4-step strategy: Turn AI-powered TPRM into a business advantage

When it comes to organizational risk, AI presents a double-edged dilemma. Every leap forward brings powerful capabilities to better assess and address risk across an expanding enterprise, but it also introduces new and fast-changing exposure through the vendors, suppliers and other third-party organizations and technology that use it. As those third-party networks grow and AI is embedded into more processes and workflows, many organizations can’t identify, assess or manage the related risk fast enough. It’s a far worse problem when models, data sources, functionality and downstream dependencies can all be impacted after onboarding the third party. When this happens, risk profiles that once passed due diligence can suddenly look very different. 

AI, of course, offers capabilities to manage those increasing risks, but adding it to an unchanged business model won’t solve the problem. Instead, risk leaders should consider the opportunity to redesign third-party risk management (TPRM) for the AI era, including how to incorporate TPRM managed services as a continuous, decision-oriented capability to both govern AI risk and strengthen oversight. Here is a four-step strategy for risk leaders to follow:

1. Identify AI exposure beyond traditional vendors

It’s important to broaden the view of AI risk factors beyond providers labeled as AI companies. Start with identifying all AI-embedded models, cloud platforms, open-source components, subcontractors and AI-enabled services that have a direct and crucial influence on business processes. Link each dependency to its business owner, data access, operational purpose and downstream relationships so the organization can pinpoint exact exposures and create a risk mitigation approach.

From there, create a plan to keep track of changes within these vendors. If a provider’s model, training data, functionality or subprocessor network shifts after onboarding, things like privacy, cyber, compliance and operational processes can all pose a risk.

4. Build the governance and operating model before scaling

Before expanding AI across your organizational lifecycle, strengthen the basics. A trusted third-party inventory, risk-based segmentation, defined decision rights, clear data lineage and escalation thresholds are all priorities. Assign ownership for the model, the data, the workflow and the final risk decision. Test for accuracy, explainability, bias, security and drift and retain an audit trail of recommendations and overrides.

When internal capacity becomes strained, many forward-thinking leaders now opt for a technology-led TPRM managed services model to provide scalable monitoring, specialist expertise and continuous improvement as needs and situations change. As you adopt this kind of operating model, use outcome-based measures — stronger coverage, faster cycle times, fewer avoidable surprises and clearer decisions — to prove value.