AI agents and machine identities expand the number of runtime actors that need credentials, which increases the chance of duplicated, overprivileged, or stale secrets. As access patterns spread across tools and pipelines, organisations need lifecycle visibility, ephemeral access where possible, and strong governance to avoid treating every machine actor like a permanent trust relationship.
Why This Matters for Security Teams
secrets sprawl becomes more dangerous when the number of non-human actors grows faster than the ability to govern them. AI agents, service accounts, CI/CD jobs, and platform integrations all need authentication material, but they rarely fit neatly into human-centric access reviews. The result is duplicated keys, long-lived tokens, and credentials embedded in places that are hard to inventory or revoke. This is exactly the pattern highlighted in The State of Secrets Sprawl 2025, where 38% of incidents in collaboration and project management tools were classified as highly critical or urgent.
For security teams, the risk is not just exposure. It is the operational drag of managing too many trust relationships across too many runtime contexts. A secret issued to one pipeline often gets copied into another, then reused by a bot, then left behind after the workload changes. Guidance from OWASP Non-Human Identity Top 10 and the NIST AI Risk Management Framework both point toward stronger identity governance, but the practical challenge is that machine access tends to multiply faster than review processes can keep up. In practice, many security teams discover secrets sprawl only after a token has already been copied into a chat tool, build log, or agent workflow.
How It Works in Practice
AI agents increase secrets sprawl because they operate dynamically. A human user may sign in once and reuse the same role patterns, but an agent may call multiple tools, chain actions, delegate subtasks, and request new permissions during execution. That means every step can introduce another credential, another token exchange, or another integration secret. The safer model is to treat the agent’s workload identity as the primary trust anchor, then issue short-lived access only when a task requires it. That approach is consistent with the direction of CSA MAESTRO agentic AI threat modeling framework and the NIST AI Risk Management Framework, both of which emphasise runtime context and governance rather than static trust.
Practitioners usually need four controls working together:
- Workload identity for the agent or service, so the system proves what it is before it gets anything sensitive.
- Just-in-time credential issuance, so secrets exist for the shortest practical time and are revoked after the task completes.
- Policy evaluation at request time, so access is based on current context instead of a pre-baked role that may outlive the workflow.
- Central inventory and rotation, so teams can see where secrets are stored, duplicated, or leaked.
NHIMG research on the Ultimate Guide to NHIs — 2025 Outlook and Predictions and incidents like CoPhish OAuth Token Theft via Copilot Studio show why embedded tokens and over-scoped integrations are such a recurring failure mode. These controls tend to break down in autonomous pipelines with shared build runners and broad third-party tool access because secrets are copied faster than they can be rotated.
Common Variations and Edge Cases
Tighter secrets controls often increase operational overhead, so organisations have to balance speed against containment. That tradeoff becomes sharper in agentic environments, where a workflow may need temporary access to code repositories, ticketing systems, cloud APIs, and message queues within a single run. Current guidance suggests using short-lived credentials wherever possible, but there is no universal standard for every agent architecture yet. Some environments can adopt ephemeral token exchange cleanly, while others still rely on static secrets for legacy SaaS integrations or batch jobs.
Edge cases matter. A single-purpose automation account with one fixed API endpoint is not the same as a multi-agent orchestration layer that can self-route tasks. The latter creates more opportunities for secret copying, prompt leakage, and indirect exposure through logs or chat transcripts. This is why the OWASP Agentic AI Top 10 and OWASP NHI Top 10 both matter here: they frame secrets exposure as a lifecycle problem, not just a storage problem. The practical takeaway is to minimise standing secrets, separate agent permissions by task, and assume that any secret accessible to one autonomous workflow may eventually be reused somewhere else.
