How should organizations manage credentials for AI agents?

Why This Matters for Security Teams

AI agents do not behave like service accounts with a fixed purpose. They can chain tools, change tactics mid-task, and request new access based on goals that were not fully known at design time. That is why static IAM patterns and long-lived secrets are a poor fit. Current guidance increasingly points to dynamic credentials, workload identity, and runtime authorisation, as reflected in the OWASP NHI Top 10 and the NIST AI Risk Management Framework. The security issue is not just credential theft, but credential misuse by an autonomous system that can act faster than human review.

This matters because the blast radius is often larger than teams expect. When an agent receives broad API rights, those rights can be reused across workflows, data stores, and downstream tools. That turns one compromised token into repeated, machine-speed access. NHIMG research on the Ultimate Guide to NHIs — Static vs Dynamic Secrets shows why static secrets remain a structural weakness for non-human workloads. In practice, many security teams encounter over-privileged agent access only after the agent has already exposed data or executed an unintended action.

How It Works in Practice

The practical answer is to treat each agent as an ephemeral workload, not as a human user with a persistent identity. Start with workload identity, then issue short-lived credentials only when the agent is actively performing an approved task. That can be implemented with OIDC-backed token exchange, SPIFFE/SPIRE-style workload identity, or a secret broker such as the MCP Secret Wrapper. The goal is to prove what the agent is, then grant only the minimum access needed for the current intent.

For autonomous systems, intent-based authorisation is more appropriate than static role assignment. A role says what a principal may do in general; an intent-based decision says whether the agent should be allowed to do this specific action for this specific task, with this context, right now. Policy engines can evaluate factors such as task scope, data sensitivity, tool chain, environment, and completion status. That is closer to the direction described in OWASP Agentic AI Top 10 and OWASP Non-Human Identity Top 10 guidance.

• Issue credentials per task, not per application lifecycle.
• Set tight TTLs and revoke on task completion or anomaly detection.
• Bind secrets to workload identity so reuse outside the agent runtime fails.
• Use policy-as-code for runtime checks instead of pre-approved blanket permissions.
• Separate read, write, and escalation paths so one tool cannot unlock everything else.

NHIMG’s analysis in Analysis of Claude Code Security reinforces a simple pattern: secure agent execution is strongest when credentials are fetched just in time and vanish as soon as the work is done. These controls tend to break down when the agent runs across loosely governed toolchains, because a single delegated token can be copied, replayed, or chained into other services before revocation occurs.

Common Variations and Edge Cases

Tighter credential controls often increase operational overhead, requiring organisations to balance security gains against developer friction and workflow latency. There is no universal standard for this yet, especially in multi-agent environments where one agent calls another agent on the fly. Best practice is evolving, but the direction is clear: dynamic secrets, context-aware policy, and strong workload identity are safer than standing privileges.

Edge cases matter. Some teams still need longer-lived machine credentials for batch jobs, legacy integrations, or systems that cannot support token exchange. In those cases, the safest approach is to isolate the legacy path, constrain it with zero standing privilege, and monitor for unusual tool access. The Moltbook AI agent keys breach is a reminder that exposed or overly durable agent credentials become high-value targets quickly. External threat research from Anthropic, first AI-orchestrated cyber espionage campaign report also shows how fast adversaries adapt when agent access is reachable.

For most organisations, the right question is not whether an AI agent should have a credential, but whether that credential should exist long enough to be stolen or misused. That is the practical line between governance and exposure.

Related resources from NHI Mgmt Group

• How can organizations effectively manage access delegation for AI agents?
• How should organizations manage the identity risks associated with AI agents?
• When should organizations prioritize the detection of shadow AI agents?
• Why do AI agents create more risk when they reuse existing credentials?