use cases 1208, and sends the model use cases 1208 to the compliance engine 1214. Model use case 1208 refers to the specific application or scenario in which an AI model is used. Each model use case 1208 includes at least a portion of the operational data 1204 containing information about the AI system's objectives, data inputs and outputs, and the algorithms employed. For example, a model use case 1208 for a recommendation system can include the objective of increasing user engagement, data inputs such as user behavior data, and outputs like personalized content recommendations. The information can be used the regulatory requirements that apply to each use case and for assessing compliance using methods discussed with reference to FIG. 14. In some implementations, model use case 1208 can include metadata describing the model's training data, validation methods, and performance metrics. For instance, the metadata can include details about the sources of training data, the preprocessing steps applied, the algorithms and hyperparameters used, and/or the methods employed for validating the model's accuracy and robustness. Performance metrics such as precision, recall, and/or F1 score can be included in model use case 1208 to provide a quantitative assessment of the model's effectiveness and/or be used to evaluate the model's compliance.

The inventory module 1206 references the guidelines 1210 to generate an inventory of model use cases containing model use case 1208 using methods discussed with reference to FIG. 14. In some implementations, the inventory of model use cases includes model use cases 1208 defined by corresponding operational data 1204 that the organization identifies as related to an AI system/application. Using organization-specific definitions to filter operational data 1204 enables the compliance engine 1214 to target, for example, computer systems already recognized as AI by the organization. Additionally or alternatively, the inventory of model use cases can include an overall inventory of computer systems in use by the business, which can further encompass both internal systems and third-party systems operating internally and/or externally. The overall inventory ensures that all operational data 1204 (e.g., of organizational systems), even those not initially identified as AI, can be evaluated against regulatory definitions since some operational data 1204 may meet the expanded legal or regulatory definitions of AI, even if the operational data 1204 was not originally classified as such by the business. In some implementations, both sets (full inventory and inventory filtered by organizational definitions) are constructed, and a user can compare the compliance documentation 1220 from the compliance engine 1214 of both sets of inventories.

Guidelines 1210 (e.g., guidelines 602, guidelines 902) can include the regulatory standards and/or best practices that the organization must adhere to when deploying AI systems. The guidelines 1210 can be sourced from various regulatory bodies and industry standards. For instance, guidelines 1210 can include the EU AI Act's/California SB-1047's requirements for transparency and accountability, and/or the GDPR's data protection principles. In another example, guidelines 1210 can include software development lifecycle control (SDLC) documents. SDLC documents can cover one or more requirements/guidelines of the software development lifecycle (e.g., requirements gathering, coding, testing, debugging, deployment, monitoring). Including SDLC documents within the guidelines 1210 ensures that software, including AI systems, are developed and maintained in compliance with best practices and regulatory standards within the SDLC documents. In some implementations, the SDLC documents can include automated testing scripts and/or continuous integration/continuous deployment (CI/CD) pipelines.

In some implementations, guidelines 1210 can be stored in storage module 1212. Storage module 1212 is a repository for storing all relevant data, guidelines, and compliance documentation. Storage module 1212 ensures that information related to the guidelines are stored and easily accessible for compliance assessments and audits. For example, the storage module 1212 can store compliance reports, audit logs, and/or regulatory guidelines. In some implementations, storage module 1212 can use cloud storage solutions to provide scalable and secure data storage options.

The compliance engine 1214 uses criteria within the guidelines 1210 to evaluate the compliance of the model use case 1208 and to identify any gaps that need to be addressed using methods discussed with reference to FIG. 14. Compliance engine 1214 manages the evaluation of the model use case 1208 with criteria from regulatory guidelines 1210. The compliance engine 1214, in some implementations, uses one or more generative AI models (e.g., models 1216a, 1216b, 1216c) to analyze operational data, model use cases, and guidelines to identify gaps in compliance. For example, the compliance engine 1214 can analyze the operational data of a facial recognition AI to ensure the model use case meets privacy regulations, identifying any non-compliant data handling practices and generating actionable recommendations to address the gaps. In some implementations, compliance engine 1214 can use machine learning algorithms to continuously improve its compliance assessment capabilities based on feedback and new regulatory updates using feedback loop 1222.

