Notifications
Clear all
Topic starter
20/08/2026 5:51 pm
TL;DR: AI misuse prevention is shifting from static awareness to continuous, data-driven governance that correlates behaviour, identity, and threat signals across employees and AI agents, according to Living Security Human Risk Management Platform. The key challenge is that Shadow AI and non-human activity create risk outside traditional monitoring, so IAM and security teams need identity-aware controls, not training alone.
NHIMG editorial — based on content published by Living Security Human Risk Management Platform: What Is an AI Misuse Prevention Training Platform?
By the numbers:
- 72% of organisations have experienced or suspect they have experienced a breach of non-human identities, including 46% confirmed breaches and 26% suspected breaches.
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 shadow AI tools create identity governance risk?
A: Shadow AI is risky because users often reach those tools through identities, browser sessions, or tokens that were never assessed for data handling or access scope.
Q: What do security teams get wrong about AI visibility?
A: They often assume licence data or static configuration data is enough to understand AI risk.
Practitioner guidance
- Classify AI tools by identity risk Group sanctioned, unsanctioned, and delegated AI tools by the identities they touch, the data they can access, and whether activity is attributable in logs.
- Link AI use to access review workflows Add AI tool access and AI agent permissions to periodic access reviews so human accounts, service accounts, and delegated workflows are assessed together.
- Correlate behaviour with privilege Detect risky AI use by joining behavioural signals with IAM and PAM context, especially where privileged users paste data into public models or unvetted tools.
What's in the full article
Living Security Human Risk Management Platform's full blog post covers the operational detail this post intentionally leaves for the source:
- Behavior-based scoring logic for identifying risky AI use across employee and agent activity
- Examples of adaptive micro-training and contextual nudges triggered by specific misuse patterns
- Platform workflow details for correlating identity, behaviour, and threat signals in one view
- Guidance on building a human risk management programme around AI misuse rather than awareness alone
AI misuse prevention and Shadow AI: are identity controls enough?
Explore further
20/08/2026 6:30 pm
AI misuse prevention is becoming a governance discipline, not a training category. The article is strongest when it treats misuse as a pattern emerging across people, tools, and data rather than as a single employee mistake. That aligns with how modern identity programmes have evolved: visibility matters, but lifecycle control matters more. For IAM and NHI teams, the lesson is that behaviour change without access governance only reduces symptoms, not exposure.
A question worth separating out:
Q: Should organisations re-evaluate IAM and PAM for agentic AI deployments?
A: Yes, because agentic systems can inherit credentials and exercise privileged tools in ways that traditional IAM and PAM reviews do not fully capture. Organisations should reassess whether their current models account for ephemeral tasks, delegated authority, and machine-speed execution. The key test is whether access can be constrained to the exact task and revoked immediately afterward.
👉 Read our full editorial: AI misuse prevention needs identity-aware governance, not training alone
