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    Best Practices Guide

    Board Questions Before Approving an AI Agent Rollout

    Before approving an AI agent rollout, directors should ask management for evidence in eight areas: scope and risk appetite, accountability, agent identity and permissions, runtime enforcement, auditability, third-party and tool dependencies, incident response, and ongoing monitoring. A ready organization gives specific, tested, owned answers. Unnamed owners, shared credentials, policies that exist only on paper, and missing logs are gaps that justify delaying approval.

    Why agent rollouts call for different board questions

    Approving an agent rollout means deciding which actions the agent may take, who answers for it, and how the board will know it is operating within limits. The questions in this guide ask management for evidence on each point, rather than for assurances.

    The areas below follow the order in which a board typically needs to be satisfied: what the agent is permitted to do, how access is controlled and enforced, and how the organization will see and respond to what happens after launch.

    Scope, risk appetite, and accountability

    Scope and risk appetite

    Ask which actions the agent may take, and what limits the board accepts.

    Evidence to request: documented permitted and prohibited agent actions that match the board's risk appetite.

    Accountability

    Ask who owns the rollout and how that person reports to the board.

    Evidence to request: a single accountable owner, named, with a reporting line to the board.

    Agent identity, permissions, and runtime enforcement

    Identity and permissions

    Each agent should be identifiable and hold only the access it needs.

    Evidence to request: a distinct identity for each agent and a reviewed least-privilege permission set.

    Runtime enforcement

    Policies should be applied to tool calls as they happen, not only documented.

    Evidence to request: enforcement of tool calls and external connections at runtime, backed by test evidence.

    Auditability, dependencies, incident response, and monitoring

    Auditability

    Actions should be reconstructable from reliable records.

    Evidence to request: audit logs that allow a sample agent action to be reconstructed end to end.

    Third-party and tool dependencies

    Agents act through tools and external connections, so ask how those connections are controlled. Tool calls and external connections should be enforced at runtime, with test evidence.

    Incident response and ongoing monitoring

    Escalation paths and metrics should continue after approval.

    Evidence to request: incident escalation and ongoing board reporting defined before launch.

    Reading management's answers

    Specific, tested, owned answers signal readiness. The gaps below justify delaying approval until they are closed.

    Ready answers and gaps that justify delay
    AreaReady answerGap that justifies delay
    AccountabilityA single accountable owner is named and has a reporting line to the board.Unnamed owners.
    Identity and permissionsEach agent has its own identity and a reviewed least-privilege permission set.Shared credentials.
    Runtime enforcementTool calls and external connections are enforced at runtime, with test evidence.Policies that exist only on paper.
    AuditabilityAudit logs allow a sample agent action to be reconstructed end to end.Missing logs.

    Conditions to confirm before the board approves

    • Permitted and prohibited agent actions are documented and match the board's risk appetite.
    • A single accountable owner is named and has a reporting line to the board.
    • Each agent has its own identity and a reviewed least-privilege permission set.
    • Tool calls and external connections are enforced at runtime, with test evidence.
    • Audit logs allow a sample agent action to be reconstructed end to end.
    • Incident escalation and ongoing board reporting are defined before launch.

    Turn board questions into verifiable controls

    Trussed AI provides runtime governance and security for enterprise AI agents, including agent identity, permissions, tool approval workflows, and audit logging. Talk to an expert about how these controls can support the evidence your board asks for.

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