GOVERNING ADVERSARIALLY ROBUST ARTIFICIAL INTELLIGENCE IN CRITICAL INFRASTRUCTURE SECURITY OPERATIONS: THE ROLE OF BEHAVIORAL CLUSTERING IN ACCOUNTABILITY FOR AUTOMATED DEFENSE

The integration of artificial intelligence into Security Orchestration, Automation and Response platforms has expanded the capacity to detect, prioritize, and respond to cyber incidents in critical environments. However, in sectors such as energy, healthcare, transportation, telecommunications, and public services, automated cyber defense may create significant operational risks when incorrect decisions block legitimate users, isolate essential assets, or interrupt services that depend on continuity. This article analyzes, based on scientific literature, how behavioral clustering can function as both a technical and governance layer in AI-SOAR systems applied to critical infrastructure protection. It argues that clustering models applied to audit logs, network traffic, and user and entity behavior patterns can create contextual baselines, support false-positive reduction, and establish verifiable limits for automation. The analysis shows that adversarial robustness depends not only on algorithmic performance but also on traceability, explainability, independent audit, human review, and formal documentation of automated decisions.