Mitigating third-party cyber risk using AI-powered threat intelligence and compliance analytics

Adegbenro, Samson A and Hundeyin, Whenume O and Olinmah, Florence and Adaba, Chinedu A (2025) Mitigating third-party cyber risk using AI-powered threat intelligence and compliance analytics. World Journal of Advanced Research and Reviews, 26 (2). pp. 2909-2929. ISSN 2581-9615

In an increasingly interconnected digital landscape, third-party relationships have emerged as a critical vector for cyber risk exposure across industries. Increasingly, organisations rely on outside vendors, hosted and cloud services and supply chain partners to deliver key services but this reliance brings with it vulnerabilities that traditional cybersecurity models are not generally effective at mitigating. Third-party ecosystems are complex in nature, and combined with varying compliance postures and disjointed threat visibility, it becomes clear that the approach has to move from static risk assessment to dynamic, intelligence-led protection. This paper details how AI can revolutionize third-party risk management through AI-powered threat intelligence and compliance analytics. We begin by contextualizing the third party risk landscape, highlighting key challenges such as insufficient vendor transparency, dynamic threat actors, and regulatory fragmentation. The study then delves into the architecture and functionality of AI-driven platforms that ingest multi-source threat feeds, behavioral indicators, and compliance metrics to produce predictive risk scores. Through machine learning algorithms, these systems continuously adapt to emerging attack patterns and detect anomalies indicative of compromise within vendor networks. A major focus is placed on integrating compliance analytics—enabling organizations to automatically assess vendors against frameworks such as NIST, ISO 27001, and GDPR. This fusion of threat intelligence with regulatory mapping allows for proactive risk prioritization and enhanced vendor segmentation. Case studies and real-world applications demonstrate how organizations using AI-based tools have reduced response times, improved audit readiness, and minimized breach propagation across digital supply chains. By combining predictive AI modeling with automated compliance enforcement, organizations can move beyond reactive controls to implement a continuous, risk-informed approach to third-party cyber governance.