How to Write an AI Agent Security Questionnaire for Vendors

Why Standard SaaS Questionnaires Fall Short

Traditional vendor security questionnaires were built to assess static access controls, data handling practices, and request-response API architectures. AI agents operate differently. They can chain multiple tool calls autonomously, act on ephemeral or delegated credentials rather than fixed user identities, and produce non-deterministic outputs that complicate pre-deployment testing. OWASP's Agentic AI guidance identifies excessive agency, tool misuse, and identity or privilege abuse as risks distinct from standard LLM application concerns. A questionnaire that only asks about data encryption, access logging, and incident response history will miss the risk surfaces specific to how an agent decides what to do, and what it is permitted to touch while doing it. Enterprises evaluating AI agent vendors need a separate instrument (or a substantially modified one) that treats agent behavior and tool access as first-class evaluation targets.

Structuring the Questionnaire Around Agent-Specific Risk Surfaces

Rather than organizing questions by generic security domains, structure the questionnaire around the technical characteristics that differentiate agents from conventional software. Five categories cover most of the relevant ground: agent identity and authentication, permission scoping, tool-call governance, runtime policy enforcement, and auditability. This structure aligns with risk categories described in NIST's AI Risk Management Framework and its Generative AI Profile, as well as OWASP's LLM and Agentic AI guidance, giving evaluators a way to map vendor responses to recognized frameworks rather than relying on ad hoc criteria. Each category should include both a factual question and a request for supporting evidence, since a vendor's stated policy and its actual implementation are not always the same thing.

Why Model Context Protocol Requires Explicit Questions

The Model Context Protocol, introduced by Anthropic in late 2024, standardizes how AI models connect to external tools and data sources through a client-server architecture. Its specification includes guidance on user consent and tool annotations, but it explicitly states that implementers, not the protocol itself, are responsible for building access controls, authorization flows, and consent mechanisms. This distinction matters for questionnaire design. A vendor claiming MCP compliance has adopted a communication standard, not a guaranteed security posture. Questions should ask specifically how the vendor's MCP server authenticates and authorizes tool calls, whether it logs tool invocations with sufficient detail for audit, and whether it enforces any restrictions beyond what the base protocol requires. Because MCP centralizes tool and resource access through a server layer, that layer becomes the primary point of security evaluation regardless of which model or agent framework sits on top of it.

Runtime Enforcement and Auditability

Because agent behavior is non-deterministic, security assurance cannot rely solely on pre-deployment testing. Runtime enforcement and monitoring become the primary controls for catching unauthorized or anomalous tool-call sequences as they happen. Questionnaires should ask what mechanism detects and blocks agent actions that fall outside expected policy, and whether that enforcement happens at the model layer, the tool-execution layer, or a separate gateway. Equally important is auditability: what logs are generated for each tool invocation, including inputs, outputs, and the decision path that led to the call, and what retention period applies to those logs. NIST's Generative AI Profile and joint CISA/NSA guidance on AI data security both emphasize verifying data provenance and access controls for third-party AI integrations, which extends naturally to requiring detailed, retrievable audit trails from agent vendors rather than summary-level reporting.

Evaluating Vendor Responses

• Require architecture diagrams and policy configuration examples rather than accepting affirmative-only checkbox answers
• Ask for sample audit logs to verify the granularity and retention of tool-call records
• Map each response to an existing framework category, such as NIST AI RMF or OWASP's Agentic AI threats, to standardize comparison across vendors
• Treat the questionnaire as the start of ongoing risk monitoring, not a one-time approval, given how frequently agent tools and model versions change
• Distinguish between vendor-only controls and controls the enterprise can configure, such as tool allowlists or approval workflows

Where This Fits in Broader AI Governance

No single authoritative standard yet defines an AI agent vendor questionnaire. Enterprises are adapting existing frameworks, including NIST's AI RMF and its Generative AI Profile, OWASP's LLM and Agentic AI guidance, and MITRE ATLAS's catalog of adversarial techniques against AI systems, to fit this specific evaluation need. This means questionnaire design will continue to evolve, and enterprises should expect to revise their evaluation criteria as agentic AI threat guidance matures. What remains constant is the underlying principle: agent security depends on how identity, permissions, and tool access are enforced at runtime, not just on what a vendor's documentation claims. Trussed AI provides runtime governance for enterprise AI agents, including agent identity, least-privilege permission enforcement, tool approval workflows, and audit logging: the same control categories a well-constructed vendor questionnaire should probe.