4 Critical Items Organizations Should Consider When Adopting AI into their TPRM Programs.
As Third-Party Risk Management (TPRM) organizations increasingly turn to AI-driven solutions, selecting the right AI is crucial. And just like the SOC reports we all look at in the TPRM space, not all AI systems are created equal, and making the wrong choice could introduce risks rather than mitigate them. So lets talk about what TPRM organizations should ask about when adopting AI and the controls they should demand from AI providers.
I think we can all agree that a real struggle in TPRM is resource and time constraints. Thats what makes AI so enticing. When integrating AI into a TPRM program, organizations should be evaluating not only the efficiency gains it can give you and the features it has, but the VALUE of the technology’s capabilities, the risks it may present, and the agility it has to align with your existing work flows and processes. AI should enhance—not replace—robust risk management practices while maintaining compliance and security.
Here are four (4) critical factors to consider during the evaluation process:
- Flexibility, Agility and Compatibility with Your Existing Program: Do you have to start fresh with a whole new platform or workflow? If the answer is yes, you need to understand if the value of the tool is larger then the effort to change, upgrade, redo etc. The use of an AI tool should integrate seamlessly into your current workflows without requiring drastic changes. Otherwise, does it really add value or create efficiencies? AI must be flexible enough to integrate with your current policies, workflows, technology stack, and data sources, rather than imposing rigid, predefined structures that could disrupt your processes.
- Privacy and Confidentiality: This should be the top priority and not surprisingly, it isn’t always a building block of an AI solution. In TPRM, any AI you use will have access to the organization's crown jewels—its most valuable and sensitive data. And that of your vendors. Protecting this data is paramount, and AI must adhere to strict confidentiality measures to ensure its security and integrity. Sensitive third-party data must be safeguarded at all costs. Require your vendors to demonstrate complete segregation of data and strict model training requirements (no live data). Should not be using open or generative AI as is.
- Consistency and Reliability: AI should consistently deliver accurate results that are reproducible. Controls are satisfied or not. A model that produces different outputs for the same input introduces uncertainty and undermines trust in its recommendations. This is how you build the trust.
- Resistance to Manipulation: AI must be designed to defend against adversarial attacks, including prompt injections and malicious use. Hallucinations should be absolutely forbidden, as they can introduce severe risks to TPRM programs. The AI must be designed to prevent the generation of misleading or fabricated information. TPRM must be fact-based, and AI should deliver verifiable outputs rather than generating fictitious or misleading responses. They should be able to demonstrate controls that prevent hallucinations and prompt injections as well as provide evidence that those controls work.
**There is ongoing research on Generative AI highlights its capacity for malicious behavior jeopardizing a program's confidence and trust. The Generative AI bot has actually been proven to have the capacity for cheating and nefarious behavior (scary stuff!!). So you have to be ready to protect against that.**
The bottom line? For AI to truly add value in TPRM, it must integrate seamlessly with existing workflows, safeguard sensitive data with uncompromising privacy and confidentiality, deliver consistent and reliable results, and be resilient against manipulation—because in risk management, trust and integrity outweigh innovation for its own sake.
