Explainable AI for Adaptive Enterprise Cybersecurity with Secure Cloud-Native Threat Intelligence Frameworks
DOI:
https://doi.org/10.21590/Keywords:
Explainable AI, Enterprise Cybersecurity, Adaptive Security, Cloud-Native Security, Threat Intelligence, Machine Learning, Cyber Threat Detection, Security Analytics, SHAP, LIME, Cloud Security, Anomaly Detection, Automated Incident ResponseAbstract
Enterprise cybersecurity environments are becoming increasingly complex due to cloud-native applications, distributed infrastructures, hybrid networks, sophisticated cyberattacks, and rapidly changing threat landscapes. Conventional security mechanisms frequently struggle to detect emerging threats and provide transparent explanations for automated decisions. This research proposes an Explainable Artificial Intelligence (XAI) framework for adaptive enterprise cybersecurity that integrates machine learning, threat intelligence, cloud-native security controls, continuous monitoring, and interpretable decision-making. The proposed framework collects security telemetry from cloud workloads, network traffic, identity systems, applications, endpoints, containers, and security information and event management platforms. Machine learning models analyze these heterogeneous data sources to identify anomalies, classify threats, prioritize risks, and predict potential attacks. Explainability techniques such as SHAP-based feature attribution, LIME-based local explanations, rule-based reasoning, and confidence scoring are incorporated to clarify why security alerts and automated responses are generated. A cloud-native architecture supports scalable threat detection through containerized security services, microservices, orchestration, automated policy enforcement, and secure API integration. The methodology includes threat-data collection, preprocessing, feature engineering, model development, explainability analysis, adaptive response orchestration, and experimental evaluation. The framework aims to improve detection accuracy, reduce false positives, enhance analyst trust, accelerate incident response, and strengthen security governance. The proposed approach demonstrates how explainable and adaptive AI can support transparent, scalable, and resilient cybersecurity operations across modern enterprise cloud environments.
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