AI Vendor Risk Assessment: 15 Questions to Add to Your TPRM Program

Most enterprise vendors were assessed for a different kind of software. Today, the CRM you approved three years ago drafts customer emails. Your HR platform scores candidates. Your support desk sends tickets to a large language model your team has never reviewed.

None of these changes triggered a new review. They arrived as product updates. Meanwhile, your data started flowing to new processors under terms nobody negotiated. Across hundreds of SaaS vendors, that adds up to a large, unmeasured shift in third-party risk.

For CISOs and TPRM leaders, AI vendor risk assessment is not just a technical task. It is a governance question. Can you show your board, auditors, and regulators that AI risk in your supply chain is identified, assessed, and owned?

The good news is that you do not need a new program. Instead, you need sharper due diligence inside the one you already run. Below are 15 questions to add to your AI vendor assessments, grouped by risk area, along with the answers that should trigger escalation.

Why do standard vendor questionnaires miss AI risk?

Standard questionnaires and SOC 2 reports were built to check infrastructure controls, such as encryption, access management, incident response, and resilience. These controls still matter. However, they are no longer enough.

AI adds risks those tools were never designed to test. For example, a vendor may reuse your data to train its models. Its outputs may be wrong, yet still reach customers or drive decisions. Content the model reads can manipulate it. And the vendor often relies on model providers that sit outside your contract.

Contracts often lag behind, too. Agreements signed before a vendor adopted AI rarely cover training rights, model changes, or automated decisions. As a result, you have little leverage when problems appear.

That is why mature programs treat AI as its own risk area. They also rely on references built for it, such as NIST’s AI 600-1 Generative AI Profile, which lists the risks generative AI creates or makes worse.

How can you tell when a vendor has become an AI vendor?

The first challenge is knowing which vendors to review. Fortunately, the signs are usually easy to spot:

• Release notes that announce AI assistants, copilots, or agents

• Updated privacy terms that mention model training

• Subprocessor notices that name AI model providers

• New AI add-on pricing tiers

Any of these signs should trigger an AI review right away, even if the vendor’s annual assessment is months off. Tools that never went through procurement need a different approach, which we cover in our guide to finding shadow AI.

What questions should you ask an AI vendor?

Use these questions as the core of your AI vendor risk assessment, alongside your existing questionnaire. For each one, we explain why it matters and which answer should raise a red flag.

Data use and model training

Most AI vendor risk starts here. Training rights and retention terms decide how long your data lives and where it ends up.

Model supply chain

Most AI vendors build on another company’s model. As a result, their supplier choices become your fourth-party risk.

AI-specific security

AI also creates new attack paths that traditional controls miss. The OWASP Top 10 for LLM Applications gives you a shared language for testing vendor answers.

Accuracy and human oversight

Output quality is a risk issue, not just a product issue. At best, a wrong answer creates rework. At worst, it causes customer or regulatory harm.

Governance and compliance

Finally, confirm that the vendor manages its own AI risk, with evidence an auditor would accept.

How do you build AI vendor risk assessment into your TPRM program?

Adding questions is the easy part. Making them work across a large vendor portfolio takes four changes.

1. Tier by impact: Use the full question set for vendors that handle sensitive data or shape regulated decisions, such as hiring, credit, or healthcare. For low-impact tools, a shorter set is enough. This keeps reviews in proportion and stops security from slowing down procurement.

2. Ask for evidence, not promises: Request contract terms on training and retention, a current list of model providers, and test results. Remember, a questionnaire answer is not a contract commitment.

3. Tie findings to decisions: Match each red flag to a clear outcome: approve, approve with contract changes, limit data access, or decline. This way, your AI due diligence becomes risk treatment that auditors can trace.

4. Reassess when things change: Annual reviews miss the moment risk changes most, which is when a vendor adds AI features or switches model providers. The Interagency Guidance on Third-Party Relationships already treats TPRM as a full lifecycle that includes ongoing monitoring. AI simply makes that stage more important.

Scaling AI Vendor Due Diligence with Zania

The real limit is capacity. As more vendors add AI, few TPRM teams can run deeper reviews across hundreds of vendors by hand. Zania’s autonomous TPRM agents handle risk tiering, collect and check vendor evidence, manage follow-ups, and keep monitoring vendors after approval. They also follow your team’s own methodology.

Every finding links back to its sources and reasoning. As a result, analysts can verify conclusions quickly, and your team stays accountable for every decision. To estimate the impact on your program, try the Zania ROI calculator.

The bottom line

AI has changed the risk of vendors you already approved. However, leading programs will not build a separate process to manage it. Instead, they will sharpen their AI vendor risk assessment, demand contract-level evidence, and reassess when vendors change, not just once a year. Start this quarter with the vendors that hold the most data and shape the most decisions. To evaluate AI inside your own GRC stack, see our AI GRC platform buyer’s guide.

To see how AI agents can extend AI vendor due diligence across your portfolio, book a demo with Zania.