Identity governance and administration (IGA) activities eat up time and money for enterprise organizations. It takes time and effort compiling employee access to multiple systems, cross-referencing records, importing HR updates, staying up-to-date on company identity policy. It’s often inefficient, imprecise, and slow, even if it is an important pillar of the company’s cybersecurity posture.

The new wave of generative AI, large language models, and machine learning capabilities offers a beacon of hope to overworked, understaffed, and sometimes underappreciated identity and security teams. That trend has already taken a foothold. According to a survey of CISOs referenced by the World Economic Forum, “35% reported that they are already experimenting with AI for cyber defence, including malware analysis, workflow automation and risk scoring.”

New IGA solution providers, like BalkanID, are working to embed artificial intelligence into their products, for reasons that we’ll explore further in this article. Employing AI and directing it at many of the repetitive or manual tasks associated with IGA can help companies achieve transformative efficiency gains, simplify access to complex datasets, building and automating workflows while uncovering hidden relationships within identity data.

Efficiency Gains through AI

One of the most immediate benefits of AI in the realm of IGA is its capacity to replace repetitive, manual tasks with intuitive and automated processes. AI excels at handling large volumes of data quickly and accurately using simple, human language queries, which means tasks that previously took hours or days can now be completed in minutes. This not only speeds up operations but also frees up valuable human resources for more strategic, high-level tasks that require human insight.

Key Areas of Efficiency Improvement:

• Automated User Access Reviews: AI can automatically prioritize and action on user access reviews based on it’s understanding of historical actions taken by the user coupled with it’s analysis of the user’s peer group.  
• Real-Time Risk Assessment: With AI, organizations can monitor and evaluate identity risks in real-time, providing proactive responses to potential threats.

Identifying risks and threat intelligence analysis are the top two generative AI use cases for cybersecurity, according to Splunk’s State of Security 2024 report. These two capabilities are connected strongly to identity governance and administration activities, underscoring AI’s importance to efficiency, prioritization, and risk reduction.

Simplified Access to Complex Data Sets

AI technologies, particularly those employing natural language processing, allow users to interact with complex identity datasets without the need for technical expertise. Employees across an organization, regardless of their technical background, can query vast datasets using simple language and receive insights in a straightforward, digestible format.

Enhancements Provided by AI

• Natural Language Data Querying: Non-technical users can gain data insights through simple questions and commands without the need for technical knowledge of advanced filters, SQL, or other languages.
• Data Visualization: AI tools can automatically generate visual representations of data, making it easier for stakeholders to understand and make informed risk-based decisions.

Uncovering Hidden Relationships in Identity Data

Perhaps one of the most advanced capabilities of AI in IGA is its ability to analyze and visualize connections within large and complex datasets that might be overlooked by human analysts. By leveraging machine learning models and algorithms, AI can detect patterns and relationships that are not immediately obvious, offering insights that can lead to improved security visibility and control.

Impact of AI in Data Analysis

• Predictive Analysis: AI can predict potential security breach scenarios by analyzing user behavior and access patterns.
• Enhanced Threat Detection: By understanding the relationships and interactions between different data points, AI can more effectively identify anomalies that may indicate a security threat, allowing organizations to get ahead of gaps they may not have been aware of.

Building and Automating Workflows

The automation capabilities of AI extend beyond simple task execution to complex workflow creation and management. AI can identify inefficiencies and optimize workflows in real-time, ensuring that identity management processes are as streamlined as possible.

Automation Examples in IGA

• Policy Enforcement: AI systems can automatically enforce security policies, like violations of multi-factor authentication (MFA) policies, across an organization. Because policies have clearly defined parameters, tools employing artificial intelligence can respond accurately to situations that violate those rules.
• Provisioning and Deprovisioning: AI can manage the lifecycle of user identities, from initial creation to eventual deactivation, with minimal human intervention. With inputs from HRIS tools and other sources of data, AI tools can automatically respond to employee events like intercompany moves, terminations, and other events that can affect access to systems and data.

Pushing Identity Governance and Administration Forward

Applying AI to identity governance and administration offers significant advantages for today’s organizations, especially those with complex environments and large, global employee bases. By automating repetitive tasks, simplifying access to complex data, enhancing workflow efficiency, and revealing hidden data relationships, AI not only boosts productivity but also strengthens an organization's security framework. As AI technology continues to evolve, its integration into IGA processes will become increasingly essential, transforming them from necessary overheads into dynamic, strategic assets that drive successful business outcomes.