A recent TechRadar Pro article explains that the rise of autonomous AI agents in enterprise systems is fundamentally upending traditional security models, especially identity-and-access management (IAM) frameworks built on static permissions. Classic approaches like Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC) are premised on predictable, rule-based behaviour, where you can clearly define which user or system can access what resource. But modern AI agents don’t operate that way — they act on intent and reasoning to achieve goals, meaning they may infer, combine, or reconstruct sensitive information without ever explicitly breaching defined access rules.
The article illustrates this with scenarios in which an AI agent tasked with improving customer retention correlates non-sensitive system logs, support tickets and purchase histories to identify individuals at risk of churn. Although such an agent had no explicit access to personal data like names or account numbers, it effectively re-identified users through inference, sidestepping access controls not because of a breach, but through context-driven reasoning. This phenomenon has been described as “contextual privilege escalation”, where meaning and purpose — rather than permissions — become the real vectors for data exposure.
Because AI agents operate adaptively and dynamically, traditional security tools may appear compliant while policy boundaries quietly erode. As the article explains, multiple agents interacting with each other — sharing outputs, interpreting contexts, and chaining tasks — can gradually drift from their original authorization scope, forming unintended access paths even when each step seems legitimate on its own. This drift undermines static access models and creates risks that go unnoticed until significant exposure has occurred.
To address these emerging threats, the piece argues that organizations must shift from governing “access” to governing “intent”. Recommended safeguards include binding actions to the original human context, adopting dynamic authorization that adapts to runtime circumstances, tracking provenance across agent interactions, and maintaining human oversight for high-risk actions. In effect, security systems need to evolve from static permission checks to adaptive, intent-aware governance frameworks that can monitor and audit how agent reasoning unfolds across environments — a major departure from legacy IAM approaches.
