AI & Identity & Access Management (IAM) – Between Innovation and Risks

80% of cyberattacks use identity-based attack methods, according to CrowdStrike. Rightly so, attackers don’t need to "hack in" when they can simply log in using stolen credentials, abused session tokens, or compromised access controls. The real challenge? Most organizations still rely on outdated authentication methods and poor identity hygiene, making them easy targets. CyberArk indicates that 99% of security leaders believe they will experience an identity-related compromise in the next year. 

Recently, AI-driven identity and access management (IAM) have filled some gaps, transforming authentication from a one-time event to a continuous, adaptive security process. AI-driven IAM systems are smarter, more adaptive, and capable of real-time decision-making, making authentication both stronger and seamless

Traditional access control relies on predefined policies, but AI eliminates the need for static rules by introducing dynamic, contextual decision-making. Machine learning models analyze behavioral patterns, access history, and contextual data to determine whether a user’s request is legitimate. If an employee tries to access sensitive financial records outside standard working hours, AI can flag the activity, trigger multi-factor authentication (MFA), or block access altogether—without human intervention. For organizations operating in SaaS, multi-cloud, or hybrid cloud environments, AI-driven access decisions reduce friction while improving security, and ensuring compliance with regulations like HIPAA and GDPR.

The promise of AI-driven identity and access management (IAM) is compelling—automated decisions, real-time adjustments, and a seamless security experience. However, as organizations hand over more control to AI, a new set of challenges emerges. Over-reliance on automation introduces risks that, if left unchecked, could lead to serious security breaches, regulatory violations, and insider abuses.

Unlike human decision-makers who consider context, intent, and business impact, AI processes request based on predefined rules and historical data patterns. If an AI system misinterprets an access request—whether due to a misconfiguration, a compromised user account, or an attacker exploiting the system’s logic—it could grant unauthorized access to sensitive systems, bypassing traditional security measures entirely. If an AI-based IAM lacks sufficient verification layers, the system could approve these requests, giving attackers unrestricted access to customer databases, financial records, or proprietary code repositories—all without triggering alarms.

In environments where AI directly provides and de-provisioning access, such miscalculations could escalate quickly. A single misjudged access approval could allow an attacker to establish persistent backdoors, exfiltrate confidential data, or manipulate critical infrastructure, leading to regulatory fines, reputational damage, and financial loss.

Beyond security risks, AI-driven IAM must also navigate the maze of regulatory compliance. Financial institutions, healthcare providers, and government agencies operate under strict access control laws, including GDPR, HIPAA, SOC 2, and PCI DSS. Any unauthorized access to sensitive data—whether accidental or malicious—can result in heavy penalties and reputational damage.

On top of these aspects, AI-driven IAM introduces a transparency challenge. Many AI models operate as black boxes, meaning security teams cannot easily explain why access was granted or denied. This lack of transparency poses a serious problem in compliance-driven industries, where auditors must be able to trace every access decision to a clear, documented justification. If a regulatory body demands an audit trail for sensitive access approval, security teams must prove why the AI system made a specific decision. But what if the decision was based on correlations even the IT department doesn’t fully understand? Without explainable AI (XAI) and clear documentation, organizations risk non-compliance, legal disputes, and regulatory penalties.