AI Role Mining in Identity Governance: How Machine Learning Revolutionizes IGA

As more organizations adopt SaaS applications to meet new business demands more efficiently, they need to address insufficiencies in their identity management strategies and take decisive steps to improve them. For many organizations, quickly identifying and managing access using manual processes becomes unsustainable as they onboard more SaaS applications and more users.

The challenge compounds through the identity lifecycle as users change roles, leave the organization, and so forth. Addressing this challenge today and in the future requires organizations to embrace a modern Identity Governance and Administration (IGA) solution. Establishing the right role-based access controls using a role-mining exercise is a vital step in this process. But how does one do that efficiently and effectively?

In this post, we’ll explain how role mining works and why it helps improve overall security posture. You’ll also gain insight into how emerging technologies like artificial intelligence-based machine learning help automate role mining and make it easier and faster for IT administrators to manage access and compliance policies in response to changing business requirements.

What is Role Mining?

Put simply, role mining is a process of discovering relationships between entitlements and a user’s job role. Role mining enables the IT administrators responsible for identity governance and administration (IGA) to analyze mapping access to data and systems and determine if users in an enterprise have sufficient access to the applications and systems they require to do their jobs. After analyzing this mapping data, identity access and governance managers can modify permissions to support the principle of least privilege.

Role mining is widely considered the best way to gather intelligence about the user permissions and entitlements that are necessary to perform specific roles in an enterprise. When performed correctly, a role can also help to reduce complexity during the onboarding process by assigning birthright access and entitlements by function, role, or role set, and enable new hires to be productive when they are first hired into a new position.

How Effective Role Mining Contributes to Better Cybersecurity

Data collected for The State of Identity Governance 2024 showed that unnecessary access to systems and applications and overly permissive accounts are a widespread concern in most enterprises. Overall, more than 72 percent of IT professionals surveyed agree that people in their organizations have unnecessary access to assets or are over-permissioned.

For organizations using legacy or in-house built IGA systems, this number jumps to nearly 78 percent. This troubling trend in access management is probably the principal source of concern about identity-related threats and is likely a significant reason for identity security breaches, compromised user accounts, and hackers gaining unauthorized access to sensitive data.

The role mining process is a critical aspect of Role-Based Access Control (RBAC), because it helps IT administrators improve cybersecurity by mitigating the proportion of users that have access privileges beyond what they need to do their jobs.

How Role Mining Works

There is not a single role-mining technique. In general, there are a couple of role-mining methods. These are capable of effectively mining roles, consolidating roles, and facilitating a more structured account access management process.

Data-Driven Role Mining:

The first method is based on data. In data-driven role mining, Identity Access and Governance managers mine users’ activities and relationships to reveal insights into access patterns. Data-driven role mining tools look for similarities between user activities across the environment to create logical groupings of user roles.

So, what is the role of data mining? Compared to alternative role mining methods, data-driven role mining offers a more accurate picture of what a technical role, business role, etc. should look like in an organization. It is an effective method for managing roles with common permissions. However, in instances where there are substantial anomalies and it is difficult to fit users into a logical pattern, this role management technique may not be as effective.

Template-Based Role Mining

Another approach is to develop templates to create roles. In Template-Based Role Mining, roles are predefined by identity access and governance managers with business leaders’ support.

When administrators add users to the system, they assign them to a previously created role. This approach is relatively quick to implement because there are already-created roles generated and role discovery is already done. It enables administrators to easily select, edit and save a role, etc.

The downside of using this approach to manage business roles is the elevated risk of giving users higher privileges than they need because there is an insufficient number of roles templated or a created business role does not reflect the true needs of every user assigned to it.

Department-Defined Roles

Organizations may also empower department or business unit managers to define role models. In this process, each stakeholder group engages in mining job roles to identify role candidates and make recommendations based on user-activity relationships within their specific areas of responsibility.

This approach creates a more accurate matching of privileges and permissions to user requirements, such as grouping technical roles with common applications and systems access requirements. But it can become problematic when users in different departments have, for example, identical job titles but dissimilar roles in the organization. In that instance, users would have the same permissions but not the same role requirements.

How Role Mining Results in Lower Impact from Account Credential Theft

The State of Identity Governance 2024 reported that more than 90 percent of IT and business professionals are concerned about identity-related cybersecurity threats. Specifically, they are most frequently concerned about account credential theft; with malware designed to steal credentials (87 percent), compromised user credentials (86 percent), and unauthorized access to sensitive data by external attackers (85 percent) topping the list. This amplifies the importance of identity management and ensures that organizations can effectively manage and permission user accounts at all access points.

There are many advantages to using role-mining tools to ensure more comprehensive account provisioning and reduce the impact of account security credential theft. Role mining helps you better match a role’s permission and security needs by only providing access to what a user needs to do their job effectively. By doing so, the impact of attacks on identity from credential theft is reduced significantly.

