How AI Is Impacting Identity and Access Management (IAM)

Identity and Access Management (IAM) incorporates various technologies and methods to help organizations ensure that human users and machines gain access to the assets required to fulfill their roles. IAM is critical to maintaining the confidentiality, integrity, and availability of systems, applications, and data. It enables organizations to satisfy industry-specific regulatory requirements and reduce the size of the cybersecurity threat landscape particularly in the areas of insider threats and data breaches.

Beyond mitigating insider threats and data breaches, IAM also plays a crucial role in limiting lateral movement in the event of a breach, preventing attackers from escalating privileges and accessing additional systems within the network.

A growing number of organizations have recently started to integrate Artificial Intelligence in IAM solutions to optimize efficiency and Artificial Intelligence (AI) is quickly becoming a critical technology in helping mitigate the risk of identity-related cybersecurity incidents.

In this post, we’ll highlight current trends in the evolution of AI in IAM and suggest how organizations can apply new and existing AI capabilities to IAM solutions. You’ll see how the integration of AI in IAM is improving efficiency and driving revolutionary improvements in IAM and Identity Governance and Administration (IGA) more broadly.

The Impact of AI in IAM

As organizations are integrating AI in IAM to achieve more efficiency from their identity and access management tools, here is where AI is making the biggest impact:

Intelligent Monitoring and Identity Analytics

Legacy and in-house developed IAM tools and even some modern IAM tools lack the capacity to monitor user activity across diverse IT infrastructures. Even in organizations where IAM tools are sufficiently robust to monitor all user activity in a modern enterprise, they frequently fall short in analyzing the information they collect and using it to improve identity security posture. Integrating machine learning and AI in IAM enables organizations to analyze large amounts of user activity data to detect user behavior patterns and apply this intelligence to simplify provisioning and maintenance of robust identity management.

Natural Language Processing Interfaces for Access Control

Natural Language Processing (NLP) is a feature of AI that enables machines to understand, interpret, generate, and respond to human language. NLP combines linguistics, machine learning, and computer science to process and analyze text or speech data. An NLP-powered natural language interface built into an IAM tool helps organizations to adjust authentication methods and access permissions dynamically based on real-time data. The NLP-enhanced interface allows admins and decision-makers to authenticate and approve access requests in an intuitive communication platform with the look and feel of a messaging app with which most users are very familiar.

Generative AI Applications

AI and machine learning (ML) drive more informed decision-making across identity lifecycles. ML performs cluster analysis to determine context and recommendations for new and updated roles that better align with the needs of the workforce in an organization. ML algorithms collect and crunch data and generative artificial intelligence in IAM operationalizes it to offer access control suggestions to administrators and approvers within a modern system. Generative AI in IAM improves role modeling and access requests, approvals, and certifications processes and provides the relevant human-readable context to eliminate unnecessary complexity, reduce confusion, and mitigate certification fatigue in an organization. Chat-assisted AI enables users to quickly request access; and managers can easily approve or reject requests, making the process more efficient and eliminating rubber-stamping approvals.

Benefits of Artificial Intelligence for IAM

Improved Security Posture

AI in IAM enables cybersecurity teams to analyze large amounts of user activity data and proactively detect and mitigate potential insider and external threats.

Operational Efficiency

AI helps users “serve themselves” in completing identity management tasks. This reduces the need for manual administrative intervention and streamlines processes. An AI-powered NLP-enhanced interface also enables administrators and decision-makers to authenticate and approve access requests in an intuitive environment.

Enhanced User Experience

Automated recommendations and chat-assisted AI reduce the learning curve associated with access requests and approvals, driving efficiencies in the onboarding process and empowering IT administrators and users to be more productive from day one. Rather than compelling new users or users changing roles to engage a UI that they may never see again, they can secure access using familiar AI-powered interfaces.

Simplified Auditing and Regulatory Compliance

Ensuring compliance without restraining business efficiency and collaboration is an ongoing challenge. Using AI powered solutions in IAM tool makes cybersecurity audits simpler and ensures organizations can easily demonstrate compliance with industry-specific regulatory standards.

Challenges in Implementing AI for IAM

Data Privacy and Compliance

When deploying AI in IAM, administrators must pay close attention to their automated process to ensure the algorithms are working as intended. AI in IAM analyzes personally identifiable information (PII) that must be protected against cyberattacks. As organizations onboard new assets, they must ensure their AI-enhanced IAM tools are analyzing and protecting the PII they are adding. Organizations must also account for the fact that data protection regulations constantly evolve; data not considered sensitive today may well be tomorrow. For more insights on AI-driven data privacy and compliance, download Omada’s Compliance Kit.

Integration with Existing Systems

Many organizations have determined that using legacy systems to apply access controls and manually review user access behavior across new platforms like SaaS applications is an unsustainable process, but integrating AI technologies with legacy IAM infrastructure presents significant challenges. While AI algorithms turn the insights they uncover into actionable information that organizations can use to optimize their identity security strategy, to make it all work seamlessly they must apply a defined best practice standard process to ensure the adopted system can be effectively integrated with existing technology and satisfy organizational business and security requirements.

Continuous Monitoring and Adaptation

As cybercriminals deploy AI in more sophisticated ways to gain access to sensitive applications and data, for AI in IAM to help mitigate these evolving threats, organizations must commit to ongoing monitoring and adaptation of AI models.

Future Trends in AI-Driven IAM

To this point, the integration of AI in IAM tools has created significant efficiencies in identity governance and access control practices and will continue to help organizations improve their identity-related cybersecurity management posture. Here are a few trends to watch:

Partially Autonomous AI Agents

A partially autonomous AI agent operates independently in some respects but still requires human supervision, intervention, or approval for critical decisions. It strikes a balance between full automation and human control, making it useful in scenarios where full autonomy is risky or impractical, such as changing industry-specific regulatory frameworks. Partially autonomous AI agents hold significant upside for improving IAM performance in the future.

Predictions for the IAM Landscape

In 2024, typical enterprise organizations use an average of 371 SaaS applications, a 32% increase over 2023. As the number of SaaS applications continues to rise, the need for more and better AI-driven IAM tools to manage permissions, access control, provisioning and deprovisioning effectively in these environments will intensify. NLP will be crucial in providing organizations with a standard interface for users to “self-serve” access requests; and administrators will rely on NLP to efficiently manage the foundational functions associated with effective identity access management as their IT infrastructures grow. Organizations will also need to rely more heavily on AI-driven IAM tools for user activity monitoring and behavior analytics as the cybersecurity threat landscape becomes more complex.

Conclusion

As we can see, integrating Generative AI in IAM holds significant potential for helping organizations monitor and analyze user activity to make better identity-related decisions and maintain regulatory compliance. AI-powered NLP enables administrators to use familiar communications platforms to more easily authenticate and approve access requests. As organizations adopt AI in IAM, they must account for keeping up with changing regulatory compliance frameworks and plan for the challenges of integrating AI with existing IAM tools. As IT infrastructures grow, AI will help organizations create IAM best practices enabling them to take on the dynamic cyberthreat landscape.

Where to Learn More

To gain insights into how organizations can successfully integrate AI in their IAM strategies to enhance security and efficiency, watch Omada’s on-demand webinar, The Future of Identity Governance: How Generative AI Will Transform IAM in 2025.

See how Omada is partnering with Microsoft to leverage Semantic Kernel and create identity security-focused AI assistants that drive automation, enhance decision-making and deliver highly personalized and contextual user experiences.