Detecting Cybersecurity Threats by Integrating Explainable AI with SHAP Interpretability and Strategic Data Sampling

Explainability is operationally essential in cybersecurity: The paper demonstrates that security analysts need to understand why an AI system flagged a threat as suspicious. By using SHAP interpretability analysis, the framework provides "actionable explanations" that allow human operators to verify and trust AI recommendations rather than treating them as black boxes.

Data quality directly impacts trustworthiness: The research identifies "Automated Data Leakage Prevention" as critical for experimental integrity, systematically removing contaminated features that could artificially inflate model performance. This finding suggests that responsible AI deployment requires rigorous data governance—not just accurate algorithms.

Efficiency and transparency aren't trade-offs: The framework successfully reduces "computational overhead" while maintaining detection performance through Strategic Sampling that preserves class distributions. This challenges the assumption that explainable AI systems must sacrifice speed or accuracy, making responsible AI more feasible for real-world security operations.

Transparency enables accountability in high-stakes decisions: In security operations centers where AI recommendations can trigger significant responses, the paper emphasizes that "decision transparency is paramount." This underscores why explainability matters beyond compliance—it's fundamental to maintaining human oversight and accountability in critical infrastructure protection.