Many companies are already experimenting with generative AI for drafting, research, summarization, and analytics. New tools are appearing in nearly every department, often faster than governance frameworks can adapt. For legal and legal operations teams, this creates a complicated balancing act. Legal leaders are expected to support innovation while also protecting confidential data, maintaining regulatory compliance, and managing risk.
For most legal departments, the challenge is visibility. Without clear insight into how AI tools are being used, it becomes difficult to manage risk or develop responsible governance practices.
One practical way to address this challenge is by building an AI registry that tracks where AI tools are used internally and how they interact with company data.
Building an AI Registry
Most organizations underestimate how quickly AI tools spread among employees. A single pilot project often expands into broader adoption across departments. Teams begin using AI to draft documents, analyze data, summarize meetings, or support research. At the same time, software vendors are embedding AI features directly into their platforms, sometimes without significant changes to the user interface.
An AI registry provides a structured way to understand that landscape. At its core, an AI registry is a centralized system that captures information about the AI tools employees use and how those tools interact with company data.
Instead of relying on informal awareness or scattered approvals, a registry creates a single place where legal, compliance, and security teams can view AI activity across the enterprise.
Why an AI Registry Matters
Without an AI registry, organizations often operate with incomplete information about AI usage. This makes it difficult to evaluate whether employees are sharing sensitive data with external systems or whether multiple teams are purchasing redundant tools.
Legal departments benefit from having a comprehensive view of AI adoption because it allows them to work collaboratively with other teams. Security teams gain visibility into vendor data practices. Procurement teams can identify opportunities to consolidate tools. Business leaders can discover high-value use cases that deserve further investment.
What Data Should an AI Registry Capture?
Once a legal team decides to implement an AI registry, the next question is what information should be collected. The goal is not to create an overly complex intake process. Instead, the registry should capture enough information to help legal, compliance, and security teams understand how AI tools interact with company data.
Most AI registry forms start with a few core categories: the tool itself, the intended use case, who will use it, and the type of data involved.
Basic Tool Information
The first section of an AI registry should capture details about the technology being introduced. This helps legal and security teams understand the nature of the tool and how it fits into the broader technology environment.
- What software or AI tool will be used?
- Is the tool internally built, vendor-provided, or an AI-assistant embedded in another platform?
- What functionality does the tool provide?
- What geographic regions will the tool be used in?
- Which departments or teams plan to use the tool?
Capturing this information early helps organizations track where and what AI tools are being used.
Understanding the Use Case
An AI registry should also capture the context behind the request. Knowing why a tool is being used can reveal whether the use case carries operational or regulatory implications.
- What is the intended use case for the AI tool?
- Is this submission for a pilot or proof of concept?
- Who will interact with the tool?
- Will the output of the tool be used outside the company, such as on a website or social media?
Distinguishing between experimentation and production use helps legal teams decide how much review is required before the tool is deployed.
Data and Model Considerations
One of the most important aspects of any AI registry is understanding how company data interacts with the AI system.
- Will the tool be trained using company data?
- What type of data will be used or processed?
- Will confidential or regulated information be included?
- What type of AI model powers the tool?
Some organizations distinguish between generative AI models and traditional machine learning tools. Others simply ask whether the system produces new content based on prompts.
TIP: Some employees may not understand technical AI terms. It can be helpful to include definitions or examples within the form itself.
Risk-Sensitive Use Cases
Certain types of AI use cases require closer oversight from legal teams. An AI registry can help flag these situations by asking targeted questions.
For example:
- Will the tool be used for employment or HR-related decisions?
- Could the use case impact health or safety outcomes?
- Will the outputs influence regulatory filings, contracts, or external communications?
- Will every output generated by the tool be reviewed by a human?
Responses to these questions can automatically trigger additional reviews within the AI registry.
Simplifying the Process with Risk Logic
An AI registry works best when it includes built-in logic that helps streamline approvals. Not every request requires the same level of review.
For example, organizations may decide that:
- Pilot projects are automatically approved as long as they do not involve high-risk data or safety implications.
- Requests involving HR decisions or safety require legal review.
- AI tools that process confidential or regulated data must go through security review.
This approach helps legal teams focus their attention on higher-risk deployments while still encouraging experimentation.
Risk Security Review Example
Risk Level | Example Criteria | Review Required |
|---|
Low Risk | Pilot use, internal content drafting | Auto approval |
Medium Risk | Internal analytics using company data | Security review |
High Risk | HR decisions or safety impact | Legal + security review |
Designing a Registry People Will Actually Use
One of the most important considerations when building an AI registry is usability. If the process is overly complicated, employees may avoid submitting requests entirely.
Successful AI registries usually follow a few principles:
- Keep forms simple and conversational
- Use dropdown options instead of open text where possible
- Provide definitions and helper text for unfamiliar AI terminology
- Use conditional logic so users only see relevant questions
The goal is to make the registry feel like a quick intake process rather than a compliance burden.
Tip: Design the registry for adaptability. AI use cases evolve quickly, so the form, approval matrix, and workflows should allow for regular updates without requiring major changes.
Summary
Generative AI tools are spreading quickly across enterprise teams, often faster than governance processes can keep up. For legal departments, the biggest challenge is visibility into where AI tools are being used and how they interact with company data. Building an internal AI registry provides a practical way to track AI usage, assess risk, and ensure legal, compliance, and security teams have oversight as adoption grows.
