Topic starter
03/06/2026 11:00 am
TL;DR: Pathlock's June 10 webinar says a local LLM can surface orphaned SAP accounts, Segregation of Duties risks, and privileged sessions in seconds while also building provisioning workflows from chat, shifting the IGA question from manual review speed to whether identity governance can safely absorb runtime decisions inside the environment.
Editorial analysis by NHI Mgmt Group, based on content published by Pathlock: “Orphaned Accounts, Privilege Abuse & Broken Workflows: How Pathlock’s Agentic AI Handles All Three”.
Key questions
Q: How should teams govern agentic AI inside IGA workflows?
A: Start by limiting the agent to evidence discovery and workflow drafting, while keeping approval authority and policy changes under human control.
Q: What breaks when AI agent access is reviewed only after the fact?
A: After-the-fact review leaves a gap between action and containment.
Practitioner guidance
- Define the local model boundary Confirm which identity datasets the agent may query, which outputs it may generate, and which actions still require explicit human approval inside the workflow.
- Separate evidence retrieval from approval Make sure the system can surface orphaned accounts, SoD violations, and privileged sessions without collapsing those findings into automatic provisioning or access changes.
- Review generated workflows as governed artifacts Treat chat-built onboarding flows like any other policy change, with approval path review, entitlement validation, and exception handling checked before deployment.
Bottom line: Agentic AI changes IGA by compressing evidence search, workflow drafting, and review support into a single interaction.
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This topic was modified 2 days ago by NHI Mgmt Group
03/06/2026 11:03 am
Agentic AI changes IGA from workflow execution to governance interpretation. The webinar's core point is that the system is not only automating identity work, but interpreting questions, evidence, and workflow intent in one interaction. That matters because IGA controls were designed to separate review, decision, and execution into distinct steps. When those steps collapse into a single conversational path, the governance model has to account for who is effectively deciding what the system searches, surfaces, and builds.
A few things that frame the scale:
- A Harris Poll survey of more than 300 technology decision-makers found that 86% expect agentic AI to deliver positive ROI, yet fewer than half had AI governance policies in place.
A question worth separating out:
Q: What is the difference between conversational IGA and conventional workflow automation?
A: Conversational IGA lets a user ask for analysis or a workflow in natural language, while conventional workflow automation follows predefined forms, rules, and paths. The key difference is that agentic systems may infer intent and assemble steps, so governance must verify the generated logic instead of only checking a fixed configuration.
👉 Read our full editorial: Pathlock’s agentic AI for IGA asks what changes for identity
This post was modified 2 days ago by NHI Mgmt Group