Modernize Role-Based Access Control (RBAC) with AI-driven Identity Analytics

Reduce Operational Costs, Mitigate Risks, and Ensure Continuous Compliance

AI-Driven Role Management Accelerates Zero Trust

Megatrends like digital transformation, rapid cloud adoption, and a historic rise in remote work are redefining the parameters of good identity governance and administration (IGA). Many new identity types (machines, devices, APIs, applications, and microservices) require advanced methods to control access. Legacy RBAC solutions rely on manual role mining and modeling. This approach fails to keep up with identities at scale in today's fluid business environments, where employees frequently change jobs, roles and or organizations. The results are overprovisioned access, orphaned accounts, and entitlement creep, which notoriously escalate insider and external threats.

Understanding the challenges of traditional RBAC and the best practices to address them is crucial for maximizing your identity governance investments. This white paper highlights the pitfalls of legacy RBAC solutions and how to leverage AI and machine learning to augment your existing IGA solution. You’ll be able to better manage and enforce least privilege access in a Zero Trust world. The result? Lower operational costs, smarter security, and time back in your day.

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