10 AI Security Questions Your Vendor Assessment Process Is Missing

Your vendor questionnaire was built for a different era. Traditional third-party controls (data handling, access controls, encryption in transit) still matter, but they don't cover what AI vendors are actually introducing into your environment: model training on your data, opaque decision-making, hallucinated outputs with real consequences, and attack surfaces that didn't exist five years ago. Most Enterprise vendor questionnaires haven't caught up, and with AI now embedded in everything from HR to legal ops to your GRC software, the gap is growing. These are the ten questions every CISO should be asking AI vendors right now.

1. Does your AI train on customer data, and is that opt-out or opt-in?

Plenty of AI platforms use customer interactions, prompts, and uploaded documents to fine-tune their models. Some disclose this in terms of service. Most don't proactively flag it. Your employees' queries, the documents they upload, and the workflows they run through an AI system can all become training material. That material can surface later in another customer's model output.

Ask for a written statement: does your model train on customer data, ever? Is this opt-out (customer must disable it) or opt-in (disabled by default)? What's the process if I want to verify?

2. Where is data processed and stored, and can it cross jurisdictions?

Most AI systems route queries to third-party LLMs like OpenAI, Anthropic, Google, or others, often in the US, regardless of where your data is supposed to live. For companies with GDPR obligations, this creates a real transfer problem.

Ask: when a user submits a query or document through your platform, exactly where does that data go? Does it leave the EU? Is there a data processing agreement in place for each provider in your AI supply chain? Can you give me a data flow diagram? The same question applies to retention: how long is inference data held, and where?

3. How do you prevent my data from appearing in another customer's output?

Model contamination is an under-appreciated risk. AI systems with insufficient isolation can sometimes surface content from one customer's context in another's response, not through a traditional data breach, but through the way models surface statistical patterns across usage.

Ask vendors specifically how customer tenancy is enforced at the model inference layer, not just the application layer. Separate infrastructure is not the same as separate access controls.

4. What is the scope of your AI: what tasks can it perform?

Broad-scope AI, where an agent can write, retrieve, analyze, and act across large contexts, carries fundamentally different risk than narrow, purpose-built AI with constrained inputs and outputs. The narrower the scope, the smaller the surface area for hallucination, prompt injection, and unintended behavior.

Ask vendors to describe exactly what each AI component does and doesn't do. A vendor who can't define the limits of their AI's scope hasn't designed it with security in mind.

How this should look in practice: Complyance's HIPAA Agent reviews HIPAA control evidence. That's it. It can't access unrelated data, can't generate arbitrary text, and only outputs predefined formats. Every action stays auditable. That's what narrow scope looks like in deployment.

5. How is AI-generated output attributed and auditable?

When an AI system makes a decision, flags a finding, or generates a recommendation, you need to trace exactly what inputs produced that output. You need to defend AI-assisted decisions to regulators and auditors, and when something goes wrong at scale (and at scale, something will), you need to reconstruct exactly what happened.

Ask: is every AI action logged with its inputs, outputs, and timestamp? Can I export that log? Is it immutable? Does it integrate with my SIEM?

7. How do you handle AI model updates, and what notice do customers receive?

Behavior change is a quiet risk with AI vendors. A model update can change how the AI interprets inputs, scores risks, or surfaces findings, without any obvious UI change or notification. If model drift goes undetected in a compliance or security context, you might be relying on outputs from a fundamentally different AI than the one your team calibrated their trust against.

Ask for the change management process: how are model updates versioned? Are customers notified before updates roll out? Is there a way to freeze a model version for audit continuity?

8. What is your process when the AI gets it wrong?

AI systems confidently generate incorrect information. In compliance, risk management, or security tooling, that costs you an audit or exposes you to regulatory risk.

Ask vendors directly: has your AI produced incorrect outputs in production? How was this detected? How was it disclosed to affected customers? What's the remediation process?

A vendor who says their AI doesn't hallucinate isn't being honest. A vendor who has a documented process for identifying and disclosing errors is.

9. What access does your AI have to my broader environment?

AI systems increasingly request broad permissions: access to email, calendar, files, code repositories, and communication platforms. Each integration expands the blast radius of a compromise.

Ask for a permissions inventory: what system access does your AI request by default, and what happens if I remove specific permissions? Is the AI functional with minimal permissions, or does it require broad access to work? Least-privilege applies to AI systems the same way it applies to any other piece of infrastructure.

10. What does your AI security incident response process look like?

AI-specific incidents (a data exposure through inference, a model manipulation, or an output that caused a downstream decision) are different from traditional security incidents. Most vendors haven't built AI-specific playbooks yet.

Ask: if your AI causes a data exposure, what's your SLA for notification? Who investigates? Do you have a dedicated AI security incident classification? Is this covered under your existing SLA, or is it a separate process?

If the answer is vague, that's your answer.

Getting This Right at Scale

One vendor conversation is manageable. Managing AI security risk across your entire third-party ecosystem is a different problem. Most Enterprise organizations now have hundreds of vendors with some AI capability embedded in their stack. Reviewing them one at a time, with questionnaires built before the AI era, doesn't scale, and the gaps compound with each new vendor that lands in your stack.

The work needs to shift shape. Your team stops reading 200 questions per vendor and starts reviewing only the answers that didn't clear your bar. The same ten questions run against every vendor in your stack, every cycle, without the team growing.

Every question in the list above can be configured as a criterion in Complyance. The Vendor Questionnaire Review Agent scores every vendor response against those criteria as soon as it's submitted and flags the answers that don't clear your bar. Your team reviews the flagged findings, not the 200 questions around them, and approves the ones that should escalate. When you approve an escalation, Complyance's TPRM Agents create the risk in the register with the description and suggested treatment plan already drafted. The vendor and the risk stay bi-directionally linked. You can see every risk from the vendor profile, and trace any risk back to the vendor that surfaced it. Audit trail intact by default.

Clients have moved from three FTEs and a week per vendor review cycle to one FTE and a few hours. That's the operational shift. Not a faster questionnaire. A different shape of work.

You can ask these ten questions of one vendor. The harder question is how you ask them of every vendor, every cycle, as AI risk keeps evolving, without growing the team. That's the question Complyance is built for.