The AI Registry with Superpowers
A centralized AI Registry is the foundation for effective AI governance. It provides a living system of record to gain visibility into your AI landscape – from discovering AI to managing assets across the whole lifecycle – while empowering teams to scale compliant AI deployment through automated and self-serve processes.
In brief:
As enterprise AI adoption accelerates, organizations often struggle to keep track of their AI models, use cases, agents and third-party tools. See why a centralized AI Registry is a crucial first step for effective AI governance and how you can give it superpowers.
We break down the two main challenges – gaining visibility into your AI landscape and properly assessing your assets – and show how trail's automated, living AI Registry replaces fragmented collections to help you innovate confidently and compliantly.
In one view:
With trail’s registry, your organization can:
- Centralize AI assets: Store and map dependencies for all AI models, systems, agents, and vendors in one living hub.
- Automate AI discovery: Detect and sync shadow AI instantly from your existing GRC and developer tools or agent builders across the organization.
- Manage ideation: Evaluate use case ideas centrally to identify governance requirements early and accelerate innovation.
- Enrich assessments of AI: Automatically pull relevant context and data for faster, more accurate risk evaluations.
- Scale governance with self-serve: Empower business and engineering teams with standardized workflows to reduce bottlenecks in compliance teams.
- Customize workflows: Dynamically map the right policies, controls, and alerts to each asset based on its unique risk profile.
- Govern AI vendors: Seamlessly integrate third-party AI risk management alongside your internal systems.
Why do you need an AI Registry? And how does a great one look like?
Problem 1: Gaining visibility into your organization
Creating a ground truth for governance
To be able to govern AI you first need to keep track of AI. This is often the first major hurdle for most enterprises. AI assets are fragmented across disparate tools and disconnected business units or teams, often still living in basic Excel spreadsheets . Without a centralized view, leadership lacks visibility into what AI projects and assets are actually ongoing or live, who owns them, and what their current governance status is.
trail solves this by providing a living, continuous system of record. The AI Registry of trail collects and stores all relevant information for your AI models, agents, systems, use cases and vendors in one accessible hub. Beyond just listing assets, trail uniquely maps and visualizes the intricate dependencies between them, giving you a complete, interconnected overview of your AI landscape and its compliance posture. This allows you to make use of already performed assessments or collected evidence of your model or vendor, for instance, across multiple assets later on, or it can give you a better understanding of how requirements or risks may change on a use case if an underlying model has changed.
Discovering AI assets
Building on the visibility challenge, simply finding and discovering (shadow) AI is a massive operational burden – which is even more challenging with the rising amount of AI agents. Data about these assets is often buried in legacy platforms, developer tools, agent builders, or isolated silos. Manually hunting down untracked systems to keep an inventory up to date across a large organization is inefficient and often prone to human error.
trail automates this AI intake process through integrations with your existing asset inventories, GRC platforms, and the environments where AI is actively built (these sources can include tools like OneTrust, ServiceNow, Collibra, SAP LeanIX, Notion, Confluence, MLOps platforms like Databricks, or agent builders like Microsoft Copilot Studio or n8n, just to name a few). These connections can seamlessly detect and pull AI assets directly into the registry. Where needed, trail can also write back updated governance data to your preferred central system of record, ensuring your broader enterprise architecture stays perfectly in sync without double documentation and creating new silos.
Managing use case ideas
Effective AI governance does not begin at deployment or when you start your procurement process – it already starts during use case ideation. This initial planning stage inherently shapes future governance requirements and potential risks. And this is why in most organizations with an advanced governance maturity, you typically find governance specialists as part of the AI Center of Excellence or innovation unit supporting in that early stage. However, without a structured process, teams often keep working in silos, leading to disjointed evaluations, delayed decisions, and redundant, double entries of similar ideas across the company.
trail allows organizations to centrally manage and evaluate new AI ideas across teams in a structured, transparent way. By capturing potential risks and requirements from day one, it eliminates duplicate requests and streamlines approvals, e.g. by defining greenlighting conditions for fast-track AI deployments. Instead of acting as a blocker, this early-stage governance involvement becomes a true innovation driver, guiding teams to build compliant, high-value AI much faster.
Problem 2: Know your AI
Having the right information for your AI assessment
When you collect your AI assets the next step is to assess them to identify the necessary governance requirements and risks. Assessing an AI asset requires a deep understanding of its context, data, and technical specifications, however. Unfortunately, reviewers usually have to chase down this information across scattered documents, code repositories, suppliers and teams. This manual data gathering delays assessments, and hence your AI deployment, and could even lead to incomplete risk profiles.
trail automatically pulls and enriches asset data from your existing tools, repositories and other sources, such as attached PDFs or configuration files, through integrations – and by using agents. Especially trail’s Agent Flows are a powerful tool to help you assess the relevancy of your collected sources and to further populate questionnaires and the asset cards containing all relevant information about your asset.
Standardizing governance workflows for effective self-serve
A lack of standardized procedures and assessments to understand the risks and governance requirements of an AI asset better often leads to incomplete data across different departments. And this absence of a clear process additionally generates confusion for technical and business (1st LoD) teams, who are left guessing what is actually required for proper governance. With GRC teams typically facing limited headcount, manually guiding every AI project is impossible. Ideally, organizations need a self-serve approach that empowers the teams who bring in the AI use cases to meet 80-90% of governance requirements independently, freeing up human resources. Instructing your teams what to do when is yet a key struggle here.
trail’s AI Registry not only collects your assets and their relevant information but also provides helpful user guidance, such as recommending the right and standardized assessments and questionnaires for a specific asset.
Additionally, trail then recommends the right governance requirements coming from your policies, the necessary controls to implement or check for and the potential risks to mitigate. All from your asset card and supported with dashboards and analytics to oversee governance status, as well as trail’s Agent Flows to automate whole compliance processes. We’ve designed all of these features in a user-friendly experience for both your 1st LoD and the 2nd LoD.
