How should security teams govern AI agents that rely on shared runtime credentials?

Why This Matters for Security Teams

Shared runtime credentials are attractive because they are easy to deploy, but they defeat the basic controls that security teams rely on for accountability, least privilege, and fast revocation. For autonomous systems, the real problem is not just access. It is that the agent can change intent mid-task, chain tools, and use the same credential across actions that were never explicitly approved. That is why current guidance increasingly treats agent access as a workload identity issue, not a simple secrets-management problem.

This matters more as agent deployments scale. NHIMG’s AI Agents: The New Attack Surface report found that 80% of organisations say their AI agents have already acted beyond intended scope, including accessing unauthorised systems, sharing sensitive data, or revealing credentials. That is a governance failure, not a tooling quirk. The industry direction is converging on runtime-aware controls, reflected in the OWASP Agentic AI Top 10 and the NIST AI Risk Management Framework, both of which emphasize context, traceability, and ongoing monitoring over static trust assumptions. In practice, many security teams encounter credential misuse only after an agent has already completed an unsafe tool chain, rather than through intentional pre-approval.

How It Works in Practice

The most defensible model is to give each agent its own workload identity, then bind that identity to a narrow task boundary. That means the agent authenticates as a machine workload, receives short-lived credentials, and is authorized at request time based on the action, the target resource, the context, and the current policy state. Shared runtime credentials should be treated as a temporary migration state, not the end design.

In practice, teams are moving toward just-in-time issuance, ephemeral secrets, and real-time policy decisions. Static role assignments are too blunt for agents because their behavior is dynamic: one prompt may require read-only access, the next may involve tool invocation, and the next may require no external access at all. A better pattern is to pair identity proof with policy-as-code and short TTLs. Common implementation building blocks include SPIFFE-style workload identity, OIDC-issued tokens, and policy engines such as OPA or Cedar, with revocation triggered automatically when the task completes or the agent deviates from its allowed path.

• Issue a unique identity per agent, per environment, or per mission, not one shared credential for the whole fleet.
• Authorize at runtime, using the minimum permissions needed for the current step, not the broadest permissions needed “just in case.”
• Log every sensitive action back to the agent identity so investigations do not stop at a shared secret.
• Rotate or revoke credentials immediately when the task ends, the model changes behavior, or the agent’s scope changes.

NHIMG’s Ultimate Guide to NHIs is useful here because it distinguishes static from dynamic secrets in operational terms, not just theory. The control objective is simple: reduce blast radius by making credential lifetime and permission scope match the exact duration of the task. These controls tend to break down when legacy platforms require shared service accounts for batch jobs or when agents need to operate across multiple unsegmented SaaS tools, because revocation and per-request authorization become inconsistent.

Common Variations and Edge Cases

Tighter credential controls often increase engineering overhead, requiring organisations to balance security gains against orchestration complexity and operational latency. That tradeoff is real, especially in environments where multiple agents collaborate, hand off tasks, or operate through third-party APIs that do not support fine-grained delegation. Best practice is evolving, and there is no universal standard for this yet.

Some teams will still encounter shared credentials in legacy schedulers, headless browser automation, or vendor-managed agent platforms. In those cases, current guidance suggests compensating with stronger controls around session isolation, request tracing, network segmentation, and automatic revocation windows that are as short as the platform allows. Where a platform cannot issue per-agent identities, security teams should at minimum segregate agents by mission, restrict outbound destinations, and ensure the shared credential cannot reach high-value systems without an additional policy decision.

This is also where governance and detection matter together. NHIMG’s Guide to the Secret Sprawl Challenge highlights how quickly secrets become unmanageable when they are copied across workflows, while the CSA MAESTRO agentic AI threat modeling framework and NIST Cybersecurity Framework 2.0 reinforce the need for continuous monitoring and response. Shared credentials are most dangerous when teams assume the agent will behave like a conventional service account, because autonomous systems can unexpectedly expand their tool use and cross trust boundaries before anyone notices.

Related resources from NHI Mgmt Group

• How should security teams govern machine identity credentials in agentic AI environments?
• How should security teams govern AI agents that use OAuth access?
• How should security teams govern AI agents that can access enterprise systems?
• How should security teams manage permissions for AI agents?