What is the difference between delegated AI access and shared agent identities?

Why This Matters for Security Teams

Delegated AI access and shared agent identities are not just two ways to log in. They determine whether the human user still constrains what the agent can do, or whether the agent becomes the only subject the downstream application recognizes. That distinction affects least privilege, auditability, and blast radius. OWASP’s OWASP Agentic AI Top 10 and NHI research from Ultimate Guide to NHIs both point to the same operational problem: identity context collapses quickly once an autonomous system starts acting on behalf of multiple users.

In delegated access, the application can still apply user-level ceilings, step-up checks, and policy conditions tied to the human. In shared agent identities, those ceilings often disappear because the service only sees a broad agent account with standing permissions. That makes a shared agent identity easier to operate at scale, but it also makes it far easier to over-privilege a workload and much harder to explain who actually authorized a sensitive action. The difference is especially important in AI workflows because the agent may chain tools, retry requests, and move laterally in ways a human never intended.

For security teams, the practical question is not only who authenticated, but whose authority is still present at decision time. In practice, many teams discover that the user’s entitlement stopped functioning as a control only after an agent had already accessed more data than the original request would have allowed.

How It Works in Practice

Delegated access keeps the user in the authorization path. A service may receive a token that represents both the agent and the human subject, so the downstream system can evaluate the request against the user’s permissions, consent scope, and context. Shared agent identities remove that linkage. The application sees a single non-human principal, which means the agent’s standing roles, token scopes, or workload permissions become the main control surface.

That is why current guidance from OWASP Non-Human Identity Top 10 and the CSA MAESTRO agentic AI threat modeling framework increasingly favors workload identity plus per-task authorization instead of long-lived shared accounts. A practical implementation often includes:

• Token exchange or impersonation flows that preserve the human subject where the target system supports it.
• Short-lived credentials issued for a specific task, then revoked automatically when the task completes.
• Policy evaluation at request time, not only at provisioning time, using context such as data sensitivity, tool type, and agent state.
• Separate workload identities for each agent, environment, or tenant to prevent permission blending.

This aligns with the direction of the NIST AI Risk Management Framework, which emphasizes governance, measurement, and operational accountability rather than assuming identity alone is sufficient control. NHIMG’s AI LLM hijack breach analysis shows why this matters: once an attacker or misconfigured agent gets control of a broad NHI, the identity can be reused far beyond the original task scope. These controls tend to break down in legacy SaaS and internal APIs that only support coarse service accounts, because they cannot evaluate user context at request time.

Common Variations and Edge Cases

Tighter delegated-access design often increases integration complexity, requiring organisations to balance stronger containment against application compatibility. That tradeoff is real, especially where older systems only accept a single service principal or cannot consume user claims from modern token exchange flows.

Best practice is evolving, but the consensus is clear on one point: if the downstream system cannot preserve user context, the control benefit of delegation weakens quickly. In those cases, security teams should treat the agent identity as a high-risk workload identity, apply the narrowest possible scopes, and enforce OWASP NHI Top 10 guidance around secret exposure, privilege sprawl, and tool abuse.

Shared identities may still be acceptable for low-risk automation, but only when the agent is operating in a tightly bounded environment with minimal data access and strong monitoring. Delegated access is the better pattern when the business needs per-user accountability, conditional approval, or policy enforcement based on the human’s original entitlement. The edge case that breaks many deployments is multi-user orchestration: once a single agent serves multiple people, identity mixing can make audit trails ambiguous and authorization decisions inconsistent unless the architecture explicitly preserves per-request subject context.

Related resources from NHI Mgmt Group

• What is the difference between governing human access and governing AI agent access?
• What is the difference between managed identities and hardcoded secrets for AI agents?
• What is the difference between human identity governance and AI agent governance?
• What is the difference between workload identity and API keys for AI agents?