Topic starter
15/08/2026 6:13 pm
TL;DR: Generative AI systems face prompt injection, data poisoning, model inversion, and identity and access control failures that can expose data and manipulate outputs, according to Obsidian Security. The practical issue is not just model safety but whether AI agents, tokens, and data-source access are governed as security-relevant identities before abuse becomes operational.
Editorial analysis by NHI Mgmt Group, based on content published by Obsidian Security: “GenAI Security Risks: Understanding Emerging Attack Vectors”.
Key questions
Q: What breaks when AI agents are given access without identity governance?
A: What breaks is accountability.
Q: When do AI agent credentials create more risk than they reduce?
A: They create more risk when they are long-lived, over-scoped, hard to revoke, or copied into code and prompts.
Q: What do security teams get wrong about prompt injection defence?
A: They often assume better blocklists will solve the problem, but obfuscation simply changes the shape of the payload.
Practitioner guidance
- Scope AI agent access as non-human identity Inventory every agent, token, service account, and delegated connector.
- Separate prompt handling from tool execution Do not let model output directly control downstream actions.
- Test for prompt injection and jailbreak paths Add adversarial testing to pre-production and change management.
Bottom line: Generative AI risk is no longer limited to unsafe content generation, because prompt manipulation, poisoned data, and overprivileged access can all become operational security failures.
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15/08/2026 6:19 pm
AI governance debt is now an identity problem, not only a model-risk problem. The article shows that the most damaging failures are not limited to bad outputs. They appear when AI systems hold access that was never governed as a security identity. That means the control gap is not just around prompts, but around delegated authority, token scope, and revocation discipline. Practitioners should treat AI access governance as a core part of identity architecture.
A few things that frame the scale:
- Only 23% of IT leaders were very confident in their organisation's ability to manage security and governance for GenAI deployments, according to a 2025 Gartner survey of 360 IT leaders.
A question worth separating out:
Q: Should organisations govern AI tools like third-party software dependencies?
A: Yes. External tools carry operational intent in the same way dependencies carry code risk. If the tool catalogue is not reviewed for provenance, scope, and hidden instructions, the agent inherits an untrusted supply chain at runtime. The governance model should therefore cover onboarding, approval, and continuous review.
👉 Read our full editorial: GenAI security risks expose the gap in AI agent governance