A demo can show an AI agent reading a SOC 2 report and producing a convincing assessment. The harder test comes when the report is outdated, sources disagree, or the agent loses access halfway through.
Evaluate an AI GRC platform on completed work and the evidence it leaves behind. Check whether a reviewer can verify the conclusion, permissions hold, and interrupted work can resume without lost progress or duplicate records.
Use these 12 questions with the same documents and acceptance criteria for each vendor. Start with approved or redacted material; clear the data-handling review before testing with sensitive information.
Accuracy and explainability: make the answer verifiable
A policy can describe a control without proving that it operated during the period under review. Accuracy includes recognizing what the evidence can support.
1. Can the platform show the sources and rationale behind each answer?
Follow an answer’s citation to the exact passage, document version, and reporting period. Ask how that evidence supports the conclusion. A precise reference lets your reviewer check the claim without rereading the report.
Then provide conflicting policy versions. Check whether the platform identifies the difference in scope or date. Ask for a concise decision rationale; a generated explanation alone does not prove correctness.
2. How accurate is it on our content, and how do you measure that?
Build a small evaluation set from representative policies, vendor reports, and questionnaires, including formats you use such as the Shared Assessments SIG. Have your team establish the expected conclusions before the pilot. Include cases that require a qualified answer or further evidence.
Define errors before the pilot: an unsupported conclusion, a missed material exception, a wrong document version, or an action taken without permission. Keep those categories separate from writing quality. Repeat the important cases and record the number of attempts. A system that succeeds once after several retries creates a different operating burden from one that completes the task consistently. Measure reviewer corrections and checking time alongside the result.
3. What happens when the AI is unsure?
Remove a required document or supply a report outside the period being assessed. Check whether the platform identifies the coverage gap and leaves unsupported conclusions unresolved for a named reviewer.
Ask the provider to show how its confidence scores compare with correctness on examples that were not used to tune the system. A score labeled “high confidence” is useful only if it has been checked against outcomes on relevant work. The reviewer should still see the evidence, open questions, and conditions that would change the conclusion. Treat an unanswered question as an explicit result to resolve, not a reason to fill the gap with a plausible answer.
Security and data privacy: follow the information
GRC documents describe how your organization operates and where it is exposed. Review their handling by both the application and its downstream providers.
4. Is our data used to train your AI models?
Request contractual terms on training use, the model providers involved, and relevant settings. Confirm that the commitment covers the service and account type you will buy.
Review retention separately. A no-training commitment does not tell you whether prompts, uploaded files, outputs, or abuse-monitoring logs are stored. Establish who retains each category, for how long, and how deletion works.
5. How is our data stored, isolated, and protected?
Request the data-flow diagram, encryption details, tenant-isolation design, and regions used for processing and storage. Check those regions against your requirements. Review the scope and period of the vendor’s SOC 2 Type II report and relevant penetration-test findings, including how exceptions were addressed.
Ask what happens to application records, provider logs, backups, and derived data when you delete an assessment or end the contract. Confirm the deletion timetable and available evidence.
6. How do you defend against AI-specific threats?
Agree on a safe pilot test: put instructions in a supplier document that ask the agent to ignore its task or access restricted information. Inspect its response and attempted actions.
Use the OWASP guidance on prompt injection and MITRE ATLAS to frame the review. Ask where access restrictions are enforced, which actions the agent can take, and what happens when a request is refused. Input filtering is one part of that assessment.
Governance and human oversight: test who can decide
Define which decisions your policy reserves for people. Test whether that boundary holds when work is reviewed, changed, and sent to another system.
7. Where do humans review and approve the AI’s work?
Choose a required approval, such as sending a questionnaire to a customer. Try it as a user without approval authority. Confirm that the action is blocked and an authorized reviewer can inspect the evidence before approving.
Change the answer or evidence after approval. Check whether a material change requires another review under your policy and leaves a clear decision record.
