Third-Party Risk Management in the Age of AI - Rethinking Trust and Accountability
Companies are rapidly integrating AI into their operations, from customer service chatbots to advanced analytics tools. And if organisations are using AI, then so are their third party vendors, the companies processing data on their behalf. Do we know how our vendors are using our data, and how will we manage that risk? AI and Third-Party Risk: What’s at Stake? AI amplifies third-party risk in several ways: Data Leakage: Information entered into AI tools could be stored, reused, or exposed. Are your inputs contributing to a model that may resurface these comments elsewhere? Opaque Data Practices: What AI systems are your third parties using? Are they using in-house proprietary models? How clear are you on data usage, retention, and any onward sharing? Model Vulnerabilities: If an AI model is compromised, its outputs could become inaccurate or biased, damaging trust and operations. What obligations do you have, and how do you manage this risk? Supply Chain Risks: Many AI solutions will rely on a network of sub-processors, expanding the risk landscape. Do you know where your data ends up? The Key Questions to Ask Yourself and Your Vendors To manage third-party risk effectively in the AI era, you need to ask the right questions: ...