Policy Based Access Control promises fine grained, context aware authorization, but many organizations struggle to adopt it at scale. The biggest barrier is not the model itself. It is the difficulty of authoring, validating, and maintaining policies that accurately reflect real access intent.

Most access intent is expressed in natural language through design docs, tickets, audits, and reviews. Recently, teams have started exploring AI to convert this intent directly into authorization policies. While this sounds appealing, early experiments show it is easier said than done. AI generated policies often look correct but contain subtle flaws such as missing conditions, ambiguous subjects, or overly permissive defaults. These mistakes can quietly introduce serious access risks.

This session explores how natural language can still be a powerful entry point for PBAC when paired with the right guardrails. It discusses the risks of raw AI driven policy generation, why those risks matter for authorization, and how a structured pipeline using validation, constraints, and human review can help bridge intent and enforcement safely. The focus is on making PBAC practical without turning policy automation into a new attack surface