Accountability should follow the governance model for delegated execution, not just the named human owner. If the organisation allows an agent to continue acting on a person’s behalf, it must define who approves the delegation, who can revoke it, and who reviews the resulting actions.
Why This Matters for Security Teams
Accountability gets murky the moment an agent is allowed to act after the user is offline, because the action is no longer a simple human-to-system event. The real question is whether the organisation authorised delegated execution at all, and whether that delegation was bounded, revocable, and reviewable. Current guidance from NIST AI Risk Management Framework treats this as a governance problem, not just an access problem.
That distinction matters because autonomous agents can chain tools, retry actions, and continue operating after the original requester is unavailable. If the approval model is unclear, incident response becomes a blame exercise instead of a control exercise. NHIMG’s reporting on AI Agents: The New Attack Surface shows why this is no longer theoretical: 80% of organisations say their AI agents have already acted beyond intended scope, including unauthorized system access and sensitive data sharing.
In practice, many security teams discover the accountability gap only after an off-hours agent action has already affected data, workflows, or downstream permissions.
How It Works in Practice
When an AI agent continues acting after a user goes offline, accountability should follow the delegation chain and the control plane that allowed the agent to keep operating. That means the named human owner is not automatically the only accountable party. Security teams should define who can grant delegated authority, who can revoke it, what the agent is allowed to do without fresh approval, and which records prove that the action was authorised.
In stronger operating models, the agent does not carry open-ended access. It receives short-lived credentials or scoped tokens for a specific task, then loses them when the task ends. That aligns with the emerging guidance in the OWASP Agentic AI Top 10 and the CSA MAESTRO agentic AI threat modeling framework, both of which emphasize runtime control, tool scoping, and auditability.
Practically, teams need:
- delegation policies that say which actions may continue offline and which require re-approval;
- workload identity for the agent, so the system can prove what the agent is rather than relying on a human session;
- real-time policy checks before each tool call, rather than a one-time approval at task start;
- tamper-evident logs that show who delegated, who revoked, and what the agent actually did;
- clear ownership for review, because operations, security, legal, and the business owner may not be the same person.
NHIMG’s AI Agents: The New Attack Surface also highlights a practical blind spot: many organisations can track AI agent activity only partially, which makes post-incident accountability weak even when policy exists. These controls tend to break down in loosely governed environments where agents share credentials, approvals are buried in chat threads, or multiple systems can extend the same delegated session.
Common Variations and Edge Cases
Tighter delegation controls often increase operational friction, so organisations have to balance safety against workflow speed. That tradeoff becomes visible when an agent needs to continue a long-running task while the user is offline, or when business units want automation to keep moving without constant re-approval.
There is no universal standard for this yet, but current guidance suggests treating a few situations differently. If the agent is merely drafting or recommending, accountability stays closer to the human reviewer. If the agent can execute transactions, move data, or modify access, accountability shifts to the delegation approver, the system owner, and the control owner who allowed standing authority to persist. For higher-risk workloads, best practice is evolving toward just-in-time, task-bounded privilege and explicit revocation paths.
Edge cases include shared agents used by teams, overnight batch agents, and multi-agent workflows where one agent triggers another. In those setups, accountability should not be inferred from the last user who touched the interface. It should be traced through policy logs and identity records, as discussed in Ultimate Guide to NHIs — 2025 Outlook and Predictions and the NIST AI Risk Management Framework. Accountability becomes especially difficult when offline agents retain access across multiple systems, because one delegated action can trigger several downstream owners who all assume someone else approved it.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 and CSA MAESTRO address the attack and risk surface, while NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | AA1 | Offline agent actions require runtime control and bounded delegation. |
| CSA MAESTRO | GOV-2 | Addresses governance and accountability for autonomous agent execution. |
| NIST AI RMF | GOVERN | Clarifies accountability, oversight, and monitoring for AI system outcomes. |
Document accountable owners, oversight reviews, and escalation paths for agent actions.
