What is the ForgeRock Autonomous Identity product?

ForgeRock Autonomous Identity (AI-driven) is a separate analytical product in the ForgeRock portfolio that uses machine learning to analyse identity data and access patterns to support identity governance decisions. It was designed to solve one of the most persistent problems in identity governance: role explosion and entitlement sprawl in large enterprises where thousands of roles and permissions have accumulated over years with little coherent rationale.

Autonomous Identity performs three main functions:

Role Mining — The system analyses existing user-entitlement assignments across connected systems (Active Directory groups, application roles, cloud entitlements) and uses clustering algorithms to identify natural role patterns. Instead of manually designing roles from scratch, administrators get AI-suggested roles based on what users actually have in common.

Access Risk Scoring — Each user-entitlement assignment is scored for risk based on how unusual it is compared to peers with similar job functions, locations, and organisation units. A software developer with read access to the financial ledger would receive a high risk score because it diverges from the peer group norm. This enables risk-based access reviews where reviewers prioritise high-score anomalies rather than reviewing every assignment.

Access Certification / Campaign Insights — During access review campaigns (typically run in ForgeRock IDM's governance module), Autonomous Identity provides certification recommendations — should this access be approved or revoked? — based on the risk score and historical certification decisions for similar assignments.

Autonomous Identity is typically deployed as a separate analytics cluster (Apache Spark-based) that ingests identity data via connectors and produces recommendations consumed by IDM's governance workflows. In the PingOne era, these capabilities are being integrated more tightly into the core platform.

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