Because they consume secrets programmatically and often repeatedly, which changes the risk profile. Human-centric controls like login convenience and self-service recovery do not govern runtime secret retrieval well. AI workflows need narrow scope, explicit retrieval conditions, and logs that show which non-human identity used which credential.
Why This Matters for Security Teams
AI agents and scripts do not handle secrets like human users because they retrieve and reuse them at runtime, often across multiple tools and tasks. That changes the control objective from convenient authentication to tightly governed secret distribution, scope, and auditability. Human-centric patterns such as password resets, MFA prompts, and interactive recovery do not reduce the blast radius when a workload can call APIs, chain actions, or expose tokens programmatically. Current guidance from the OWASP Agentic AI Top 10 and NHI research such as OWASP NHI Top 10 both point to the same issue: runtime identity and secret handling must match autonomous behaviour, not human login flow.
NHI Management Group’s research shows the risk is already operational, not theoretical. In the AI Agents: The New Attack Surface report from SailPoint, 80% of organisations said their AI agents have already performed actions beyond intended scope, including revealing access credentials. That is the practical reason secret handling for agents must be narrower, shorter-lived, and more observable than for people. In practice, many security teams only discover this after an agent has already reused a token in an unexpected workflow.
How It Works in Practice
The right model starts by treating the agent or script as a workload identity, not as a person with a password. That means the secret is not something the agent “knows” for long periods. Instead, the workload proves what it is, requests only the credential needed for the current task, and receives a short-lived token or scoped secret that expires quickly. This approach aligns with NIST AI Risk Management Framework principles and the implementation direction in the CSA MAESTRO agentic AI threat modeling framework.
Practically, strong secret handling for agents usually includes:
- Just-in-time issuance of secrets with short time-to-live values.
- Per-task scoping so the token cannot be reused for unrelated systems.
- Explicit retrieval conditions, such as policy checks on task type, destination, and data class.
- Central logging that records which NHI accessed which secret, when, and for what purpose.
- Automatic revocation or rotation when the task ends, fails, or escalates unexpectedly.
This differs from human controls because the human does not continuously consume secrets through code paths. For agents, the credential itself becomes part of the execution chain, which is why runtime policy matters as much as vault hygiene. Research on secrets management from The State of Secrets in AppSec also reinforces that credential sprawl and delayed remediation create lasting exposure. These controls tend to break down in long-running autonomous pipelines, where one secret is cached across many tool calls because repeated vault lookups are seen as too expensive or operationally awkward.
Common Variations and Edge Cases
Tighter secret control often increases operational overhead, requiring organisations to balance security against latency, reliability, and developer friction. That tradeoff is real, especially when scripts run in batch jobs, CI pipelines, or agentic workflows that need frequent access to the same service. Best practice is evolving here, and there is no universal standard for every workload pattern yet.
Some environments can tolerate longer-lived credentials if the execution surface is tiny and fully isolated, but that is the exception, not the norm. In multi-agent systems, shared secrets are especially risky because one compromised agent can become a bridge to others. For that reason, current guidance suggests using separate workload identities per agent or per function, rather than one broad service account. Where possible, pair this with policy-as-code and real-time authorization so a token is issued only when the request context still matches the approved task.
Edge cases also include third-party connectors and legacy tools that cannot do short-lived tokens cleanly. In those cases, the safer path is compensating controls: tighter vault access, network segmentation, and aggressive rotation. The Replit AI Tool Database Deletion incident and the CoPhish OAuth Token Theft via Copilot Studio case show how quickly misuse can spread when tokens outlive the task they were meant to support.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10, CSA MAESTRO and OWASP Non-Human Identity Top 10 address the attack and risk surface, while NIST AI RMF and NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | A1 | Agentic apps need runtime controls because autonomous use of secrets changes with each task. |
| CSA MAESTRO | GOV-2 | MAESTRO covers governance for agent behaviour and secret use across autonomous workflows. |
| NIST AI RMF | AI RMF applies to managing operational risk from autonomous systems using credentials. | |
| OWASP Non-Human Identity Top 10 | NHI-01 | Non-human identities need distinct secret handling from human accounts. |
| NIST CSF 2.0 | PR.AC-1 | Access control and identity governance are central to limiting secret exposure. |
Issue credentials per task and evaluate access at runtime before any agent tool call.
Related resources from NHI Mgmt Group
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Reviewed and updated by the NHIMG editorial team on July 9, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
