Generative AI is a type of artificial intelligence (AI) that can create new content, such as text, images, music, code, videos, etc. using data on which it has been trained. Generative AI models are typically powered by deep learning techniques like neural networks that mimic the way human brains process information and transformers (e.g., GPT) that generate text generation and understanding.
In Identity and Access Management (IAM), Generative AI helps create more intuitive interfaces, brings flexibility to identity governance, and helps tools adapt more easily to the behavior and requirements of administrators and users. In this post we’ll more clearly define Generative AI, articulate the specific implications of Generative AI and Natural Language Processing (NLP) in the context of AI and suggest how organizations can leverage these technologies to make IAM tools more efficient and scalable.
Differences between AI and Generative AI
Unlike traditional AI, which focuses on recognizing patterns and making decisions, Generative AI (GenAI) can produce original and human-like outputs. GenAI drives natural language processing and is particularly helpful in creating an improved user experience. Broader AI models such as predictive identity analytics can exist independently of GenAI in IAM tools to improve efficiency and user experiences in various application interfaces.
Main Benefits of Generative AI in IAM
GenAI helps provide a range of enhancements to IAM tools for both administrators and users. Here are the main areas of IAM in which GenAI can be instrumental:
Better User Experience
Tools in traditional IAM systems may each use a different interface. To achieve the outcomes the tools are designed to produce, administrators and users must work in these discrete interfaces with which they may be unfamiliar. This increases the risk of user errors, even with the most effective IAM tools. Chat-based natural language interfaces used in environments like Microsoft Teams or Slack are more intuitive. When these GenAI-powered interfaces are integrated into IAM tools, the core functionalities they offer become easier and less error-prone to execute.
Simplified Access to Relevant Data
Users and administrators can use chat-based natural language interfaces to freely query Generative AI on a wide variety of relevant details such as access request history and user behavior to improve IAM efficiency.
Flexible Natural Language Processing
Generative AI considers multiple factors to interpret complex actions and behaviors, making it particularly useful for complicated provisioning, de-provisioning, access management, and other critical Identity Governance and Administration (IGA) tasks.
Improved Clarity for End Users
Generative AI enables end users to ask questions and get accurate responses about their own access levels in a conversational format. GenAI-powered interfaces provide relevant responses to queries; offering users instant answers not only about what assets they are restricted from but also why they may or may not have access to specific systems or sub-systems.
Benefits of GenAI for Operations and Efficiency
Faster Access Reviews
Generative AI can help access program managers accelerate periodic audits to validate that access rights align with job responsibilities, such as ensuring that when users no longer need access to an organization’s IT infrastructure or when users’ roles change their access rights are promptly updated.
Reduced Administrative Overhead
GenAI-power capabilities help reduce the manual intervention required to complete identity management tasks and streamline processes. For example, an NLP-powered natural language interface built into an IAM tool helps administrators dynamically adjust authentication methods and access permissions based on real-time data.
Simplified Regulatory Compliance
Generative AI brings a level of flexibility to IAM tools that ensures industry-specific regulatory compliance. Regulations and standards frameworks such as DORA, NYCRR, NIS2 and CMMC 2.0 mandate strict controls over sensitive data and IT systems. Integrating GenAI chat-driven collaboration tools into IAM enables users to request access rights using natural language and comply with these frameworks. Approvers benefit from machine learning (ML) insights like peer-group comparisons and historical request outcomes to facilitate more informed, faster decisions. Generative AI helps automate low-risk decisions over time while maintaining human oversight. Using GenAI-powered tools in this context helps organizations balance automation with transparency and ethical governance; ensuring ongoing organizational compliance.
Practical Tips for Implementing Generative AI in IAM
Identifying Suitable Use Cases for Generative AI in IAM
The first step an organization must take is to assess in which areas Generative AI could enhance its IAM tools and IGA solution overall. Here are typical use cases to consider:
Enforcing Complex Identity Lifecycle Management Policies
Generative AI can help organizations manage the entire lifecycle of user, machine, and AI-generated identities within an organization. GenAI chat-driven collaboration tools can simplify joiner-mover-leaver processes and enable organizations to monitor and control access rights according to defined roles, rules and policies.
Managing Complex Provisioning and Deprovisioning Needs
Use GenAI to automate the configuration and deployment IT resources, such as servers, virtual machines, storage, network components, and applications; and rapidly provision and de-provision access while reducing the need for manual intervention.
Remote Workforce User Access Management
In regulated industries that require employees to have minimal access at all times, Generative AI brings the flexibility to identity governance needed to help IAM tools more easily adapt to remote administrators’ and user’ behaviors.
Integration Compatibility
Using legacy IAM infrastructure to apply IAM tools with Generative AI across new platforms like SaaS applications presents significant challenges. To ensure Generative AI can present insights that translate into actionable information that optimizes their identity security strategy, organizations must employ a defined best practice standard process to facilitate integration with complicated on-prem solutions or other auxiliary software, such as Privileged Access Management (PAM).
Mitigating Risks of Generative AI Adoption in IAM
Integrating Generative AI in IAM tools changes the risk matrix for some core identity governance functions. Here are factors you need to consider during the GenAI adoption process:
Limiting Privacy Risks
Organizations must ensure that their implementation of GenAI in IAM tools is airtight and changes do not afford potential intruders new attack vectors on sensitive systems. The introduction of Generative AI-powered features must not affect compliance with essential data privacy regulations.
Guarding Against Hallucinated Information
In AI, a hallucination refers to the generation of incorrect, misleading, or entirely fabricated information that is presented as fact. Generative AI can create hallucinated information. To account for this, an organization must deploy ironclad models based on existing robust IAM tool setups that will not produce false information.
Future Developments in Generative AI for IAM
GenAI is expected to shape the future of user experience with IAM tools and continue to simplify IGA in important ways. Here are a few developments to watch:
Intelligent Identity Lifecycle Automation
Instead of manually assigning roles and permissions, GenAI will enable the prediction of the correct access rights based on job roles, past behavior, and policies. It will also detect anomalies in access requests and flag potential risks in real time.
Adaptive and Context-Aware Authentication
Generative AI will continue to enhance risk-based authentication by analyzing behavior patterns and dynamically adjusting authentication requirements. It will generate intuitive explanations for security policies, so users understand why certain authentication methods are required.
Reduced IAM Complexity for Non-Technical Users
Generative AI will offer more intuitive explanations of IAM concepts in simple terms and make it easier for non-technical users to understand security measures. Provided with these insights, more employees, contractors, and partners will be able to effectively troubleshoot access issues without requiring IT intervention.
Conclusion
Generative AI and GenAI-powered NLP in particular are poised to help the organizations that adopt these technologies become more proficient at making their IAM tools more efficient and scalable. Chat-based natural language interfaces simplify access to relevant data and make user experiences more intuitive and productive. Generative AI helps facilitate access reviews to reduce operational and administrative overhead and simplify compliance. To realize the manifold benefits of integrating GenAI in IAM tools, organizations must ensure they start with proven robust models and work on the use cases that stand to gain the greatest efficiency and ensure they maintain data privacy during the integration process.
Organizations can look forward to Generative AI continue to bring more value to their IAM tools by making them more intuitive and faster to use, especially for non-tech savvy users and as their identity management landscape grows.