Role mining also affords user accounts transparency. For example, the system helps detect accounts that should no longer be active, which further reduces the attack surface.

AI Access Control Based on Machine Learning in IGA

The role mining definition is changing with the emergence of AI-based machine learning (ML) and its impact on RBAC. AI based on ML provides bedrock technologies on which organizations can automate their identity governance. AI, augmented with rich data, enables IT managers to have a 360-degree look at all aspects of identity management. The result is streamlined role mining as well as other IGA use cases like access requests, and access review intelligence.

Using AI, governance and access managers no longer need to manually analyze masses of data, enabling them to proactively identify access risks and give critical context to facilitate quicker decision-making. AI can accurately identify excessive privileges and provide confidence scoring that teams can use to make provisioning decisions and turbocharge existing IGA processes.

AI-based on ML provides deep learning, clustering, and natural language processing techniques to identify and categorize various business roles and their associated access privileges and resource entitlements within an organization. Process mining, time-series, and sequential pattern mining help discover the various business processes within an organization and their associated access and resource requirement patterns.

Unsupervised/semi-supervised learning and generative predictive analytics can detect and predict user access and user behavioral changes within an IT system to inform dynamic access change recommendations. Adversarial ML and anomaly detection identify suspicious, risky, and anomalous user behaviors in an organization’s IT system.

These technologies make it much easier for IT system administrators to uphold the policy of least privilege in the face of evolving requirements. This enables them to provide sufficient access to the applications necessary to perform their work while quickly blocking unnecessary access to combat identity-related security threats.

Using AI based on ML, IT administrators can automate systems to detect over-permissioned and non-essential access. Identity governance uses advanced analytics to predict, make recommendations, and enforce changes to users’ access privileges and entitlements. The principal benefit of broadening, limiting, and adjusting access based on employees’ changing business roles and job execution patterns enables greater system security and enhances employee productivity.

AI-Based Role Mining Challenges

Despite the many benefits to using AI-based role mining, there are some challenges that need to be addressed by organizations:

1. Data quality issues: AI algorithms rely on high-quality, comprehensive data. Inaccurate or incomplete data can lead to flawed role recommendations
2. Over-reliance on automation: Organizations should balance AI-driven insights with human expertise to ensure role assignments align with business needs
3. Complexity in highly dynamic environments: Frequently changing job roles or organizational structures may require more frequent retraining of AI models
4. Privacy concerns: The use of AI in analyzing user behavior and access patterns may raise privacy issues that need to be addressed

Future Trends in AI-Powered Role Mining

Some future trends in AI-powered role mining are predictive role modeling, where AI systems will not only analyze current access patterns but also predict future access needs based on career progression and organizational changes. Continuous adaptive role optimization will see AI systems continuously refining and adjusting roles in real-time, responding to changes in user behaviors and organizational needs. Another major future trend in role mining will likely be natural language processing (NLP).

What is the Role of NLP in Data Mining?

Advanced NLP capabilities will allow for automatic generation and updates of access policies based on business documents and communications. This will significantly enhance data mining by helping to efficiently process and analyze unstructured textual data to extract actionable insights.

In the context of identity governance and administration, NLP aids in precise role mapping, dynamic role adjustment, and automated role detection by uncovering relationships and patterns in user activities and communications. This integration results in more accurate and context-aware role-based access control strategies.

Best Practices for Implementing AI-Driven Role Mining

When implementing AI-driven role mining, your organization should begin with the following actions:

1. Ensure data quality: Cleanse and validate all current identity data to ensure data quality before implementing an AI-driven role mining solution.
2. Start with a pilot program: Begin with a small-scale, pilot program implementation to identify and address any issues with the AI role mining solution before fully deploying a larger scale solution throughout your organization.
3. Combine AI insights with human expertise: Use AI recommendations as a starting point, but involve human business stakeholders in all final role definitions.
4. Regularly retrain and update AI models: Ensure your AI system adapts to organizational changes and evolving access patterns by regularly retraining and updating it.
5. Maintain transparency: Keep detailed logs of AI-driven decisions for auditing and compliance purposes.
6. Invest in user training: Educate IT staff and business users on how to interpret and act on AI-generated role recommendations.
7. Monitor and measure results: Continuously assess the impact of AI-driven role mining on security, efficiency, and compliance metrics.

Omada Brings Real-Time Readiness to Role Mining in Identity Governance

The State of Identity Governance 2024 report revealed that nearly four in ten IT professionals and business leaders consider AI based on ML for assistance in role mining a top functionality to have in a new IGA solution. As more organizations see the role AI based on ML plays in automating role mining, that figure is destined to rise.

As identity-related cybersecurity threats become more sophisticated, having AI-driven role mining to quickly identify roles is bound to evolve from a “nice to have” to a “must have” technology. Contact Omada to see how we can help you realize the potential of making AI based on ML for role mining a driver of your organization’s IGA strategy.