If your identity governance program feels like a relic from a simpler time, you’re not alone. Traditional identity governance and automation (IGA) was built for a world where job titles told the whole story. A software engineer was a software engineer; a sales rep was a sales rep. Assigning access was intended to be as simple as slotting people into predefined roles.
Today, that model is showing its age. Your organization is likely feeling the pressure. You may now have roles bloated with thousands of entitlements that no one quite understands. Access reviews have devolved into a rubber-stamping ritual because managers lack the context to make better decisions. And your security team is left wondering which of those thousands of permissions is the one an attacker will find first.
The static, label-based approach to access is creating more problems than it solves. It’s time for a smarter way forward, one that sees people for who they really are: a collection of unique, dynamic layers.
The risk of one-dimensional access in legacy IGA
Legacy IGA treats people like static labels, flattening identities and ignoring the nuances of real-world risk. This leads to a kind of “role fatigue” that introduces serious risk. Well-intentioned role-mining projects often spiral into a maintenance nightmare, sometimes creating more roles than there are people in the company.
This outdated approach creates several critical challenges for security leaders:
- Pervasive rubber-stamping: During access certification campaigns, managers face long lists of users and permissions. Without clear context on why someone has access, they often approve everything just to get the task done. This can turn a critical security check into a meaningless compliance exercise.
- Widespread role bloat: What happens when someone needs an exception? More often than not, teams just tack on more entitlements to an existing role. A financial analyst joins a special project and gets temporary access that is never revoked. Over time, these roles become bloated containers of excess permissions, creating a massive attack surface.
- Amplified security risks: Over-privileged users are a goldmine for attackers. When an employee’s account has more access than needed, it becomes a high-value target for compromise. The more permissions an account holds, the more an attacker can do once they’re inside.
To close these gaps, we need to move beyond the single dimension of a job title and look at the actual complexity of a modern workforce.
Building a dynamic identity profile using a layered access model
People are more than their job titles. A person’s identity has multiple layers that define what access is truly appropriate. These layers are shaped by things like their department, location, team assignments, current projects, and even their specific access behaviors. These attributes, not a static job title, should determine what access a person actually needs.
Instead of forcing everyone into a rigid box, a modern approach to identity governance starts with the individual and views identity as a collection of dynamic layers:
1. Birthright access: The baseline for every employee on day one, covering essentials like email, the HR portal, and the company intranet.
2. Role-based access: Access based on a core job function, like a developer gaining access to code repositories. In this model, traditional roles still exist, but they remain lean.
3. Peer-based access: This is where the system gets smarter. By analyzing the access patterns of functional peers, we can make intelligent recommendations. If every other data scientist on a team uses a specific tool, a new team member probably needs it too.
4. Individual access: The final layer, which accounts for unique permissions needed for special projects or specific tasks.
This layered model provides a far more accurate and dynamic picture of a person’s identity profile. It reflects how people actually work, acknowledging that access needs are fluid and contextual.
Moving from guesswork to precision with AI profiles
While this layered model provides a better conceptual framework, implementing it at scale is another challenge entirely. Manually tracking these dynamic attributes across an entire organization is impossible. This is where artificial intelligence changes the game.
AI profiles apply machine learning to analyze identity data across your organization. The system continuously examines user attributes, access patterns, and usage to build a dynamic, intelligent profile for every single identity. This isn’t intended to replace human oversight; it’s about arming it with data-driven insights.
Here’s how this approach solves the core challenges of traditional governance.
Simplifying access with AI-driven recommendations
Instead of asking a manager to approve a list of 50 arcane entitlements for a new hire, an AI-powered system can offer a simple, powerful recommendation: “This person’s profile is a 95% match with other senior engineers on the team. Do you want to grant them the same access?”
This transforms the conversation from a granular, technical review to a simple, business-focused decision. The manager doesn’t need to be an expert on every permission; they just need to confirm that the person’s role aligns with their peers. This can help automate onboarding with access that fits from day one.
Reducing fatigue with anomaly-based access reviews
The rubber-stamping problem is a direct result of information overload. AI profiles cut through the noise by highlighting anomalies. Instead of showing a manager every permission a user has, it flags the outliers that represent real risk. For example, an alert might read: “This project manager has access to a financial database that no other project manager uses. Should this access be revoked?”
This gives the manager a clear, actionable decision. By focusing reviews on the exceptions, they can become faster, more effective, and far better at reducing risk.
Empowering data owners through usage patterns
Beyond simplifying the manager’s experience, AI profiles also ensure that the right person, specifically the data owner, is in the loop.
Ultimately, the person who understands the data best is the data owner. Yet in traditional models, they are rarely involved in access decisions. AI profiles can identify who owns a particular application or dataset based on usage patterns and organizational structure. When an access request comes in for that resource, the system can route it directly to the data owner for approval, ensuring the person with the most context makes the final call.
Strengthening security posture with AI-driven identity governance
Moving to an AI-driven model for identity governance isn’t about chasing a trend. It’s about solving long-standing problems that expose your organization to risk and bog down operations. By embracing a more dynamic and intelligent approach, you can finally end the cycle of access review fatigue, automate secure onboarding, empowering your team to focus on mitigating actual risks rather than chasing administrative noise.
The goal is a stronger security posture and a more efficient organization. Your people get the access they need to do their jobs—and nothing more. It’s a human-centric approach, powered by machine intelligence, that allows you to achieve access that fits the individual, not just their label.
Brian Smits is a solutions strategy architect at CyberArk, a Palo Alto Networks company.
Ready to take your IGA program beyond labels? Learn how layered AI identity profiles improve access decision accuracy, reduce certification fatigue, and strengthen compliance in this webinar hosted by Brian Smits.
