Overcoming Identity Governance Challenges with ForgeRock Autonomous Identity

Short Summary

Identity Governance and Administration (IGA) is a core IAM capability, but it remains difficult for both businesses and users because identity data is spread across silos, key tasks are operationally inefficient, and decisions are made with too little context. Organizations struggle to understand where access occurs, provide consistent entitlement experiences (roles, groups, requests, reviews), and perform meaningful analytics across disconnected environments. As a result, overprivileged access becomes common, permissions accumulate beyond what is required, orphaned accounts persist, and visibility and accountability suffer—raising security, audit, and regulatory compliance risks.

Traditional conceptual approaches like role mining and role engineering have not reliably solved these problems, especially as businesses require agility due to cloud adoption, remote work shifts during the COVID-19 pandemic, and rapidly changing collaboration and operating models. IGA must adapt quickly rather than inhibit change, yet many current implementations rely on static entitlement models, complex role structures, and highly manual processes such as recertification campaigns that encourage “rubber stamping.”

AI and ML augmentation is positioned as a practical way to reduce complexity and improve outcomes, provided it is implemented carefully. AI can identify entitlement patterns and outliers, propose likely entitlements to request, recommend approvals or revocations during reviews, and help focus “micro certifications” on what matters most. Success depends on focused outputs (avoiding overload), accurate recommendations, sufficient data volume and variety (including usage data), and explainable, predictable, reproducible results.

ForgeRock Autonomous Identity is presented as an AI-driven identity analytics overlay that ingests and normalizes broad identity data from ForgeRock and third-party systems, builds historical models of access change, produces confidence scores with justification, and pushes actionable recommendations for remediation, provisioning, deprovisioning, and role definitions via APIs and an integrated UI.

Interesting Facts

• IGA includes identity lifecycle management (provisioning) and access governance functions such as access requests, approvals, reviews, and analytics.
• Overprivileged access is described as the norm rather than the exception in many IGA implementations.
• Excessive permissions increase cyber risk and can violate regulatory compliance requirements.
• Orphaned accounts are common because static entitlement data does not indicate whether accounts are in use.
• Access reviews are commonly perceived as time-consuming, hard to understand, and cumbersome.
• AI can analyze entitlement patterns, identify outliers, and determine standard entitlements for groups of users.
• AI can recommend entitlements likely to be requested, simplifying search across large entitlement catalogs.
• Successful AI in IGA requires focused recommendations, accuracy, and sufficient data volume, including usage data.
• ForgeRock Autonomous Identity consumes data from ForgeRock’s Identity Platform and third-party IGA solutions.
• ForgeRock Autonomous Identity produces confidence scores, justification, and recommendations, and can push remediation and provisioning-related actions.

Notable Quotes

• “The challenge for IGA today is that tasks are complex, without context, and highly manual.”
• “Today’s IGA is not built to rapidly adapt to changes, such as the shift to the cloud or new remote work models in the COVID-19 pandemic.”
• “Searching for the ‘needle in the haystack’ is a task well suited for AI.”
• “Successful AI solutions must deliver predictable and reproducible results.”
• “AI must not lead to information overload.”
• “AI must explain the key indicators as to why it is proposing these recommendations.”
• “Don’t put all your eggs in the AI basket, but also solve the conceptual issues.”
• “ForgeRock Autonomous Identity is not just an add-on to existing IGA solutions.”
• “This transparency is essential for modern AI solutions.”
• “IGA must not be an inhibitor for change.”