It’s no surprise that AI is being integrated into identity governance and administration (IGA) platforms. Automation promises productivity boosts, risk detection can be in real-time and cloud environments allow greater scalability. What’s more, the pace of AI means IGA is quickly moving beyond slower, more rigid, rule-based approaches.
It’s also no surprise that malicious actors are harnessing AI capabilities in similar varieties, volumes and velocities. From automating phishing at scale with LLMs to using generative AI to launch credential theft attacks at unprecedented speed, hackers can gain elevated access to hybrid environments where lateral movement often remains undetected. That’s why it matters that AI implementations within IGA are hardened to match this evolving AI-assisted threat landscape.
Why AI in IGA matters
IGA platform users can’t afford to miss out on AI’s advantages such as access recommendations, role analysis and behavioral monitoring. However, those users now come with identities that are often remote, distributed and require access from beyond the traditional perimeter. When these identities are breached, any existing visibility silos allow attackers to penetrate deep into networks. The escalating potential for damage is why Gartner highlights human and machine identities as “the primary attack surface.”
The growing identity-based attack surface comes from both humans and non-human identities (NHIs). AI is a key driver of the rise of NHIs that need access and authentication, and that now outnumber human colleagues by 82 to 1. This level of complexity is asking new questions of access environments, with AI offering some of the answers while also raising further questions for IGA.
For example, an NHI can continuously monitor business-critical systems, but any always-on endpoint also needs to have always-on security. Especially because there’s a “growing number of devices, identities and tools across perimeters,” ranging from around 7–30%.
Further opportunities and vulnerabilities come from the rise in agentic AI, with 90% of the Fortune 500 using Copilot Studio to build AI agents and automations. Naturally, the IP within those types of organizations make them a prime target for threat actors deploying AI-led attacks.
But AI can bring just as many advantages to those big companies, and indeed to any enterprise who wants to bring real-world effectiveness to their IGA strategies.
What AI does well in IGA
AI-assisted reporting takes the load off administrators who previously managed identity lifecycles manually. Approving, revoking and limiting access can be done dynamically, based on live requests with role-based and attribute-based granularity.
AI-based monitoring can check for orphaned or dormant accounts, with revalidation reminders automatically sent to resource owners. Recommendations and actions are managed throughout the identity cycle, creating a complete audit trail needed for evolving privacy laws.
AI can also bring context-awareness to IGA for reducing risk and limiting exposure. Behavior analysis hardens security across the environment by building up AI-algorithmic understanding of user and role activities. Over time, these signals establish a model of expected behaviors and risk scores – for example, where users log in, what time, and on what device. Any anomalies or deviations from the established norms can automatically trigger a further step for authentication, isolate devices or instant lockout.
AI allows this to happen in real-time, solving the bottlenecks that can appear when employees are left waiting for service desk approval. Of course, when systems are operating autonomously in this way, gaps and vulnerabilities are more likely to appear both in the attack surface and between the AI capabilities that have been promised to buyers and vendors.
The gap between hype and reality
The use cases so far for AI in cybersecurity show that the technology can augment human expertise. For example, in analyzing data from first- and third-party sources, at high-volume, and working on security and IT tasks in ways that would be beyond traditional teams. Another AI-driven win involves automating lifecycle management for human identities and NHIs. This removes the need to manually keep track of individual employee role changes or organizational exits – a repetitive task that can easily result in human error.
Of course, data is the common theme that runs, literally, through the enterprise. And that’s where “Garbage In, Garbage Out” can limit AI-driven gains for IGA. There’s no point configuring an AI to manage access if the user credentials aren’t up to date. And if identities aren’t categorized correctly, there’s the risk of false positives and false negatives.
For example, imagine an NHI is accidentally registered as a human worker. An AI may log huge numbers of API calls being made at high velocity and revoke access based on behavior that goes outside a typical employee’s activity. Whereas a human cybersecurity professional would look at the data and recognize that the numbers are impossible for anything other than an NHI. It’s this real-world experience that underscores why any multi-layered strategy has a continued need for human review and judgment. Without oversight, the AI will continue to learn from bad data and repeat the mistakes.
