DETECTION AND MITIGATION OF DATA EXFILTRATION TECHNIQUES VIA ENCRYPTED NETWORK COVERT CHANNELS

The increasing dependence of modern organizations on digital infrastructure has significantly intensified the risks associated with unauthorized data disclosure and exfiltration. The widespread adoption of encrypted network communications provides essential protection for confidentiality and integrity, but it also creates additional challenges for security monitoring and threat detection. Encrypted network covert channels may allow sensitive information to leave an organizational environment while remaining difficult to distinguish from legitimate encrypted communication. This article examines the principal challenges associated with detecting and mitigating data exfiltration through encrypted network covert channels and analyzes contemporary defensive approaches. Particular attention is given to traffic metadata, statistical characteristics, behavioral analysis, anomaly detection, machine learning, Data Loss Prevention (DLP), Security Information and Event Management (SIEM), network segmentation, and Zero Trust Architecture. The study emphasizes that effective detection cannot rely exclusively on payload inspection or predefined signatures because encryption restricts direct visibility into network content. Instead, a multilayered security strategy based on behavioral indicators, contextual analysis, endpoint monitoring, and continuous risk assessment is required. The article also discusses the importance of reducing false positives, protecting user privacy, and integrating technical controls with organizational security policies. The findings indicate that combining network-level analytics with endpoint, identity, and data-centric security mechanisms provides a more resilient approach to identifying and mitigating covert data exfiltration.