Topic starter
09/07/2026 12:38 pm
TL;DR: Traditional IAM controls struggle to log, scope, and review AI interactions as GenAI becomes embedded in enterprise workflows, according to Knostic. The core issue is assumption drift: access review, authorization, and audit models still presume discrete human actions, while AI systems synthesize data across sources in ways those controls never modeled.
Editorial analysis by NHI Mgmt Group, based on content published by Knostic: “Identity and Access Management for the GenAI Era”.
Key questions
Q: How should security teams handle AI systems that infer sensitive data across multiple sources?
A: Security teams should treat inference as an access path, not just the final output.
Q: Why do traditional IAM controls miss AI oversharing risks?
A: Traditional IAM controls miss AI oversharing because they were built for discrete actions such as login and file access, not semantic synthesis across many sources.
Q: What breaks when AI agents are connected through personal accounts or shared credentials?
A: Shared or personal credentials break accountability, lifecycle control, and revocation.
Practitioner guidance
- Implement prompt and response logging Capture user identity, timestamp, prompt text, generated response, source lineage, and policy decisions so AI interactions are auditable end to end.
- Bind AI access to sensitivity labels Apply content classification at retrieval and generation time so models cannot use secret or regulated material outside the approved scope.
- Inventory AI agents as governed identities Separate human credentials from agent runtime access, then document each agent’s ownership, privilege scope, and offboarding path.
Bottom line: AI-driven workflows expose a structural gap in IAM because they can combine data across sources without producing the clean, discrete evidence that legacy audit models expect.
Explore further
View Full Forum → | NHI Foundation Course → | Our Services → | Read the full analysis →
09/07/2026 12:44 pm
AI agents create an auditability gap, not just an automation gap: traditional IAM was designed to log visible, discrete actions, but GenAI can combine retrieval, inference, and response into a single opaque interaction. That means the issue is not whether a file was opened, but whether the organisation can reconstruct the decision path that produced the answer. Practitioners need to treat traceability as a design constraint, not a reporting add-on.
A few things that frame the scale:
- 7% of security leaders admit they do not know how often their AI systems are making autonomous changes to infrastructure, according to the 2026 Infrastructure Identity Survey.
- Generative AI use specifically increased from 33% in 2023 to 79% in 2025, according to McKinsey’s Global Surveys on the State of AI.
A question worth separating out:
Q: How can organisations tell whether AI governance is actually working?
A: Organisations can tell AI governance is working when they can inventory every agent, explain its purpose, show who owns it, and prove that permissions are tightly scoped. If those four things are missing, the programme has policy language but not operational control. Auditors will notice the gap quickly.
👉 Read our full editorial: AI agent identity risk is outpacing traditional IAM controls