Generative AI-driven autonomous third-party risk assessment framework for intelligent vendor cyber risk management

Modern enterprises depend on vast third-party ecosystems-cloud providers, managed service vendors, software partners, and AI-enabled business integrators-each representing an amplified cyber risk exposure that propagates nonlinearly across digital supply chains. Conventional Third-Party Risk Management (TPRM) programs remain anchored to annual questionnaire cycles, spreadsheet-based scoring, and static audit methodologies that cannot detect emerging vendor vulnerabilities in real time or analyze unstructured evidence at enterprise scale. This article presents the Generative AI-Driven Autonomous Third-Party Risk Assessment Framework (GAI-ATRAF), a novel six-component architecture integrating Large Language Model (LLM) reasoning, Retrieval-Augmented Generation (RAG), Vendor Knowledge Graph Intelligence, Cyber Digital Twins, Graph Attention Network (GAT) risk propagation, and SHAP-driven Explainable Governance. GAI-ATRAF continuously ingests vendor contracts, SOC reports, threat intelligence, vulnerability disclosures, and compliance evidence, transforming heterogeneous signals into dynamic risk scores and predictive forecasts. Experimental evaluation demonstrates 97.1% risk prediction accuracy-a 7.8-point improvement over machine learning baselines-alongside 86.7% reduction in assessment duration, 80% reduction in manual analyst effort, and compliance coverage gains averaging 19.3 percentage points across NIST CSF, ISO 27001, and SOC 2. These results confirm that autonomous generative AI reasoning, when architecturally unified with graph intelligence and explainability, delivers statistically significant operational superiority over all existing TPRM approaches.