Who is accountable when an AI agent uses delegated access incorrectly?

Why This Matters for Security Teams

When an AI agent uses delegated access incorrectly, the problem is usually not “the agent misbehaved” in isolation. It is a failure in delegated authority design, workload identity, and control ownership. Security teams need to trace accountability through the human sponsor, the service identity, the workflow owner, and the policy engine that approved the action. That is especially important for autonomous systems that can chain tools, reuse tokens, and act outside the original intent.

Current guidance suggests treating the agent as an execution surface rather than the accountability endpoint. NIST’s NIST AI Risk Management Framework and the OWASP Agentic AI Top 10 both reinforce that governance must cover the system, not just the model. NHIMG’s OWASP NHI Top 10 also maps the identity risks that show up when agent credentials are overprivileged or poorly scoped. In SailPoint’s AI Agents: The New Attack Surface report, 80% of organisations said their AI agents had already acted beyond intended scope, which is a reminder that this is not a theoretical edge case.

In practice, many security teams encounter accountability gaps only after an incident review reveals that no single owner can explain why the agent had that access path.

How It Works in Practice

Accountability should be assigned on three layers. First, the human or team that approved the agent’s use case remains responsible for the business decision. Second, the platform or identity team is accountable for how the agent is represented as a workload identity, how secrets are issued, and whether those secrets are short-lived. Third, the workflow owner is responsible for policy design, tool allowlisting, and runtime guardrails. That division matters because agents are goal-driven and may behave differently across tasks, even when the same identity is used.

Best practice is evolving toward intent-based authorisation, where policy decisions are made at request time using context such as task, destination, data sensitivity, and risk score. That is a better fit than static RBAC for autonomous agents, because a role alone cannot describe what the agent is trying to do right now. For implementation, teams should pair CSA MAESTRO agentic AI threat modeling framework with real-time policy evaluation, and use OWASP Non-Human Identity Top 10 to validate that NHI controls cover the credential lifecycle.

• Issue JIT credentials per task, not long-lived tokens that can be reused outside the workflow.
• Bind the agent to workload identity, not just an API key, so the system can prove what the agent is.
• Make approval logic explicit in policy-as-code and evaluate it on every sensitive action.
• Log the sponsor, workflow, policy decision, and downstream tool call so incident review can assign responsibility.

NHIMG’s AI LLM hijack breach analysis is a good reminder that once credentials are exposed or reused, attackers move quickly through the same delegated path the agent used. These controls tend to break down when agents are allowed to make unsupervised tool calls across multiple SaaS environments because the trust boundary becomes too fragmented to enforce consistently.

Common Variations and Edge Cases

Tighter delegated-access controls often increase operational overhead, requiring organisations to balance faster automation against stronger review and revocation. There is no universal standard for this yet, especially in multi-agent systems where one agent delegates to another and each step has a different owner. In those environments, accountability may need to be shared, but the decision chain still has to be documented.

One common edge case is a service account used by both humans and agents. That setup blurs responsibility and weakens forensics, so current guidance suggests separating human-admin identities from agent workload identities. Another is emergency access: if an agent has break-glass privileges, ownership must include the policy owner who allowed that override and the responder who approved the event. Finally, if the agent operates through an MCP-based tool chain, the orchestration layer may be the real control point, not the model itself. In that case, the owner of the tool gateway and the policy engine becomes critical for accountability.

For background on the broader identity problem, NHIMG’s Ultimate Guide to NHIs and the Ultimate Guide to NHIs - Key Challenges and Risks explain why delegated access becomes difficult to govern once secrets, tokens, and automation converge. In these cases, the accountability question is resolved by governance design, not by post-incident blame.

Related resources from NHI Mgmt Group

• When does AI agent access create more risk than it reduces?
• What is the difference between governing human access and governing AI agent access?
• Why is JIT access important for AI agent management?
• When does an AI agent become a privileged access problem?