Topic starter
20/08/2026 5:54 pm
TL;DR: GenAI training ROI cannot be measured by completion rates alone because risk now comes from both employees and AI agents interacting with sensitive systems, according to Living Security Human Risk Management Platform's analysis. The useful measure is behavioural change linked to identity, access, and threat signals, because training only matters when it reduces risky actions and closes the human machine risk gap.
Editorial analysis by NHI Mgmt Group, based on content published by Living Security Human Risk Management Platform: “How to Measure GenAI Training ROI & Effectiveness”.
Key questions
Q: How should organisations measure GenAI training effectiveness?
A: Measure whether training changes behaviour, not just attendance.
Q: Why do identity and access signals matter in GenAI training ROI?
A: Because the same behaviour has very different impact depending on what access the person or agent holds.
Q: What do security teams get wrong about GenAI training metrics?
A: They often confuse completion with change.
Practitioner guidance
- Define behaviour-based success criteria Replace completion-rate targets with measures tied to risky actions such as data exposure, unsafe prompt use, phishing susceptibility and policy violations.
- Correlate identity, behaviour and threat data Join access records, user activity telemetry and threat intelligence so you can see whether high-risk behaviours are occurring in high-value contexts.
- Add AI agent activity to your measurement scope Track agent permissions, task patterns and anomalous access in the same reporting model used for employees, especially where agents reach sensitive systems.
Bottom line: GenAI training is a governance control only when it changes behaviour in ways that reduce unsafe data handling, phishing exposure and policy violations.
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20/08/2026 6:35 pm
Training effectiveness is now an identity and access problem, not a learning-completion problem. If a training programme does not change how users handle data, evaluate AI output, or interact with connected systems, it has not changed the actual risk surface. In GenAI environments, the decisive question is whether behaviour changed at the point where identity, access and workflow intersect. Practitioners should treat training as a control outcome, not an HR metric.
A few things that frame the scale:
- 70% of organisations grant AI systems more access than they would give a human employee performing the exact same job, according to the 2026 Infrastructure Identity Survey.
- 19% of organisations give AI systems dramatically more access than human employees, nearly one in five granting unrestricted privilege, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: How should teams account for AI agents in training and risk measurement?
A: They should measure agent behaviour as part of the same governance model used for humans. That includes monitoring permissions, access patterns, task execution and anomalies, because agents can amplify risk even when employees are well trained. If the programme ignores agent activity, it misses half the risk surface.
👉 Read our full editorial: Measuring GenAI training effectiveness needs human and machine risk