The Role of Explainable AI in Security Systems: A Survey of Transparent Threat Detection

The profound integration of Artificial Intelligence has irrevocably transformed the architecture of both cy-ber and physical security, delivering unparalleled an-alytical velocity in the face of escalating global threats. Yet, the industry’s historical reliance on opaque, black-box deep learning models precipitated a critical oper-ational vulnerability, manifesting most destructively as widespread alert fatigue among frontline security per-sonnel. This comprehensive survey and architectural proposition unequivocally demonstrate that Explain-able Artificial Intelligence is not merely a supplemen-tary interface enhancement, but an urgent, founda-tional requirement for the sustainable deployment of automated threat detection. By systematically applying feature attribution frame-works within highly structured, state-aware pipelines, mathematical abstractions are successfully translated into actionable, context-rich intelligence. In the digi-tal theater, this transparency enables analysts to cir-cumvent cognitive paralysis, facilitating instantaneous triage. Concurrently, in physical environments, it de-mystifies complex video analytics, empowering human operators to execute justified interventions. Mandat-ing the integration of these transparent layers as a core component of all threat detection pipelines represents the definitive pathway to ensuring modern security sys-tems remain operationally trusted, legally accountable, and uncompromisingly effective.

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