Contextual Explainability in AI Security Systems: Bridging Analyst Trust and Automation in SOCs

Abstract

Rising Complexity of Cyber Threats And Surging Number Of Security Alerts Leading To Escalating Use of Artificial Intelligence (AI) within SOCs. Yet the black box nature of a majority of AI-based detection and response models erodes analyst trust so cannot be operationally deployed. In this work we examine contextual explainability as a conduit between automation and human-sympathetic understanding in AI security systems. Compared to traditional XAI methods that concentrate exclusively on feature-level explanations, contextual explainability injects information about the context, situation, behavior and temporal features for AI decisioning purpose. By incorporating explainability into the wider context of security—for example, user behavior baselines, network topology and event correlation—SOCS analysts are better able to evaluate whether AI-generated alerts are credible and relevant. In this paper, we submit a hybrid explainability approach that integrates (a) modelagnostic interpreters (SHAP, LIME), and ‘contextual’ elements (b) based on knowledge-aware concepts as available from investigation support tools and threat intelligence ontology repositories. The new model seeks to drive greater transparency, minimize false positives, and promote human-machine teaming among the analysts and security systems. We validate our experimental results in terms of SOC scenarios, showing an increase on analyst confidence, reduction on triage time and improvement of the resilience during operations. The results indicate that contextual explainability represents a key facilitator for the development of a trustworthy, human-aligned AI in cybersecurity.