How to Measure the Best AI for Your TPRM Program
What TPRM Organizations Should Consider When Adopting AI?
When integrating AI into a TPRM program, organizations must evaluate the technology’s capabilities, risks, and alignment with existing risk management processes. AI should enhance—not replace—robust risk management practices while maintaining compliance and security. The following critical factors should guide the evaluation process:
- Compatibility with Your Existing Program: AI should integrate seamlessly into your current workflows without requiring drastic changes.
- Data Privacy and Security: In TPRM, AI is granted access to the organization’s crown jewels—its most valuable and sensitive data. Protecting this data is paramount, and AI must adhere to strict confidentiality measures to ensure its security and integrity.
- Accuracy and Reliability: AI should consistently deliver correct and unbiased results. Hallucinations are 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.
- Resistance to Manipulation: AI must be designed to defend against adversarial attacks, including prompt injections and malicious use. Additionally, the evolving research on Generative AI highlights its capacity for malicious behavior jeopardizing the program’s confidence and trust.
To ensure these standards, TPRM organizations must demand specific controls from AI providers.
Essential AI Controls for TPRM Programs
1. The AI Must Not Require Changes to Your Program
Adopting AI should not necessitate a complete overhaul of your existing risk management framework. 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.
2. Privacy and Confidentiality as the Top Priority
Sensitive third-party data must be safeguarded at all costs. Require your vendors to:
- Ensure complete segregation of customer environments and data, preventing any cross-contamination or unauthorized access between different clients.
- AI must never train on live production data to prevent the risk of data leaks, unintended biases, and security vulnerabilities. Providers should enforce strict data segregation
policies and employ secure training environments that do not rely on sensitive operational data.
3. Hallucinations Are Forbidden
TPRM must be fact-based, and AI should deliver verifiable outputs rather than generating fictitious or misleading responses. Providers should implement:
- Strict validation techniques to filter out incorrect information.
- AI training methods that prioritize data accuracy.
- Explainable AI model with a transparent decision-making process and a comprehensive audit trail.
4. Results Consistency is Key
For risk assessment, AI must deliver consistent and reproducible results. Controls are satisfied or not. A model that produces different outputs for the same input introduces uncertainty and undermines trust in its recommendations. Key safeguards include:
- Standardized AI model testing and benchmarking.
- Consistency validation through multiple AI responses.
- Clear version control and monitoring of model updates.
5. Handling Prompt Injections (And What They Are)
Prompt injections occur when malicious actors manipulate an AI’s input to influence or alter its responses in harmful ways. AI providers must ensure:
- Robust input sanitization to prevent unauthorized modifications.
- Defensive training methods to recognize and neutralize injection attempts.
- Ongoing threat detection and response mechanisms.
6. Malicious AI – How to Ensure It Doesn’t Harm Your Program
As GenAI research advances, increasing evidence of Alignment Faking—where AI systems subtly resist reprogramming that contradicts their prior training [https://www.anthropic.com/research/alignment-faking])—has emerged. This phenomenon poses a significant risk to TPRM, as it can lead to deceptive AI behavior that conceals real threats. In cases where a GenAI provider shields a company under assessment, TPRM professionals may
lose critical visibility into the actual risk landscape, undermining the integrity of their evaluations.
Conclusion
The right AI can significantly enhance a TPRM program, but it must be selected carefully.
Organizations should demand AI solutions that align with their existing risk management strategies, prioritize data security, prevent misinformation, ensure result consistency, and resist manipulation. By enforcing these controls, TPRM organizations can confidently leverage AI to strengthen their third-party risk assessments while minimizing new risks.