8. Will the audit trail support our review?
Request an assessment export that lets reviewers reconstruct the evidence, findings, approvals, and resulting actions. Check timestamps, identities, document versions, and failed attempts.
Share the sample with internal audit. Confirm who can alter or delete records, retention periods, and whether exports remain understandable outside the platform.
9. How does the platform support AI governance frameworks?
If governing AI across the business is in scope, ask the vendor to demonstrate the records and workflows supporting your chosen approach, such as the NIST AI Risk Management Framework or ISO/IEC 42001. Inspect how an AI use case gets an owner, an assessment, a decision, and a later review.
For mappings to ISO/IEC 27001, NIST CSF, or other requirements, examine the rationale and scope. A mapping suggests a relationship. Your team still needs to decide whether the evidence satisfies each requirement and covers the relevant period.
Integration and business value: inspect the completed work
Follow the task into the systems where people act on it. Include handoffs and corrections when assessing the time saved.
10. Does the platform complete work, or only track it?
Choose a bounded task: review a supplier’s SOC 2 report and prepare follow-up questions. Define the expected artifacts and required human review. Use documents the vendor has not prepared for the demo.
Check the resulting records and files. Anthropic’s guide to agent evaluations distinguishes what an agent says happened from the final state it produced. Apply that distinction here: a message saying an assessment is complete should correspond to a saved, reviewable assessment with the expected findings and sources.
11. How does it fit with our existing tools?
Inspect the specific reads and writes supported by each integration. For a ticketing workflow, confirm which system owns the status, how changes are synchronized, and what happens if access is revoked or a request times out.
Interrupt a connector in an agreed test environment, then retry. Inspect the reported failure, recovered progress, and downstream record. Confirm that the retry did not create duplicates.
12. How quickly will we see value, and how will we measure it?
Record your current completion time, review effort, and backlog for the workflows in scope. Count setup, document preparation, corrections, and exception handling in the pilot. Otherwise, work transferred to your team can disappear from the reported time savings.
Agree on scope, duration, owners, and acceptance criteria upfront. Compare completed work of acceptable quality, including the effort needed to verify and correct it.
Run the same evaluation for every vendor
Bring the workflow owners and security, privacy, and procurement reviewers into the evaluation early. Give each vendor the same written questions, test cases, and definition of success.
Set mandatory security, data-handling, and permission requirements as pass-or-fail gates before assigning weights. Mark a requirement “not tested” when the evidence is missing; do not quietly award partial credit. A weighted total should compare vendors that meet the gates, not compensate for a failed access boundary with a strong demo. The weights below are an example for discussion, not an industry standard.
Theme | What it covers | Suggested weight |
|---|---|---|
Accuracy and explainability | Sources, accuracy on your data, handling of uncertainty | 30% |
Security and data privacy | Model training, data protection, AI-specific threats | 30% |
Governance and human oversight | Approvals, audit trail, framework support | 20% |
Integration and business value | Real task completion, fit with your stack, time to value | 20% |
For each requirement, retain the test input, expected result, observed result, evidence link, reviewer, and unresolved issue. Label whether the evidence came from a document review, a demonstration, or your own pilot. Include successful completion, missing evidence, permission refusal, and recovery after an interruption. For a vendor-assessment example, use the AI TPRM pilot checklist. This leaves procurement with a record it can challenge, rather than a collection of vendor assurances.
Speak with customers running similar workflows. Ask what still needs manual attention and which expectations proved wrong. Those answers explain the effort behind the headline result.
Evaluate Zania on the same terms
Zania builds AI agents for security, risk, and compliance work, including security questionnaires, controls testing, and third-party risk assessments. Use the questions above to examine the workflow you intend to deploy, its evidence, and the work your reviewers still need to do.
For a vendor-assessment pilot, see our third-party risk management (TPRM) software buyer’s guide. Review Zania’s security architecture and request supporting documents through the Trust Center.
Book a demo and bring a workflow, the evidence it depends on, and the conditions it must satisfy. Ask us to show you the result and how your team would review it.
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