Security teams should replace hardcoded secrets with temporary, secretless authentication and scope access to the minimum required task and time window. For AI agents, that means using federated identity, short-lived tokens, and zero standing privilege so credentials are not reused or left exposed across AWS, Azure, GCP, on-prem, or Kubernetes. This reduces blast radius and aligns machine access with Zero Trust principles.
Why This Matters for Security Teams
Hardcoded secrets are especially dangerous when the workload is an AI agent because the agent is not a fixed integration with one predictable path. It can chain tools, branch into new tasks, and touch cloud APIs, Kubernetes resources, storage, and SaaS in ways that static IAM was never designed to anticipate. Guidance from the OWASP Non-Human Identity Top 10 and the OWASP Agentic AI Top 10 both point to the same operational issue: standing credentials expand blast radius far beyond the original task.
NHIMG research on secrets exposure shows why this is not a theoretical concern. In The State of Secrets in AppSec, GitGuardian and CyberArk reported that the average time to remediate a leaked secret is 27 days, even though most organisations believe their controls are strong. That gap matters more in cloud and Kubernetes environments, where a single reused token can unlock service accounts, registries, deployment pipelines, or cluster-level permissions. In practice, many security teams discover this only after an agent has already reused a secret across environments and widened access unintentionally.
How It Works in Practice
The practical fix is to replace embedded credentials with workload identity plus just-in-time authorisation. For cloud workloads, that usually means the agent authenticates as a workload, not as a person or a shared service account. For Kubernetes, that means using pod identity, federated tokens, or OIDC-backed trust so the agent proves what it is at runtime and receives only the privileges needed for that task. The NIST AI Risk Management Framework is useful here because it reinforces governance, measurement, and ongoing control validation instead of one-time provisioning.
In a secure design, the agent should never read long-lived API keys from a config file, environment variable, or image layer. Instead, the control plane issues short-lived credentials on demand, scoped to a specific action, namespace, account, or resource. That can include:
- federated identity from cloud identity providers instead of shared static secrets
- ephemeral tokens with short TTLs and automatic revocation on task completion
- policy evaluation at request time, not just at deployment time
- distinct identities for different tools, clusters, and environments
- secretless service-to-service authentication where possible
This approach aligns with the pattern NHIMG highlights in Ultimate Guide to NHIs — 2025 Outlook and Predictions, where machine identities become the real enforcement point for modern infrastructure. It also fits Kubernetes reality better than vaulting a shared token into a pod spec, because agents often scale horizontally and are rescheduled frequently. These controls tend to break down when teams rely on image-baked secrets or sidecar injection in clusters with weak namespace isolation, because the credential can still be copied, replayed, or exposed through logs and exec access.
Common Variations and Edge Cases
Tighter secret controls often increase integration complexity, requiring organisations to balance speed of agent delivery against token issuance, policy tuning, and observability overhead. Best practice is evolving, and there is no universal standard for every cloud and Kubernetes combination yet. Some environments can adopt full secretless patterns quickly, while others must transition in stages.
In multi-cloud or hybrid setups, the main edge case is identity fragmentation. Different platforms may issue different token formats, enforce different trust boundaries, or support different revocation timing. The operational answer is to standardise on workload identity where possible and to map every agent to a dedicated trust domain. For broader agentic risk context, NHIMG’s OWASP NHI Top 10 and the CSA MAESTRO agentic AI threat modeling framework both reinforce that identity sprawl and uncontrolled tool access are central design risks, not secondary hygiene issues.
Another common exception is legacy tooling that still requires static credentials. In those cases, the safer path is to isolate the secret, rotate it aggressively, wrap it in policy checks, and limit it to the smallest possible scope while engineering toward replacement. The more autonomous the agent, the less acceptable reusable secrets become.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10, OWASP Agentic AI Top 10 and CSA MAESTRO address the attack and risk surface, while NIST AI RMF and NIST Zero Trust (SP 800-207) set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-03 | Directly addresses secret sprawl and hardcoded credentials for machine identities. |
| OWASP Agentic AI Top 10 | A2 | Agentic systems need runtime controls because behaviour is dynamic and tool-driven. |
| CSA MAESTRO | TRUST-01 | MAESTRO focuses on trust boundaries and identity for autonomous agent workflows. |
| NIST AI RMF | AI RMF governs risk, measurement, and ongoing control validation for AI systems. | |
| NIST Zero Trust (SP 800-207) | PR.AC-4 | Zero Trust requires least privilege and continuous verification for workloads. |
Replace embedded secrets with short-lived NHI credentials and rotate or revoke any standing access.
Related resources from NHI Mgmt Group
- How should security teams secure AI agents in private cloud and hybrid environments without weakening control boundaries?
- What is the difference between managed identities and hardcoded secrets for AI agents?
- How should security teams govern machine identity credentials in agentic AI environments?
- How should security teams manage permissions for AI agents?
