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15/08/2026 6:12 pm
TL;DR: AI agents introduce prompt injection, model inversion, and memory poisoning risks that traditional security tools cannot reliably detect, according to Obsidian Security. As enterprise deployments expand, security teams need identity-aware testing, continuous monitoring, and MLOps-integrated governance before agent behaviour outpaces control coverage.
NHIMG editorial — based on content published by Obsidian Security: Building an AI Agent Security Framework for Enterprise-Scale AI
By the numbers:
- 96% of technology professionals identify AI agents as a growing security threat, and 66% believe this risk is immediate.
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, sharing sensitive data, and revealing access credentials.
Questions 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.
Q: Why do AI agents complicate existing IAM and PAM controls?
A: AI agents complicate IAM and PAM because they often inherit delegated credentials, operate across multiple systems, and keep acting after the initial approval moment has passed.
Q: What breaks when prompt injection is not governed like an access problem?
A: The organisation may treat malicious text as a harmless message, even though it can steer an agent into exposing data or taking privileged actions.
Practitioner guidance
- Map every agent to a named owner and access scope Assign a business and technical owner to each AI agent, then document its data sources, tools, service accounts, and downstream permissions.
- Gate agent deployment through CI/CD and MLOps Require security tests, approval checks, and permission validation before an agent reaches production, and repeat those checks whenever the model, prompt set, retrieval layer, or integration tokens change.
- Separate trusted policy from untrusted content Keep system instructions, policy rules, and operational memory isolated from user-provided text and external documents so prompt injection has less opportunity to alter execution paths or tool use.
What's in the full article
Obsidian Security's full blog post covers the operational detail this post intentionally leaves for the source:
- Testing patterns for prompt injection, model inversion, and memory poisoning across real agent workflows
- Framework comparisons covering OWASP, NIST AI RMF, MITRE ATLAS, and ISO 42001 in more implementation detail
- Integration guidance for CI/CD, MLOps, and security orchestration workflows at enterprise scale
- Vendor evaluation criteria for open-source, commercial, and cloud-based AI security testing tools
👉 Read Obsidian Security's framework for securing enterprise AI agents →
AI agent security frameworks: are your controls keeping up?
Explore further
15/08/2026 6:19 pm
AI agents are becoming governed identities, not just software workloads. Once an agent can decide when to act, what tool to call, and which data to retrieve, the governance problem shifts from application security to identity security. The control question becomes who or what is allowed to speak and act on behalf of the organisation. That is why IAM, PAM, and NHI governance must converge around agents that hold credentials, access tokens, and delegated permissions.
A question worth separating out:
Q: How can organisations tell whether their AI security model is actually working?
A: They should test whether the control stack can explain who acted, what data was touched, and what purpose the action served. If those three signals cannot be correlated in one incident view, the model is likely monitoring access without governing behaviour. That is a visibility gap, not a complete AI security posture.
👉 Read our full editorial: AI agent security frameworks are now a governance requirement
