Govern Third-Party AI Without Slowing Down Innovation

In brief

As enterprises accelerate their use of third-party AI – from foundation models to off-the-shelf agents – a new governance gap is opening up. Traditional vendor due diligence was designed for software that stays static. But AI doesn't. With trail you can connect vendors to the use cases they power, standardizes and automate assessments and evidence collection, and keep reviews current as vendors (and their AI capabilities) evolve.

In one view:

With trail's third-party AI governance, your organization can:

• Track externally supplied AI and IT assets: Track vendors and externally provided models, systems, and tools in one place – alongside your internal AI use cases.
• Standardize and automate vendor assessments: With the help of AI agents, run vendor assessments, control assessments, and custom questionnaires to identify risks and the requirements met.
• Collect vendor documents and evidence: Aggregate relevant documentation and sources of your vendors, including vendor templates and public information from the web, as a basis for your assessments.
• Operationalize continuous monitoring: Keep evidence, reports, and supply chain information up to date, and get alerts when assessments are outdated.
• Reduce vendor sprawl: Visualize (re-)use and dependencies between vendors and assets to avoid duplicate evaluations across teams and use cases.

Challenge 1: How do you get full visibility into your third-party AI landscape?

Before you can govern third-party AI, you need to know what's actually in use – and for most organizations, discovering and collecting AI systems in use is already a big challenge.

Business units adopt AI tools directly. Employees use consumer-grade models without involving IT. Traditional SaaS vendors quietly add AI capabilities to products that were assessed and approved years ago, without triggering any new reviews. And when multiple teams are each evaluating vendors and use cases independently, the same tool can be assessed three times in parallel by people who don't know the others have done it already.

The result is a third-party AI landscape that is simultaneously over-reviewed in some places and entirely invisible in others. You can't make risk-based decisions about vendors you don't know exist, and you can't avoid redundant work without a shared view of what's already been done.

This problem compounds at the supply chain level. A single third-party AI tool may itself rely on an underlying foundation model from another provider, sub-processors in multiple jurisdictions, and APIs from further vendors. Without mapping relevant dependencies, your vendor governance ends on the first layer.

trail's AI Registry provides a single, continuously updated hub for all externally supplied AI – use cases, tools, models, and agents – alongside your internally built AI use cases. Integrations with GRC platforms, asset inventories, tool request forms, and citizen developer platforms surface third-party AI automatically for your AI governance team, including AI that would have bypassed formal governance processes. Dependency mapping visualizes the asset structure behind each vendor, and a shared registry means teams can see and reuse existing assessments rather than duplicating work when creating multiple use cases.

Challenge 2: How do you assess vendors without creating manual overhead that slows everyone?

Once you know what third-party AI tools and vendors are in use, assessing them properly is a significant operational challenge.

Vendor assessments depend on documentation that has to be tracked down: security questionnaires, data processing agreements, model cards, sub-processor lists, and compliance certifications. For a portfolio of dozens of vendors, chasing this manually is a substantial burden. And even when an assessment is completed thoroughly, it captures only a point in time. AI models are updated – sometimes silently – and a system or vendor that met your requirements at procurement may look different six months later.

The operational reality is that most organizations complete a vendor assessment once and file it. There is no systematic process to re-evaluate when a model version changes, when a vendor's compliance posture shifts, or when a tool that was low-risk at onboarding gets repurposed for a higher-stakes use case. Governance records grow stale, and the gap between what's documented and what's true widens continuously.

trail’s governance solution can not only help you to standardize your assessments at intake through vendor templates populated with the most current evidence, like your vendor’s policies. The governance agents of trail can also automate the evidence-gathering and filling out of questionnaires that make vendor assessments slow – pulling relevant documentation, public information, and vendor-provided materials without manual screening. And continuous monitoring means assessments don't just happen once: trail flags when evidence becomes outdated and can re-trigger risk and control assessments automatically, so your vendor governance reflects the current state of your stack, not the state it was in two years ago.

Learn more about how our governance agents can help you to automate your governance tasks like assessing vendors.

Challenge 3: How do you keep track of decisions and accountabilities when something goes wrong?

When an external AI tool produces a harmful output, a biased decision, or a compliance failure, accountability is rarely obvious. Is it the vendor's problem because they built the tool or model? Yours because you deployed it? Your customer's because they used it wrongly? The answer may be a combination of all the above – but without documented ownership and controls, escalation paths, and incident response responsibilities mapped to specific vendors and use cases, this question gets answered under pressure rather than in advance.

The regulatory dimension makes this sharper: under the EU AI Act, the boundary between "deployer" and "provider" is not fixed – it depends on how you use and modify a third-party AI system or model. Using a general-purpose model in a way it wasn't designed for, deploying an AI system under your own brand, or substantially modifying a model through fine-tuning can all reclassify your organization as the legal provider of that system or model – with significantly more onerous compliance obligations, including technical documentation, conformity assessments, and registration requirements. Together with our friends at the Future of Life Institute, we’ve discussed this topic specifically in another article. Your organization should have a process to detect when that threshold has been crossed.

