Who should own the risk when an LLM misuses delegated access?

Why This Matters for Security Teams

When an LLM is allowed to act on behalf of people or services, the risk is not theoretical model output. It becomes access misuse, data exposure, and unauthorized actions executed with valid identities. That means ownership must follow the same governance model used for any delegated privilege: business approval, identity control, and operational response. Current guidance from the NIST AI Risk Management Framework is clear that AI risk is a lifecycle accountability problem, not just a prompt-safety issue.

The common mistake is treating the LLM as a separate actor with its own exception process. In practice, the model usually sits inside an application that already has service accounts, API keys, tokens, or delegated OAuth grants. If those identities can reach sensitive systems, then the misuse path is an access governance failure as much as an AI failure. Security teams should therefore assign ownership across the team that approved access, the identity owners who can constrain it, and the incident responders who can revoke it quickly. In practice, many security teams encounter this only after the model has already performed an irreversible action, rather than through intentional privilege design.

How It Works in Practice

Operationally, ownership should be mapped to the control point where access was granted and where it can be removed. The application team typically requests the delegated access, IAM or platform security defines the entitlement boundaries, and the AI governance function validates the intended use case, logging, and human oversight expectations. Incident response then owns the emergency revocation path. That division matches the way non-human access is handled in OWASP Non-Human Identity Top 10 guidance, where secret hygiene, token scope, and lifecycle control are central concerns.

For practical implementation, teams should insist on the following:

• Each delegated identity is traceable to a business owner and a technical owner.
• Scopes are narrowly bounded, with separate identities for different tools or environments.
• Logs capture the model action, the delegated identity used, and the downstream system touched.
• Revocation can be performed without waiting for a model or product redeploy.
• High-risk actions require step-up approval or human confirmation.

Where agentic behavior is involved, the question is no longer only whether the LLM can answer safely, but whether it can complete a tool call safely. The OWASP Agentic AI Top 10 and the NIST AI 600-1 Generative AI Profile both reinforce the need for governance over tool use, output validation, and human accountability. These controls tend to break down when the LLM is given broad service-account access in legacy environments because entitlement scope, logging, and revocation are usually fragmented across teams.

Common Variations and Edge Cases

Tighter privilege often increases integration and review overhead, requiring organisations to balance speed of automation against the risk of uncontrolled delegated access. That tradeoff becomes sharper when the model supports customer-facing workflows, production infrastructure, or regulated data. Best practice is evolving, but there is no universal standard for making an LLM the formal owner of its own actions, so the accountable party remains the human and organisational control plane.

In low-risk environments, a read-only model with tightly limited retrieval access may sit under the application owner’s control, while in higher-risk settings the identity owner and security function should retain direct authority over secrets, tokens, and session revocation. Where the model can chain actions across systems, the risk profile starts to resemble non-human privilege abuse rather than simple content generation, which is why alignment with the MITRE ATLAS adversarial AI threat matrix and the NIST Cybersecurity Framework 2.0 is useful for mapping detection, response, and governance responsibilities. The edge case that most often surprises teams is a “helpful” agent operating correctly from a technical perspective while still violating business intent because its delegated access was never constrained to the exact task.

Related resources from NHI Mgmt Group

• When does JIT access create more risk than it reduces?
• How do application risk programmes affect identity and access governance?
• Who should own findings when application issues involve secrets or access paths?
• Why do non-human identities create more audit risk than human accounts?