Within the compliance engine 1214, models 1216a, 1216b, and 1216c are used to determine the risk category 1218. Models 1216a, 1216b, and 1216c can be the same AI model applied to different aspects of compliance evaluation or different AI models specialized for various compliance tasks. For instance, model 1216a can focus on data quality and bias detection, model 1216b on algorithmic transparency and explainability, and model 1216c on performance metrics and robustness. Models 1216a, 1216b, and 1216c can be implemented using various machine learning and deep learning architectures. For example, model 1216a can use a combination of statistical analysis and machine learning classifiers to detect biases in the data. model 1216b can use natural language processing (NLP) techniques to analyze textual explanations and ensure transparency, and model 1216c can use neural networks or ensemble methods to evaluate the performance and robustness of the AI model. Each model use case 1208 is evaluated for compliance based on its specific use case and the applicable regulatory guideline(s). For instance, model use case 1208 can be an NLP model used for sentiment analysis, a machine learning model for predictive analytics, or a deep learning model for image classification. The model use case 1208 can include contextual details about, for example, the users, the location, and so forth. In some implementations, models 1216a, 1216b, and 1216c can be deployed on different hardware platforms, such as GPUs or TPUs, to improve performance for specific tasks.

The models 1216a, 1216b, and 1216c can be used to determine the risk category 1218. Risk category 1218 of the model use case 1208 is based on different risk levels of guidelines. The risk categories can be predefined levels, such as low, medium, and high risk, or more granular categories based on specific regulatory guidelines. Each risk category can be associated with compliance requirements and mitigation strategies (e.g., the EU AI Act, California SB-1047). For example, the EU AI Act defines “AI systems” in categories such as “Prohibited,” “High-Level,” “Limited,” and “Minimal” risk. The compliance engine 1214 uses the risk categories to categorize the model use case 1208 under the particular regulatory category. For instance, a model use case 1208 associated with a higher degree of risk, such as autonomous driving, can be classified as “High-Level” risk, which can be associated with more stringent compliance checks and monitoring under a particular regulation. Similarly, California SB-1047 focuses on mitigating “critical harms.” Critical harms are defined as severe risks that could result in mass casualties, significant property damage, or other grave threats to public safety and security. The risk category 1218 can be, in this example, “critical harm” or “non-critical harm.” In some implementations, the compliance engine 1214 can use a scoring system to quantify the risk category 1218 of the model use case 1208 discussed with reference to FIG. 14.

Compliance documentation 1220 can be derived from the criteria of the risk category 1218. Compliance documentation 1220 includes reports, records, and/or evidence used to demonstrate compliance with regulatory guidelines. The compliance documentation 1220 can be generated by the compliance engine 1214 and/or stored in the storage module 1212. For example, compliance documentation 1220 can include audit reports, compliance checklists, and evidence of corrective actions taken to address identified gaps. In some implementations, compliance documentation 1220 can be automatically generated and updated based on real-time data from the compliance engine using methods discussed with reference to FIG. 14.

Feedback loop 1222 is a mechanism for continuously monitoring and improving compliance. The feedback loop 1222 collects feedback from various sources, such as user interactions, system performance, and regulatory updates, and uses the information to refine the compliance process. For example, the feedback loop 1222 can incorporate user updates to a particular model use case, changes in system performance metrics, and updates to regulatory guidelines. In some implementations, feedback loop 1222 can be used to tune models 1216a, 1216b, and 1216c using methods discussed with reference to FIG. 14. For example, hyperparameters such as the learning rate, regularization parameters, and/or the number of layers in a neural network can be adjusted to enhance model performance. Additionally, algorithm selection can be performed by evaluating and choosing different algorithms based on the feedback data. For example, the compliance engine 1214 can switch from using a decision tree to using a random forest if the latter demonstrates improved performance. Further, the compliance engine 1214 can add new input features derived from user feedback or removing features that do not contribute to the model's performance.

FIG. 13 is a block diagram illustrating an example environment 1300 for continuously monitoring compliance in AI use cases using a generative AI model, in accordance with some implementations of the present technology. Environment 1300 includes model use case 1302, compliance engine 1304, models 1306, risk categories 1308, criteria 1310, gaps 1312, compliance actions 1314, and monitoring loop 1316. Model use case 1302 is the same as or similar to model use case 1208 illustrated and described in more detail with reference to FIG. 12. Models 1306 are the same as or similar to models 1206a, 1206b, 1206c illustrated and described in more detail with reference to FIG. 12. Models 1306 are implemented using components of example devices 200 and computing devices 302 illustrated and described in more detail with reference to FIG. 2 and FIG. 3, respectively. Risk categories 1308 are the same as or similar to risk category 1218 illustrated and described in more detail with reference to FIG. 12. Likewise, implementations of example environment 1300 can include different and/or additional components or can be connected in different ways.

