EXPLAINABLE ARTIFICIAL INTELLIGENCE FOR CYBERSECURITY: TECHNIQUES, APPLICATIONS, CHALLENGES, AND FUTURE RESEARCH DIRECTIONS

Artificial intelligence (AI) has become an important component of modern cybersecurity systems because of its ability to identify suspicious activities, recognize complex patterns, and support automated threat detection. Machine learning and deep learning algorithms are increasingly applied to network intrusion detection, malware classification, phishing detection, and anomaly identification. However, many advanced AI models operate as black boxes, making it difficult for security professionals to understand why a particular activity has been classified as malicious or legitimate. This lack of transparency can reduce trust in automated security systems and complicate the investigation of cybersecurity incidents. Explainable artificial intelligence (XAI) addresses this challenge by providing human-understandable explanations of model predictions. This paper presents a review of explainable AI techniques and their applications in cybersecurity. It examines interpretable machine learning models, feature importance analysis, Local Interpretable Model-Agnostic Explanations (LIME), and SHapley Additive exPlanations (SHAP). The paper also discusses the benefits and limitations of integrating explainability into cybersecurity operations, including computational overhead, explanation reliability, adversarial manipulation, privacy concerns, and the balance between predictive performance and interpretability. Finally, it identifies research opportunities involving real-time explanation systems, privacy-preserving learning, human-centered security interfaces, and robust evaluation frameworks. The review indicates that explainability can support more transparent and accountable cybersecurity decisions, although explanations should be validated and used alongside conventional security controls rather than treated as proof of a model’s correctness.