Topic starter
15/08/2026 6:10 pm
TL;DR: AI risk is being normalized through permissive defaults, weak oversight, and repeated success without consequences, creating a culture where unsafe model and agent behaviour starts to look acceptable, according to AppSOC. The deeper problem is not just technical exposure but governance drift, because teams begin treating probabilistic AI outputs and autonomous actions as if they were reliable controls.
Editorial analysis by NHI Mgmt Group, based on content published by AppSOC: “Blog: Normalization of AI risk can lead to disaster - Normalization of Deviance”.
Key questions
Q: What breaks when AI systems are treated as trustworthy because they have not failed yet?
A: The control model breaks because absence of visible harm is not evidence of safety.
A: AI assisted workflows increase risk because they can generate working code that omits security controls, recommend unsafe dependencies, and copy insecure patterns at scale.
Q: How can security teams tell whether AI-driven exposure is becoming a governance problem?
A: Look for the point where access decisions no longer reflect the current use case.
Practitioner guidance
- Inventory AI models, agents, prompts, and tool paths Build and maintain an inventory of every model, agent, prompt flow, and connected tool so the programme knows what can act, what it can access, and where oversight is missing.
- Separate suggestion from execution authority Assign different controls to systems that recommend actions and systems that can execute them, and require explicit approval for any workflow that crosses that boundary.
- Test permissive defaults before production Review vendor defaults, data access, and runtime permissions before rollout, then validate that the deployed configuration matches policy rather than convenience.
Bottom line: The article frames normalization of deviance as an AI security failure mode because repeated success can make unsafe behaviour feel acceptable.
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15/08/2026 6:18 pm
Normalization of deviance is the real AI security failure mode: organisations do not fail because they lack awareness of AI risk, they fail because repeated success trains them to ignore it. That drift matters more than any single model flaw because it changes how teams interpret warnings, exceptions, and near misses. Practitioners should treat cultural acceptance of weak controls as a security control failure in its own right.
A few things that frame the scale:
- Only 13% of organisations feel extremely prepared for the reality of agentic AI despite the majority racing toward autonomous adoption, according to the 2026 Infrastructure Identity Survey.
- AI-related credential leaks surged 81.5% year-over-year in 2025, with the surrounding AI infrastructure leaking 5x faster than core LLM providers, according to the State of Secrets Sprawl 2026.
A question worth separating out:
Q: What should organisations do when AI systems start acting like operational controls?
A: They should reclassify those systems as governed decision points, not just productivity tools. That means defining ownership, approval thresholds, runtime monitoring, and rollback paths before the system can influence data, identity, or business processes. Otherwise, the AI becomes an informal control without formal accountability.
👉 Read our full editorial: Normalization of deviance is the real AI security failure mode