Topic starter
06/11/2026 6:56 pm
TL;DR: Buyers now want six kinds of evidence, not verbal assurances, before they accept an agent can be governed, according to Skyrelis' analysis of AI agent security reviews. Practitioners need records they can open, replay and hand to auditors when behaviour is challenged.
Editorial analysis by NHI Mgmt Group, based on content published by Skyrelis: “What Your Buyer's Security Questionnaire Is Actually Asking About Your Agents”.
Key questions
Q: Why do customer AI agents complicate tenant isolation?
A: Customer agents often operate on behalf of a user while sharing infrastructure across many customers.
Q: How should teams control AI agent access to downstream tools?
A: Teams should treat agent access as a bounded runtime grant, not a generic application permission.
Q: Where should AI agent audit logs be stored for security review?
A: Store them at the control point that already sees the request, the identity, and the policy decision, such as a gateway or central log sink.
Practitioner guidance
- Build action-level audit records Log each tool call with agent version, user or tenant context, parameters, touched data, decision outcome and any approval step.
- Separate tenant policy enforcement Evaluate every agent action in tenant context and keep logs separable so one customer's rules never influence another's behaviour.
- Inventory reachable tools and data sources Maintain a current list of every API, workflow and dataset the agent can reach, with default-deny access until explicitly granted.
Bottom line: AI agent questionnaires are now measuring whether behaviour is observable, attributable and bounded, not whether the vendor says the system is secure.
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06/11/2026 6:56 pm
Evidence is becoming the control plane for AI agent assurance. Buyers are no longer satisfied with a statement that an agent is secure or well governed. They want artifacts they can open, replay and show to their own auditors, because that is what converts an abstract trust claim into a defensible control. The practical shift is toward runtime records, approval trails and scoped policy evidence, not narrative assurances.
A few things that frame the scale:
- Gartner predicts that more than 50% of successful cyberattacks against AI agents through 2029 will exploit access control weaknesses.
A question worth separating out:
Q: What should high-risk AI agent actions require before execution?
A: High-risk AI agent actions should require rule-triggered approval from a human approver before execution, especially for money movement, privilege changes, data deletion, or external communication. The control should enforce a clear policy, log the approving identity, action scope, and timestamp, and block the agent until approval is granted or the request expires.
👉 Read our full editorial: Security questionnaires now demand evidence of agent actions