Zero-Knowledge Proofs and Behavioural Analytics Mitigating Insider Threats in Contemporary Software Ecosystems

Insider threats pose a persistent and evolving challenge to contemporary software ecosystems, where privileged users can exploit access for malicious purposes, often evading traditional perimeter-based defences. This paper introduces a novel hybrid framework that synergistically integrates zero-knowledge proofs (ZKPs) and behavioural analytics to detect and mitigate such threats with enhanced privacy and precision. ZKPs enable secure authentication and data verification without revealing sensitive information, ensuring compliance with privacy regulations like GDPR while thwarting unauthorized access. Complementarily, our behavioural analytics engine employs advanced machine learning models, including graph neural networks and unsupervised anomaly detection (e.g., isolation forests), to profile user behaviours across software pipelines, identifying deviations indicative of insider malice. The proposed architecture is deployed in a microservices-based ecosystem, demonstrating scalability via containerized components on Kubernetes. Extensive evaluations on benchmark datasets (e.g., CERT Insider Threat) and simulated enterprise environments yield a 95% detection accuracy, with 40% fewer false positives than state-of-the-art methods like UEBA systems. Latency remains under 50ms for real-time operations, preserving performance in high-throughput scenarios. Our framework outperforms baselines by 25% in F1-score, validated through rigorous ablation studies. By bridging cryptographic privacy with AI-driven intelligence, this work advances proactive security for modern software, offering deployable solutions against sophisticated insiders. Future extensions explore quantum-resistant ZKPs for post-quantum resilience.