Not sure yet how your AI use case could classify under the EU AI Act? Try our self-assessment.

trail maps the relationship between every third-party vendor and the use cases built on top of them so that when a compliance question or incident arises, you can immediately trace which vendor may be implicated, what controls are in place, and who owns the risk. Risk ownership is documented at the asset level, not left implicit. trail also tracks the conditions that determine your regulatory role for each external system – flagging when a use case configuration might trigger a deployer-to-provider reclassification under the EU AI Act – and surfaces the documentation and control requirements that apply.

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Third-party AI governance is not a separate workstream

Third-party AI risk spans the full lifecycle: from the moment an employee first considers using an external tool, through procurement and onboarding, through ongoing monitoring and re-assessment, to eventual replacement. Governing it properly requires the same infrastructure as internal AI governance – a living registry, structured workflows, continuous evidence collection, and clear accountability. The difference is that you're governing assets you don't control completely, which makes visibility and structure even more critical.

trail integrates third-party AI risk management (TPRM) directly into your AI governance program – not as a bolt-on, but as a capability built into the platform from the start of your intake. External vendors are tracked alongside internal models and agents. Assessments are automated. Dependencies are mapped. Evidence is collected continuously.

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Ready to take control of your third-party AI?

Your AI supply chain is growing faster than your governance can keep up with. trail gives you the structure, automation, and visibility to govern external AI tools with the same rigor as the AI you would build yourself – without building additional headcount or restructuring your governance landscape. Get in touch to see how trail fits your organization's AI governance program.

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FAQ

What counts as third-party AI?

Any AI system, model, or tool provided by an external vendor – whether accessed via API, deployed as SaaS, embedded in a product you use, or integrated as a component into your own AI applications. This includes foundation model APIs (like OpenAI, Anthropic, or Mistral), off-the-shelf AI agents or AI-enhanced SaaS tools.

Why isn't a vendor questionnaire at procurement of my AI tool enough?

Because AI systems change after you onboard them. Models are updated, safety guardrails may be modified, training data changes – often without formal notification to deployers and end-users. A point-in-time assessment captures vendor compliance at one moment; it tells you nothing about the vendor six months later. Continuous oversight is required to govern the actual risk, not the risk as it existed at signing.

Does trail include built-in information on AI vendors and models?

Yes, trail comes with a built-in library of vendor and model cards, including critical compliance documents and information useful for vendor assessments and evaluation. The library is continuously updated, and new entries can be added upon request. You can add your own of course!

Can trail handle vendor assessments and questionnaires?

In trail, users can initiate control assessments, vendor assessments, or custom questionnaires directly from any AI registry entry. Assessments evaluate control effectiveness based on linked evidence and sources. Questionnaires support all sorts of question types and outcomes for dynamic evaluations. All of that can be even automated completely with trail’s governance agents.

How does trail help identify risks and controls for a new vendor?

Once a vendor, model, or system is created in the AI registry, trail can automatically scan the entry details and recommend relevant risks, governance requirements (such as from the EU AI Act or NIST AI RMF), and associated controls. Relevant owners can then review and approve these recommendations.

What's the risk of using an AI tool for a high-stakes decision in the EU?

Under the EU AI Act, deployers who use an AI system for a use case that falls within a high-risk category – and where that tool was not specifically designed and marketed for that purpose – can become the legal provider of a high-risk AI system. That means substantially more compliance obligations: technical documentation, conformity assessments, registration requirements. This is one of the least understood risks in enterprise AI deployment today.

Does trail help with EU AI Act compliance for third-party AI?

Yes, trail maps the compliance obligations that apply to each use case on your external AI system based on its risk class, intended use, and your organization's role (deployer vs. provider). It flags whether the conditions that might reclassify your organization as a provider are met, recommends the appropriate controls and documentation requirements, and stores audit-ready evidence of your compliance posture.

How does trail discover third-party AI tools that weren't formally procured?

trail integrates with your existing asset inventories, GRC platforms, developer tools, and agent builders / citizen development platforms to automatically detect AI assets in use across the organization. When shadow AI is discovered, it's surfaced in the registry and routed into your standard assessment or governance workflow that you’ve defined.

Can trail work alongside our existing vendor management or GRC tools?

Yes, trail is designed to complement your existing GRC stack, not replace it. It integrates with platforms like OneTrust, ServiceNow, Collibra, and SAP LeanIX – pulling AI-relevant vendor information into the trail registry and, where needed, writing governance data back to your system of record of choice. You don't have to choose between trail and your existing tools, but you can use trail as the automation layer on top of your stack.