Model use case 1302 is a specific application or scenario where an AI model is utilized or can be utilized (e.g., model use case 1208), and defines the boundaries and expectations for the AI model's operation, including the inputs, outputs, and the context in which the model is (or to be/can be) applied. Models 1306 within compliance engine 1304 represent the various AI models employed within the environment 1300. Models 1306 process operational data (e.g., operational data 1204) associated with the model use case 1302, and generating indicators of compliance based on the defined model use cases 1302. The models 1306 can include various types of machine learning algorithms, such as supervised learning models, unsupervised learning models, reinforcement learning models, and deep learning models. Each model 1306 can be trained on specific datasets to perform tasks such as classification, regression, clustering, and anomaly detection. The models 1306 can be continuously updated and retrained with new data to improve their accuracy and performance.

The models 1306 can be domain-specific or generic. Domain-specific models 1306 are tailored to address particular regulations a specific area of a regulation. For example, a domain-specific model for cybersecurity compliance can be trained to ensure adherence to regulations such as the EU AI Act with a focus on cybersecurity requirements. Domain-specific models 1306 can be trained on datasets that include regulatory texts, compliance checklists, and historical compliance data relevant to the specific regulation or specific area of the regulation. For example, a domain-specific model for GDPR cybersecurity compliance would focus on data encryption, access controls, and breach notification requirements. The domain-specific model 1306 can output risk categories and identify gaps in compliance specific to the areas using methods discussed with reference to FIG. 14. Generic models 1306, on the other hand, can output broader risk categories and identify general gaps in compliance that apply across various regulatory frameworks using methods discussed with reference to FIG. 14.

Risk categories 1308 classify potential risks associated with the model use case 1302. The criteria 1310 of each risk category (e.g., “unacceptable risk” 1308a, “high risk” 1308b, “limited risk” 1308c, and “minimal risk” 1308d) in risk categories 1308 provide the standards that the model use case 1302 must meet, and any gaps 1312 between the criteria 1310 and the models' performance can be identified by the system (e.g., compliance engine 1304, compliance engine 1214 in FIG. 12). Risk categories can include risks of various dimensions of the regulation such as data privacy risks, model bias risks, operational risks, depending on the guidelines (e.g., guidelines 1210 in FIG. 12) used. Each risk category 1308 can be associated with specific risk factors and metrics that are used by models 1306 to evaluate the level of risk of the model use case 1302. For example, data privacy risks can be assessed based on the sensitivity of the data used by the model, while model bias risks can be evaluated based on the fairness and equity of the model's predictions.

Criteria 1310 are the specific requirements or standards that the model use case 1302 must meet. Criteria 1310 ensures that the model use case 1302 operates within acceptable parameters and adhere to regulatory, ethical, and performance standards. Criteria 1310 can include various technical and non-technical requirements such as accuracy thresholds, data quality standards, ethical guidelines, and regulatory compliance requirements. For example, accuracy thresholds can specify the minimum acceptable accuracy for the model's predictions, while data quality standards can define the required quality and integrity of the data used by the model. Methods of evaluating model use case 1302 against the criteria 1310 are discussed with further reference to FIG. 14.

Gaps 1312 refer to the discrepancies or deviations between the expected criteria and the actual performance or characteristics of the model use case 1302, and can include any type of deficiencies, absent obligations, or absent requirements. Gaps 1312 can be identified through various methods discussed with reference to FIG. 14. Compliance actions 1314 are the steps taken to address the identified gaps and ensure that model use case 1302 meets the defined criteria. Compliance actions 1314 can include modifications to the model use case 1302, updates to the data, and/or changes in the operational processes. In some implementations, compliance actions 1314 can include various technical and non-technical measures such as retraining the model with new data, updating the model's algorithms, implementing data privacy controls, and conducting regular audits and reviews. For example, retraining a model in the model use case 1302 with new data can help to improve its accuracy and reduce bias, while implementing data privacy controls may help to protect the privacy and confidentiality of the data used by the model. Compliance actions, in some implementations, can be automatically executed using methods discussed with reference to FIG. 14.

