Topic starter
25/10/2026 11:05 am
TL;DR: AI systems need continuous validation because behavior can shift across prompts, tools and context, according to Mindgard. Periodic checks on the model alone can leave runtime controls exposed to drift and unsafe outcomes.
Editorial analysis by NHI Mgmt Group, based on content published by Mindgard: “Mythos Isn't an AI Security Strategy”.
Key questions
Q: Why do AI agents create governance risk even when they are meant to help testing?
A: AI agents create risk because they can follow unexpected paths, misread context, or act on malicious instructions without needing human intent.
Q: How should security teams test AI agents that can call tools and APIs?
A: They should test the agent's actual execution paths, not just its text responses.
Q: What breaks when AI security is treated only as model security?
A: Model-only security misses the part of the system that actually touches tools, data, and workflows in production.
Practitioner guidance
- Build continuous AI revalidation into change management Re-test models, prompts, guardrails, retrieval sources and tool permissions whenever the system changes, because each change can create new attack paths.
- Inventory every AI tool and delegated permission Document which tools, memory stores and external data sources each system can reach, then map those actions to specific business and security risks.
- Separate model assistance from security decision authority Use cyber-focused models to accelerate analysis, but keep final judgments, escalation decisions and governance ownership with human reviewers.
Bottom line: AI security is not solved by better model output alone because the system itself changes under different prompts, tools and context.
Glossary terms: Psycho-technical attack surface, Indirect Prompt Injection, Context Poisoning, Continuous validation
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This topic was modified 7 days ago by NHI Mgmt Group
27/10/2026 7:16 pm
Cyber-focused models are accelerators, not control systems. They can improve code review, vulnerability explanation and other established workflows, but those gains do not equal AI security assurance. The security problem changes once the target becomes a probabilistic system with tools, memory and context dependence. Practitioners should treat model-assisted analysis as one input to governance, not the governance model itself.
A few things that frame the scale:
- Only 5.7% of organisations have full visibility into their service accounts, according to the Ultimate Guide to NHIs.
- 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 rely on cyber-focused models for AI red teaming?
A: They should use them as accelerators, not as a substitute for offensive expertise. Cyber-focused models are helpful for summarisation, code review and finding known issues, but AI security still needs proprietary attack intelligence, behavioural testing and continuous validation. The right model improves the work, but it does not replace the security programme.
👉 Read our full editorial: Cyber-focused models accelerate AI testing, but not AI security
This post was modified 7 days ago by NHI Mgmt Group