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17/08/2026 4:39 pm
TL;DR: The most persistent AI risks still concentrate around prompt control, sensitive data exposure, supply-chain access, and poisoned inputs, according to Akeyless’ review of the OWASP Top 10 for LLM Applications 2025, which are all governance problems as much as model problems. The practical lesson is that AI security fails fastest where secrets, privilege, and auditability remain loosely governed.
NHIMG editorial — based on content published by Akeyless: OWASP LLM risks and how Akeyless addresses them
Questions worth separating out
Q: What breaks when AI prompts can be changed without privileged access controls?
A: When prompt editing is not controlled, attackers or insiders can steer the model into revealing information, calling tools, or bypassing intended guardrails.
Q: Why do LLMs complicate secrets and identity governance?
A: LLMs complicate governance because they consume data through tools, memory, and retrieval paths that behave like privileged interfaces.
Q: What do security teams get wrong about AI agent identity governance?
A: They often assume human IAM patterns can be reused with minor adjustments.
Practitioner guidance
- Separate prompt administration from prompt consumption Restrict who can edit system prompts, retrieval instructions, and tool policies.
- Replace persistent AI integration secrets with ephemeral credentials Use just-in-time credentials for model connectors, retrieval services, and third-party integrations.
- Use workload identity for AI service-to-service calls Adopt workload identity patterns such as SPIFFE and SPIRE where AI services authenticate to each other.
What's in the full article
Akeyless' full blog covers the operational detail this post intentionally leaves for the source:
- Concrete RBAC and ABAC policy examples for controlling who may change AI prompts and related permissions.
- Details on zero-knowledge secret handling and end-to-end encryption for model-adjacent credentials.
- Implementation context for JIT secrets and SPIFFE/SPIRE integrations in AI pipelines.
- Examples of how audit trails are used to detect prompt tampering and unauthorized access patterns.
👉 Read Akeyless' analysis of OWASP LLM risks and secrets governance →
OWASP LLM risks: what it means for IAM and secrets teams?
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17/08/2026 4:45 pm
AI security failures are increasingly identity failures in disguise. LLM risks such as prompt injection and data disclosure only become operationally dangerous when privileges, secrets, and audit paths are weakly governed. That means AI teams cannot treat model security as separate from IAM and PAM. The practical conclusion is that AI governance must include identity lifecycle controls, not just content safeguards.
A question worth separating out:
Q: How should security teams govern AI agents that can access enterprise systems?
A: Security teams should govern AI agents as non-human identities with explicit ownership, scoped privileges, and continuous monitoring. The control set should include inventory, task-bound credentials, audit trails, and revocation paths. If an agent can call tools or touch production systems, it belongs in the same governance model as service accounts and other machine identities.
👉 Read our full editorial: OWASP LLM risks expose where AI security still depends on identity
