Template

Enterprise AI Vendor Risk Assessment Questionnaire: Free Template

An enterprise AI vendor risk assessment questionnaire should evaluate more than standard SaaS security. It should test whether the vendor can prove AI governance, secure software practices, data-use controls, model and agent permissions, runtime policy enforcement, tool-call governance, auditability, incident response, and compliance readiness. For AI agents, the questionnaire should specifically validate least-privilege access, human approval for sensitive actions, prompt-injection resilience, and traceable logs of prompts, outputs, tool calls, policy decisions, approvals, denials, and administrative changes.

Practical template for procurement, deployment, or production approval
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An enterprise AI vendor risk assessment questionnaire should evaluate more than standard SaaS security. It should test whether the vendor can prove AI governance, secure software practices, data-use controls, model and agent permissions, runtime policy enforcement, tool-call governance, auditability, incident response, and compliance readiness. For AI agents, the questionnaire should specifically validate least-privilege access, human approval for sensitive actions, prompt-injection resilience, and traceable logs of prompts, outputs, tool calls, policy decisions, approvals, denials, and administrative changes.

How to use this questionnaire

Use this enterprise AI vendor risk assessment questionnaire before procurement, deployment, or production approval. The goal is to move beyond a standard SaaS security review and ask for evidence that the vendor can govern, secure, monitor, and control AI behavior in production.

The questionnaire is most useful when it is treated as an evidence request, not only as a written response form. For each domain, ask the vendor to provide policies, architectural materials, control descriptions, logs, test reports, change records, or other records that show how the controls work.

Assessment focus

For AI agents, pay particular attention to least-privilege access, human approval for sensitive actions, prompt-injection resilience, and traceable records of prompts, outputs, tool calls, policy decisions, approvals, denials, and administrative changes.

Adjust the questionnaire by vendor type

Different AI vendors create different risk profiles. A vendor that provides an embedded AI feature, a model interface, an autonomous agent, or a connected workflow may require different levels of review. The same core domains still apply, but the depth of evidence should match the vendor’s access to data, systems, tools, and actions.

When a product includes agentic behavior, the review should include runtime behavior, tool access, autonomy limits, approval workflows, and the customer’s ability to disable agents, connectors, tools, or workflows during an incident.

Assessment areas for enterprise AI vendors

These areas summarize the minimum scope for an enterprise AI vendor assessment. They are not a replacement for a security review, they extend it to cover AI-specific governance, runtime, and agentic controls.

Governance

Ownership, policies, risk assessment, AI inventory, and control review.

Data handling

Prompts, files, outputs, embeddings, metadata, logs, retention, and training use.

Agent access

Identity, permissions, tool calls, secrets, APIs, plugins, and autonomy limits.

Runtime controls

Policy enforcement before model calls, after responses, and before sensitive actions.

Evidence

Architecture diagrams, audit logs, test reports, security policies, and change records.

Free template: core questionnaire domains

The questionnaire should test the vendor’s ability to prove control coverage across the domains that matter for enterprise AI risk.

  • AI governance, including ownership, policies, risk assessment, AI inventory, and control review.
  • Secure software practices for the AI product, connected services, agent workflows, and administrative functions.
  • Data-use controls for prompts, files, outputs, embeddings, metadata, logs, retention, and training use.
  • Model and agent permissions, including identity, least-privilege access, secrets, APIs, plugins, connectors, and tool access.
  • Runtime policy enforcement before model calls, after model responses, and before sensitive actions.
  • Tool-call governance, including explicit permissions, input constraints, approval rules, and logging.
  • Auditability for prompts, outputs, tool calls, policy decisions, approvals, denials, and administrative changes.
  • Incident response, including customer authority to disable agents, connectors, tools, or workflows during an incident.
  • Compliance readiness, including the evidence needed to support procurement, deployment, or production approval.

Questions that identify unacceptable deployment risk

The strongest vendor responses should be specific, evidence-backed, and aligned to how the product actually behaves at runtime. Unacceptable deployment risk often appears when a vendor cannot explain how access is limited, how sensitive actions are approved, how policy decisions are logged, or how customers can intervene during an incident.

Risk area Assessment question Evidence to request
Governance Can the vendor prove ownership, policies, risk assessment, AI inventory, and control review? Policies, risk assessments, inventory records, and control review materials.
Data handling How are prompts, files, outputs, embeddings, metadata, logs, retention, and training use controlled? Data-flow materials, retention policies, training-use controls, and logging documentation.
Agent access How are identity, permissions, tool calls, secrets, APIs, plugins, and autonomy limits governed? Permission models, connector documentation, tool access controls, and autonomy-limit descriptions.
Runtime controls Where is policy enforced before model calls, after responses, and before sensitive actions? Runtime policy descriptions, approval workflow evidence, and control test reports.
Evidence Can the vendor provide architecture diagrams, audit logs, test reports, security policies, and change records? Architecture diagrams, audit logs, testing evidence, security policies, and change records.

Agentic AI risk areas that generic SaaS questionnaires miss

Generic SaaS questionnaires usually focus on application security, infrastructure, identity, data protection, and compliance processes. Agentic AI also requires questions about runtime behavior, model interaction, tool calls, autonomy, approvals, and customer control during incidents.

  1. 1

    Prompt injection

    Ask how the vendor detects or mitigates attempts to override instructions, extract data, or manipulate tool use.

  2. 2

    Tool-call control

    Ask whether each tool has explicit permissions, input constraints, approval rules, and logging.

  3. 3

    Data boundary enforcement

    Ask how the product prevents cross-tenant, cross-role, or unauthorized retrieval exposure.

  4. 4

    Autonomy limits

    Ask which actions the agent can take without approval and how those limits are enforced.

  5. 5

    Change control

    Ask how model updates, prompt-template changes, connector changes, and policy changes are tested and communicated.

  6. 6

    Kill-switch authority

    Ask how customers can disable agents, connectors, tools, or workflows during an incident.

Where Trussed AI fits

This questionnaire is designed to keep runtime AI risk in scope when evaluating vendors. It helps teams assess governance, security, data handling, agent permissions, tool-call controls, audit evidence, and deployment readiness before approving enterprise AI vendors.

Use the responses to identify whether the vendor can support traceability for prompts, outputs, tool calls, policy decisions, approvals, denials, and administrative changes. For agentic systems, also confirm that sensitive actions can require human approval and that customers have authority to disable agents, connectors, tools, or workflows during an incident.

Evaluate AI vendors with runtime risk in scope

Use this questionnaire to assess governance, security, data handling, agent permissions, tool-call controls, audit evidence, and deployment readiness before approving enterprise AI vendors.

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