Another bad data driver is the lack of unified identity visibility. That may be down to hybrid AD and Entra ID environments or legacy-based interoperability issues with multiple vendor tools and connectors. Whatever the cause, lack of high-quality data impacts AI capabilities. For adversaries also using AI, this opens the door to potential breach opportunities.
Attackers are using AI too
The barrier to using malicious AI tools is low. What’s more, AI in the hands of attackers doesn’t have the guardrails that mainstream AI vendors do. For example, first there was ChatGPT. Then along came jailbroken or uncensored LLMs such as FraudGPT and WormGPT, built for cybercriminals to evade “the built-in safeguards of legitimate AI platforms.”
Much like OpenAI’s product, these dark AI versions are subscription-based, offer chat windows for entering questions and are trained on volumes of data. However, the training data comes from malicious code repositories, phishing templates and relevant dark web content. Threat actors who previously sent out badly translated emails can now use these tools and other variants to generate millions of messages in natural-sounding, localized language.
AI is also fueling a rise in credential theft, up 160% in 2025. These AI-driven cyberthreats focus more on psychology and social engineering, where targets voluntarily give up their credentials and offer access, allowing attackers to bypass traditional forms of defense controls. These vectors used to be a resource-intensive method for human attackers who would have to research targets’ personal information before making contact. That’s all changed in the modern threat landscape, where “AI-generated phishing campaigns now succeed at rates more than four times higher than their traditional counterparts.”
AI is also used for identifying vulnerabilities across networks, using automated scanning at unprecedented speeds. Any successful exploits can then lead to privilege escalation before defenses and cyberanalysts can react. And it’s not just IT networks that are being affected.
AI is also being implemented within operational technology (OT) across supply chains and utilities. CISA, FBI, DC3 and the NSA have said they “strongly urge critical infrastructure asset owners and operators to implement mitigations” that range from disconnecting OT and industrial control systems to implementing phishing-resistant MFA for network access. To mitigate network weaknesses, security leaders must explore methods to ensure AI is deployed for identity security.
The path forward for AI identity security
With AI taking over more of the routine and repeatable tasks, humans should, in theory, be freed to focus on strategic initiatives. However, many autonomous systems remain far from mature. For now, it’s a case of augmenting teams with AI before going all-in on automating.
This approach builds a foundation for identity security controls where AI’s advantages and capabilities are combined with human experience and knowledge. AI can take care of low-risk access requests, while For higher-risk and elevated privilege accounts, managers can bring real-world rationale to inform decisions on how and when to allow or deny requests. With role-based and attribute-based models of access control, organizations can ensure access processes are dynamic while also supporting Zero Trust and Principle of Least Privilege.
AI can also be deployed for continuous monitoring of behavioral patterns, trigger alerts over unused or expiring resources, and analyze identity activities. Analysts can focus on edge cases, ensure alignment with data and privacy legislation, and be accountable for strategies relating to IGA.
They do, however, need a centralized and single pane of glass where dashboards ensure visibility across on-premises and cloud-based systems for security and as evidence to regulators.
AI-powered IGA for visibility, not vulnerability
Where there’s AI innovation, there’s likely to also be AI disruption. Yet governance is one function where a disrupted workflow isn’t just bad for business, it can lead to non-compliance fines, reputational damage, financial loss and customer attrition. However, despite the variety of authentication methods, AI-based defense tools are only as strong as the human-led procedures which govern them.
At a time when access environments are increasingly complex, identity is a primary attack surface, with users – human and non-human – requiring different types of access. AI is both powering this shift while maintaining its position as an essential tool for IGA. It allows IT and security leaders to manage access at scale and with consistency, provide reporting for decision-making, and establish risk scores and baseline levels. Of course, clean data, along with human expertise and oversight, is key to realizing its potential for businesses.
The pace of business today means employees need always-on access to data, resources and apps. This is reflected in the speed of AI implementation, where there’s little time for a gradual approach. Manual methods cause bottlenecks, slowing business operations and overloading talent that’s already stretched. Gaps can appear for threat actors to exploit, using similar AI-assisted tools.
That’s why, for now, AI needs support to turn identity into security: Augmenting the existing workforce and only automating areas that will improve productivity and harden security. In practice, this means a realistic and fundamental mix of human expertise and dedicated identity and access management tools and platforms.