The monitoring loop 1316 continuously observes the model use case 1302, updating and refining the model use case 1302 as needed to maintain compliance over time. Monitoring loop 1316 is the continuous process of observing, evaluating, and updating the AI models and their use cases, and can be the same as or similar to feedback loop 1222 with reference to FIG. 12. The monitoring loop 1316 ensures that the models remain compliant over time, adapting to new data, changing conditions, and evolving standards. For example, continuous performance monitoring using the monitoring loop 1316 can involve tracking the model use case's 1302 performance metrics in real-time and identifying any deviations from the expected criteria, or conducting periodic assessments at predefined intervals of the model use case's 1302 data, algorithms, and processes to ensure compliance with the defined criteria. In some implementations, the system can continuously scan for changes in regulations, or receive a regulation update from a user input. When a new regulation or regulatory update is identified, the system automatically updates the compliance criteria and maps the new criteria to the specific AI use cases, updating the existing compliance rules and parameters using methods discussed with reference to FIG. 14.

FIG. 14 is a flow diagram illustrating a process 1400 of identifying and remediating gaps in compliance for AI use cases using a generative AI model, in accordance with some implementations of the present technology. In some implementations, the process 1400 is performed by components of example devices 200 and computing devices 302 illustrated and described in more detail with reference to FIG. 2 and FIG. 3, respectively. Particular entities, for example, the sets of AI models, are illustrated and described in more detail with reference to FIG. 12 and FIG. 13. Likewise, implementations can include different and/or additional steps or can perform the steps in different orders.

In operation 1402, the system (e.g., compliance engine 1214 in FIG. 12, compliance engine 1304 in FIG. 13) receives, from a computing device, (1) a set of alphanumeric characters (e.g., regulations, guidelines) defining one or more operative boundaries of a set of expected model use cases configured to adhere to constraints of the set of alphanumeric characters and (2) a set of operational data containing one or more of: structured data or unstructured data. The set of operational data can include data from, for example, third-party tools (e.g., downstream suppliers).

The set of expected model use cases can be, for example, “AI system” as defined in the regulations, such as the EU AI Act, California SB-1047, and/or guidelines 1210 in FIG. 12. The regulations alphanumeric characters represent the specific regulatory requirements or guidelines for the AI models in the model use case. By receiving the inputs, the compliance engine can evaluate the AI models against the defined constraints, ensuring that the model use case adheres to the regulatory standards. The operational data, which can be structured (e.g., databases, spreadsheets) or unstructured (e.g., text, images), provides the context for assessing the models' performance and compliance. For operational structured data, the system can convert data types and remove missing values, while for unstructured data, NLP for text data or image preprocessing for visual data can be employed. The compliance engine can translate alphanumeric characters representing regulatory guidelines into actionable rules and constraints using methods discussed in FIG. 8, and subsequently map the actionable rules to portions of the model use case. For example, the EU AI Act can require documentation of data governance practices, which can be mapped to specific data handling and storage requirements within the model use case.

The set of expected model use cases can include a set of attributes common among each expected model use case in the set of expected model use cases. The set of attributes can include characteristics that define “AI system” in the regulations, such as those outlined in the EU AI Act, or “covered model” in California SB-1047, and so forth. For example, for a regulation defining “AI system” as “systems designed to operate with a degree of autonomy and that can generate outputs such as content, predictions, recommendations, or decisions influencing real or virtual environments,” the set of attributes can include autonomy, output generation, data governance, algorithmic transparency, risk management, human oversight, ethical considerations, and/or compliance documentation.

Using the set of attributes common among the set of expected model use cases in operation 1404, the system constructs, by a first set of AI models, a set of observed model use cases (e.g., an inventory) from the set of operational data. Each particular observed model use case of the set of observed model use cases can include a set of features of the particular observed model use case. The set of features of the particular observed model use case can include, for example, a text-based description of the particular observed model use case, an expected input of the particular observed model use case, an expected output of the particular observed model use case, one or more AI models configured to generate the expected output of the particular observed model use case using the expected input of the particular observed model use case, and/or data supporting the one or more AI models.

