EXPLAINABILITY AS A TRUST INFRASTRUCTURE: XAI FRAMEWORKS FOR AI-SOAR ANALYST DECISION SUPPORT
DOI:
https://doi.org/10.56238/rcsv14n6-010Keywords:
Explainable Artificial Intelligence, SOC, AI-SOAR, Analyst Trust, CybersecurityAbstract
The adoption of artificial intelligence in Security Operations Centers has expanded the capacity to correlate, prioritize, and triage alerts in environments integrated with SOAR platforms. However, triage automation does not eliminate a central limitation of contemporary cyber defense: analysts must understand why a given alert was escalated, why similar events were treated differently, and how the behavior of the monitored environment has changed over time. This article proposes a conceptual explainable artificial intelligence framework for AI-SOAR workflows, organized around three interpretable layers: local explanations, contrastive explanations, and temporal explanations. Local explanations support confidence for immediate action; contrastive explanations strengthen pattern recognition between malicious and benign events; and temporal explanations enhance situational awareness by interpreting sequences, baselines, and attack trajectories. As an extension, the article discusses the use of large language models as explanatory interfaces capable of translating structured XAI outputs into plain-language rationales without replacing human judgment. The article argues that explainability can function as an infrastructure for trust, auditability, and supervision in AI-SOAR escalation workflows.
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