GenAI GRC transforms how organizations handle supply chain risk by moving away from outdated paperwork methods to real-time intelligence. Chief Information Security Officers (CISOs) are pressured to manage vulnerabilities not just from direct vendors but also from numerous layers of third parties adopting generative AI. Traditional governance, risk, and compliance (GRC) practices are insufficient against the complexities introduced by GenAI, which can create risks like training data poisoning and shadow AI. Current compliance reports often reflect outdated information, leaving organizations vulnerable to systemic failures. The article highlights two significant threats: shadow AI and model drift. Shadow AI occurs when vendors use public large language models (LLMs) to generate code without disclosing it, potentially embedding noncompliant or insecure code into products. Model drift refers to the gradual changes in AI models that can lead to biases or violations of regulations. These risks are not detectable through annual audits, making it essential for CISOs to adopt a more fluid understanding of supply chain risk, focusing on the behavior of external algorithms instead of just external defenses. To effectively address these challenges, the article proposes a shift toward GenAI GRC, which emphasizes predictive capabilities over mere compliance reporting. This involves utilizing LLMs for contextual intelligence by analyzing diverse data sources, implementing a digital trust ledger to continuously assess vendor reliability, and synthesizing complex regulations in real-time. The goal is to communicate risks in terms that resonate with boards, such as risk velocity metrics, rather than just listing vulnerabilities. By framing GRC as a strategic imperative rather than a compliance task, CISOs can secure necessary budgets and position themselves as enablers of business growth. The article urges immediate action, recommending pilot projects focused on critical vendors to leverage generative intelligence for improved resilience.