To obtain the set of observed model use cases from the operational data, the system can review an existing (e.g., internal) set of explicit and implicit definitions of AI, machine learning, and/or generative AI, and compare the definitions with: (1) definitions provided by a particular regulatory regime, such as the EU AI Act and California SB-1047, and/or (2) other regulations in the absence of a definition, such as the GDPR, NIST, and the OECD. The system can, in some implementations, identify definitional gaps and generate recommendations (or automatically executed actions) to remediate the definitional gaps by aligning the organizational definitions with the regulatory definitions using methods discussed with reference to FIGS. 5-8.

In some cases, the implementations can receive a structured AI use case input and determine applicable predefined regulations of the AI use case using rule-based systems and/or one or more AI models. The relevant regulations can be specifically selected based on the context and requirements of the AI use case being evaluated. The system analyzes metadata tags, keywords, or categories associated with the regulations stored in its database and uses NLP to parse the text of the regulations, identifying terms and clauses relevant to the AI application's domain. Regulations can be stored in vector space, allowing the system to calculate the similarity between vectors representing domain-specific terms and other related terms, applying a similarity threshold to filter out insufficiently similar terms. Additionally, the system tags relevant regulations with attributes that categorize the regulations based on criteria such as regulatory topics or jurisdictional relevance.

When processing unstructured data such as text documents, emails, or social media posts, the system can extract relevant operational data that aligns with the set of attributes, such as autonomy or output generation. For example, autonomy-related data can be identified by looking for phrases indicating independent operation, while output generation data can be extracted by identifying mentions of content creation, predictions, or recommendations. Once the relevant data is extracted, the operational data can be mapped to the regulatory attributes and organized into a structured model use case. Each use case can include descriptions of the data sources, algorithms, or other features of the model(s) used.

In some implementations, the system identifies, using a third set of AI models (which can be the same as or different from the first set of AI models and/or the second set of AI models), a portion of the set of observed model use cases. The third set of AI models can, for example, perform a Retrieval-Augmented Generation (RAG) search of the operational data using the set of attributes. During training, the third set of AI models learn to retrieve relevant information from a large dataset based on the input attributes and generate meaningful responses or insights. The retrieval component of the RAG model searches the operational data to find relevant model use cases that match the input attributes (e.g., type of model used, location, user type) using techniques such as vector similarity search, where the attributes are converted into vector representations, and the model searches for similar vectors within the dataset. The generation component uses the retrieved information to generate a contextually relevant output, which can be an identification of a portion of the observed model use cases.

For each particular observed model use case of the set of observed model use cases, in operation 1406, the system uses a second set of AI models to map the set of alphanumeric characters and the set of features of the particular observed model use case to a risk category defined within the set of alphanumeric characters. The second set of AI models can select the risk category from a plurality of risk categories defined within the set of alphanumeric characters in accordance with a level of risk associated with the set of features. For example, the second set of AI models can be trained on a dataset that includes both regulatory text and features of various model use cases, along with their corresponding risk categories. During training, the second set of AI models learn to recognize patterns and relationships between the regulatory requirements (which can be expressed as vector representations) and the features of the model use cases (which can be expressed as vector representations).

In some implementations, the set of alphanumeric characters can be converted into vector representations using text embedding techniques such as Word2Vec, GloVe, or BERT to capture the semantic and contextual meaning of the regulations while transforming them into numerical vectors. In some implementations, the features of the observed model use case, such as data quality, algorithmic transparency, and potential biases, are also converted into numerical vectors using similar methods. Once both the regulatory text and the model use case features are represented as vectors, the vectors are combined into a unified feature space. The second set of AI models can evaluate the combined vector against learned patterns and relationships within the second set of AI models' training data, assigning a risk category based on the alignment between the regulatory requirements and the observed model use case features. In some implementations, the second set of AI models identifies a mapping between the set of features of the particular observed model use case and the set of vector representations of the set of alphanumeric characters using a distance between a set of words within the set of features of the particular observed model use case and respective mapped alphanumeric characters within the set of alphanumeric characters. A distance metric can be used to calculate the similarity or dissimilarity between the vectors. For example, Euclidean distance measures the straight-line distance between two points in a vector space, cosine similarity measures the cosine of the angle between two vectors, and Manhattan distance measures the sum of the absolute differences between the coordinates of two points. The calculated distances help identify the mapping between the features of the model use case and the regulatory text. Features that are closer in the vector space to specific regulatory requirements indicate a stronger alignment. For example, if a feature vector representing “data encryption” is close to a regulatory vector emphasizing “data security,” this indicates a strong alignment.

