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When it comes to identity governance, the future is here. Hyper-automation and self-driving governance promise to make as dramatic an impact as that of agile development. The result? Faster regulatory compliance, lower costs, and substantially reduced risk.
Artificial intelligence (AI) based on machine learning (ML) is the ideal foundation for automating identity governance. When AI is supplied with rich data representing an enterprise-wide view of all aspects of identity, it can streamline and automate intelligence across all identity governance and administration (IGA) use cases: access requests, access reviews, and role mining. Rather than forcing analysts to manually correlate masses of data, AI can proactively identify access risks and provide context for quicker decision-making. In addition, AI can accurately identify excessive privileges and provide confidence scoring that teams can use when making actionable decisions.
One of the most important benefits of using AI- and ML-based solutions is the ability to hyper-automate existing IGA processes. Defined by Gartner as tools to “…integrate functional and process silos to automate and augment business processes,” hyper-automation goes beyond simply providing information to decision-makers. It can discover, monitor, and improve user access, enabling business processes to become self-driving.
As an example, hyper-automation can automate remediation recommendations, such as access predictions, access provisioning/deprovisioning, and role definitions. This pushes actions directly back to the existing IGA solution. In addition, hyper-automation paves the way for micro-certifications. Leveraging ML, it can automate approvals of low-risk, high-confidence users. This greatly reduces the workload of business line managers or applications owners who now only need to approve a small set of entitlements and roles in between annual or biennial certification campaigns.
ForgeRock’s Approach to Self-Driving Identity Governance
ForgeRock delivers the industry’s first
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