Non-human identities increase leakage risk because they often have broad machine-to-machine reach, long-lived or reused credentials, and limited human review. Once access is granted, those identities can move data through pipelines, integrations, and AI services faster than traditional governance processes can inspect.
Why This Matters for Security Teams
Non-human identities are dangerous not because they exist, but because they scale access faster than human oversight can track it. Service accounts, API keys, workload identities, and AI agents often sit inside pipelines that can read, copy, transform, and forward sensitive data without a person reviewing each action. Once one identity is over-privileged or exposed, leakage can spread through integrations, storage layers, and model-connected workflows.
That is why NHI risk is really a data-flow problem as much as an identity problem. The 2024 ESG Report: Managing Non-Human Identities from Oasis Security & ESG found that 72% of organisations have experienced or suspect a breach involving non-human identities. That scale of exposure lines up with what NHIMG has documented in The 52 NHI breaches Report and the wider patterns in Ultimate Guide to NHIs — Why NHI Security Matters Now.
In practice, many security teams discover leakage only after a secret has already been reused, a pipeline has exfiltrated data, or an AI workload has chained a benign permission into a broader disclosure event.
How It Works in Practice
Leakage risk rises when an NHI has broad reach, long-lived credentials, and too little runtime scrutiny. A single token can be embedded in code, copied into a CI job, inherited by a container, and then used by a downstream service to access records, logs, or model context. That is why secrets sprawl and over-broad entitlements are so closely tied to exposure. NHIMG’s Guide to the Secret Sprawl Challenge shows how hard it becomes to contain credentials once they are scattered across tools, teams, and environments.
Modern guidance points toward three controls working together: least privilege, short-lived credentials, and runtime policy checks. NIST’s NIST Cybersecurity Framework 2.0 reinforces identity governance and data protection as continuous functions, not one-time setup tasks. For AI-enabled workloads, Anthropic’s first AI-orchestrated cyber espionage campaign report is a reminder that autonomous systems can accelerate abuse when tool access is too permissive.
- Use JIT credentials so access exists only for the task being executed.
- Bind workload identity to the service or agent, not to a static shared secret.
- Evaluate authorisation at request time, using policy-as-code and current context.
- Segment data paths so one compromised identity cannot read, copy, and forward everything.
These controls tend to break down in legacy batch jobs and cross-team automation where one identity is reused for many systems because the ownership model is already unclear.
Common Variations and Edge Cases
Tighter controls often increase operational overhead, so organisations must balance leakage reduction against deployment friction, incident response speed, and developer workload. Current guidance suggests that this tradeoff is strongest in environments with many ephemeral jobs, external integrations, or AI agents that need tool access on demand.
One edge case is shared infrastructure. If multiple pipelines or agents reuse the same credential, the blast radius grows and attribution gets murky. Another is secret rotation without entitlement cleanup: rotation alone does not stop leakage if the old privilege path still exists. NHIMG’s 52 NHI Breaches Analysis and Top 10 NHI Issues both reinforce that exposure usually follows a chain of weak identity hygiene, not one isolated mistake.
For AI and autonomous systems, the question is also about intent. An agent can be technically authenticated and still be unsafe if it can chain tools, move laterally, or copy sensitive context into another service. NIST’s NIST Cybersecurity Framework 2.0 and the emerging AI governance model in Ultimate Guide to NHIs — Key Challenges and Risks both point toward stronger runtime oversight, but there is no universal standard for this yet.
In practice, leakage risk is highest where identity, secrets, and data controls are managed in separate silos and no single team can see the full path from credential issuance to data movement.
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 address the attack and risk surface, while NIST CSF 2.0 and NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-03 | Covers weak secret rotation and over-lifetime credentials that raise leakage risk. |
| NIST CSF 2.0 | PR.AC-4 | Least-privilege access control directly reduces how far an NHI can move data. |
| NIST AI RMF | AI RMF is relevant where autonomous agents amplify data movement and disclosure risk. |
Establish AI governance for agentic workloads and require runtime oversight for tool use and data access.
Related resources from NHI Mgmt Group
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Reviewed and updated by the NHIMG editorial team on May 26, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