In some implementations, the second set of AI models, which can be the same as or different from the first set of AI models, can generate a risk score for each observed model use case. This risk score can be used to categorize the use case into one of the predefined risk categories, such as low, medium, or high risk. For example, a use case with high data quality, transparent algorithms, minimal biases, and low decision-making impact can be categorized as low risk. Conversely, a use case with poor data quality, opaque algorithms, significant biases, and high decision-making impact can be categorized as high risk. To generate a risk score, the system can use a multi-agent architecture within the second set of AI models, where different agents (i.e., model(s)) are specialized in evaluating specific risk factors such as data quality, algorithmic transparency, and potential biases. Each agent independently assesses the assigned factor and generates a partial risk score. The partial scores are aggregated to form an overall risk score for the use case. In some implementations, the system uses an ensemble method, such as averaging the scores from multiple models. In some implementations, the system generates initial risk scores using simpler models and refines the risk score using more complex models. For example, a basic decision tree can first categorize the model use case into broad risk categories, which are fine-tuned by a neural network that considers more nuanced factors (e.g., factors specific to the regulation). In some implementations, the multi-agent architecture is similarly used by the first set of AI models to construct the set of observed model use cases.

In operation 1408, for each particular observed model use case of the set of observed model use cases, the system uses the second set of AI models (or by user input) to identify a set of criteria of the particular observed model use case within the set of alphanumeric characters. The system extracts a set of keywords from the set of alphanumeric characters. The system can map the extracted set of keywords to the set of criteria within the set of alphanumeric characters associated with the mapped risk category. The system can search the set of alphanumeric characters for sentences or phrases that contain the identified keywords and extracting the relevant information. For example, if the keyword “encryption” is identified, the system searches for sentences that mention “encryption” and extracts the criteria related to data encryption. Additionally, using the identified criteria, the system can compare different model use cases to determine whether certain model use cases pose a lower risk than others.

After extracting the criteria, the system can associate and map them with the specific risk category. The system can use one or more models trained on labeled datasets where each criterion is tagged with its corresponding risk category. These models, which can include logistic regression, SVM, or neural networks, can be trained to recognize patterns and relationships between the criteria and risk categories. During the training phase, the models learn to identify which criteria are indicative of specific risk levels. For instance, the model learns that “data encryption,” “multi-factor authentication,” and “regular audits” are indicative of a “High Risk” category, while “basic password protection” and “firewall implementation” are associated with “Medium Risk,” and “antivirus software” is linked to “Low Risk.” Once trained, the models can be deployed to automatically classify new criteria into the appropriate risk categories.

In operation 1410, for each particular observed model use case of the set of observed model use cases, the system uses the second set of AI models to identify a set of gaps of the particular observed model use case by comparing the set of criteria of the particular observed model use case with the set of features of the particular observed model use case. A gap is identified when the features do not fully meet the criteria, indicating a potential compliance or performance issue. For example, if the criteria require “data encryption” and “multi-factor authentication,” but the operational data within the model use case only include “basic password protection,” the second set of AI models can flag this as a gap. Methods of identifying a gap are discussed with reference to FIGS. 6-8.

In some implementations, the system determines, for each gap in the set of gaps, a type of gap between the set of criteria of the particular observed model use case and the set of features of the particular observed model use case. The type of gap can be associated with one or more of: a presence of a particular set of alphanumeric characters of the particular observed model use case or a semantic meaning of one or more sets of alphanumeric characters of the particular observed model use case. The system can, using the type of difference, classify each gap in the set of gaps. In some implementations, the system triggers one or more alarms in response to the classification of a particular gap in the set of gaps reaching a predetermined threshold.

In operation 1412, using the identified set of gaps, the system generates a set of actions to be performed related to the particular observed model use case configured to satisfy the set of criteria of the particular observed model use case. In some implementations, the system validates, using a third set of AI models, satisfaction of the set of actions with the set of criteria of the particular observed model use case.

