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Why AI Falls Flat in IAM
Imagine deploying a state-of-the-art AI model into your IAM system, expecting it to streamline access decisions and enhance security. However, instead of delivering insightful recommendations, it churns out ambiguous suggestions, leaving your team skeptical of its utility.
This scenario is common because AI thrives on consistent, high-quality data. Yet, many IAM environments are riddled with inconsistencies:
Diverse Entitlement Models: Organizations often manage hundreds of applications, each with its unique entitlement structures, making it challenging for AI to find patterns.
Inconsistent HR Data: Discrepancies between HR records and directory services like Active Directory can confuse AI algorithms.
Outdated Role Definitions: Roles created years ago may no longer reflect current job functions, leading AI to base decisions on obsolete information.
Lack of Behavioral Context: Without historical data on user behavior, AI struggles to distinguish between normal and anomalous activities.
So how do we get past this?
Step 1: Feed It Clean, Connected Data
Before AI can make intelligent decisions, it requires a foundation of clean, well-connected data. This involves:
Attribute Normalization: Standardizing user attributes such as department names, job titles, and locations to ensure consistency across systems.
Entitlement Cleanup: Removing obsolete or redundant entitlements that can confuse AI models.
Data Lineage Mapping: Understanding how data flows between systems to provide AI with context about data origins and transformations.
( Yes, you have to do some work beforehand. AI isn’t just magically solving all your problems…yet. But the good news is it’s not as much work as you needed before. ML algorithms are much better this time around. Now, whether or not the vendor you choose has implemented them correctly is a different story)
Step 2: Define the Behavior You Want It to Learn
AI models need clear definitions of desired behaviors to function effectively. This involves:
Establishing Baselines: Determining what constitutes normal access patterns for different roles and departments.
Identifying Anomalies: Defining what behaviors are considered risky or unusual, such as access requests outside typical working hours or from unfamiliar locations.
Feedback Loops: Implementing systems where AI suggestions are reviewed and refined based on human input, allowing the model to learn and adapt over time.
A practical application is using AI to detect unusual access requests. For instance, if an employee in the finance department suddenly requests access to engineering resources, the AI can flag this as an anomaly for further review.
Step 3: Human in the Loop = AI That Works
While AI can significantly enhance IAM processes, human oversight remains crucial. This collaborative approach ensures:
Contextual Understanding: Humans can provide context that AI might lack, such as understanding organizational changes or specific project needs.
Ethical Considerations: Human judgment is essential in making decisions that involve privacy and ethical implications.
Continuous Improvement: Regular reviews of AI decisions help in refining algorithms and improving accuracy over time.
Okta’s introduction of “Auth for GenAI” exemplifies this approach by incorporating human-in-the-loop approvals for AI-driven access requests, ensuring that AI recommendations are vetted before implementation. Source
Integrating AI into IAM isn’t a plug-and-play solution—far from it. Success doesn’t come from simply flipping a switch or enabling a new feature. It comes from taking deliberate, thoughtful steps to build a strong foundation before AI ever enters the picture.
It starts with data. AI models are only as good as the data they consume. If your environment is cluttered with inconsistencies, outdated entitlements, and mismatched attributes, you’re setting the AI up to fail. Cleaning, structuring, and connecting that data isn’t optional—it’s table stakes.
Next comes behavior. AI needs to know what “good” looks like. You have to define what normal access patterns are, what risky behavior means in your context, and what your ideal access flow should be. Without that baseline, even the most advanced AI won’t know where the boundaries are. And even then, AI doesn’t replace your team—it extends them. It augments human decisions, it accelerates reviews, and it shines a light on risks that might otherwise go unnoticed. But it still needs human judgment to guide it, challenge it, and make the final call.
When done right, AI doesn’t just automate identity—it transforms it.
But only if you teach it the language first.