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03/06/2026 11:00 am
TL;DR: Pathlock’s webinar says a local LLM can surface orphaned SAP accounts, SoD risks, and privileged sessions in seconds, and can also build provisioning workflows from chat while keeping identity data inside the environment. That shifts the question from automation efficiency to whether IAM and IGA controls can still govern runtime decisions without exposing sensitive data.
NHIMG editorial — here’s why we think this discussion matters
By the numbers:
- You have 400 SAP accounts, three people to review them, and an audit in six weeks.
Questions worth separating out
Q: How should IAM teams govern conversational access review tools for identity data?
A: Treat conversational review tools as an interface over existing controls, not as a new authority.
Q: Why do local LLMs matter for identity governance in regulated environments?
A: Local deployment matters because identity data often contains privileged relationships, audit evidence, and separation-of-duty findings that should not leave controlled environments.
Q: What should security teams check before using chat to build provisioning workflows?
A: Check that the workflow generated from chat still enforces requester, approver, and provisioner separation, plus clear exception handling.
Practitioner guidance
- Define the model’s governance boundary Document exactly which identity tasks the conversational layer may assist with, which decisions remain human-owned, and where approval is mandatory before any workflow is enacted.
- Map identity data flow end to end Trace prompts, retrieved identity records, generated recommendations, and stored outputs to verify that regulated data stays within approved systems and logging paths.
- Validate SoD logic after workflow generation Test chat-created onboarding or access workflows against separation-of-duty rules, escalation paths, and provisioning exceptions before allowing them into production.
The practical test is whether the assistant improves evidence handling across the full review trail, not whether it feels faster to use?
👉 Register for Pathlock’s live webinar on agentic AI for identity governance →
Explore further
03/06/2026 11:03 am
Conversational IGA is a governance interface change, not an identity control model. The webinar shows how a model can reduce friction in account review and workflow creation, but the underlying controls are still the same. Identity teams should treat the assistant as an interaction layer over existing governance, not as a substitute for policy, evidence, or approval boundaries. The practical conclusion is that automation may change the speed of review, but it does not change who remains accountable for the decision.
A few things that frame the scale:
- Only 52% of companies can track and audit the data their AI agents access, leaving 48% with a complete blind spot for compliance and breach investigation, according to AI Agents: The New Attack Surface report.
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, inappropriately sharing sensitive data, and revealing access credentials.
A question worth separating out:
Q: How can teams tell whether conversational IGA is improving governance or just speeding up mistakes?
A: Look for evidence quality, not just throughput. If the tool surfaces orphaned accounts, SoD issues, and privileged sessions faster, but reviewers still need to recheck every finding from scratch, governance quality has not improved enough. Measure whether decision time falls without increasing false approvals, policy exceptions, or undocumented overrides.
👉 Read our full editorial: Pathlock’s agentic AI for IGA asks what changes for identity