In some implementations, the system, using the generated set of actions, automatically triggers an automated workflow indicated by the generated set of actions to execute the generated set of actions. In some implementations, the system converts existing documentation (e.g., previously aggregated documentation) into new documentation that meets new regulatory requirements. For example, financial institutions are expected to comply with both MRM requirements and EU AI Act obligations, which differ in their risk rating methodologies. MRM takes a holistic view on risk classification (e.g., AI related to lending does not necessarily warrant a high-risk rating), while the EU AI Act adopts a use case-based categorical approach (e.g., all AI related to lending is high-risk with corresponding obligations even though there may be exceptions). The system can analyze existing documentation in conjunction with the text of relevant regulations to classify the risk category (while disregarding the preassigned risk rating provided by other regulations). Subsequently, the system utilizes the existing documentation to generate new documentation that complies with the updated regulatory requirements.

In some implementations, in generating and/or executing the set of actions, the system enables a set of agents to operate autonomously and make decisions and/or execute the set of actions based on programming, learned behavior, and/or suggestions from other models (e.g., AI models, LLMs, GenAI). For example, the agents use LLMs to ask for suggestions on what action to trigger and fetch the parameters for those actions. For example, a user may prompt the agent with a command that triggers the action of automatically adjusting a set of operational data to comply with a set of criteria of a regulatory category (e.g., a risk category) by remediating any identified gaps. The actions can be triggered by user queries as well as based on any operational data received by the first set of AI models and/or the second set of AI models.

To validate and govern the agents' actions to ensure that the agents do not take any unwanted or unapproved actions, the system can intercept and examine actions performed by an autonomous agent before execution and/or audits and logs the actions after execution. For example, the system can compare each intended action against a set of predefined rules and boundaries established by human operators (e.g., through a set of prompts, pre-loaded query context). If an action falls outside of the boundaries, the action can be flagged as unauthorized, and the system prevents the execution of the action. In some implementations, the system validates that all actions taken by the agent adhere to industry-specific standards, legal regulations, and ethical guidelines. The system can cross-reference actions with a database (e.g., static database, dynamic database) that includes the agent's permissible actions. The system can, in some implementations, use one or more LLMs (or a voting mechanism between multiple LLMs) to ensure that the agent's actions are fair, unbiased, and/or aligned with ethical standards. For example, rather than relying on a single algorithm, the system can query multiple LLMs to evaluate the proposed action. If the majority of the LLMs agree that the action is appropriate and free from bias, the action can proceed. If there is disagreement or a potential bias is detected, the unit can either automatically adjust the action or flag the action for human review. The system can generate reports that summarize the agent's compliance status, unauthorized actions prevented, and any bias corrections made.

In some implementations, for each particular observed model use case of the set of observed model use cases, the system maps each particular observed model use case to a timestamp of the set of alphanumeric characters and when the particular observed model use case was constructed (e.g., for auditing purposes). Using a third set of AI models, the system can aggregate a set of documents associated with the particular observed model use case. The set of documents satisfies the set of criteria corresponding to the mapped risk category. In some implementations, the set of documents include the mapped timestamp.

In some implementations, the system versions (e.g., assigns a version) and/or appends timestamps onto one or more of the classifications of the observed model use cases, the set of alphanumeric characters, the operational data, and so forth. For example, each version of the classification, such as Version A, can be dated (e.g., Version A, dated September 2030) and includes a detailed classification of the observed model use case. This classification can be accompanied by an explanation of the particular subset of the set of alphanumeric characters used to classify the observed model use case, (thus enabling users to trace the system's decision-making process). The explanation can include, in some implementations, the underlying instructions (e.g., queries, prompts, assumptions, operational data, metadata appended to the operational data, pre-loaded query context) used by the model(s) (e.g., the first set of AI models, the second set of AI models, the third set of AI models). In some implementations, the explanation defines the set of parameters used by one or more AI models of at least one of: the first set of AI models or the second set of AI models to generate one or more of: the risk category, the set of criteria, or the set of gaps of the particular observed model use case.

For instance, if an observed model use case is classified under a high-risk category, the system can document one or more of: (i) the classification (e.g., high-risk), (ii) an explanation of the classification (e.g., the definition of “high risk” in the regulation, the specific rules and criteria within the regulation that led to the classification), or (iii) the underlying assumptions taken by one or more models used to classify the observed model use case (e.g., the interpretation of the regulation as of a particular date). The documentation can include, for example, the prompts used by the AI model(s) to interpret the regulatory texts and map the compliance criteria to the observed model use case. By maintaining a versioned record of the classifications, the system ensures that each decision is traceable and can be audited at any time to verify that the system's classifications are consistent with the latest regulatory requirements and standards.