AI in TPRM: Why Secure, QUALITY AI Matters, Even If It Takes More Time
I took this opportunity to dive deep into what the differences are between solutions that use standard Generative AI (fast and cheap(er)) and those that use secure AI with quality commitments as well as additional controls and layers of security in place (More Secure and (sometimes) more costly)
While any AI solution can assist in document review and workload reduction, its security and confidentiality implications, accuracy, and costs often differ, especially in highly regulated industries such as healthcare and banking.
So What’s the Difference? What Do We Need to Know?
- AI built for Speed uses patterns and utilizes massive amounts of data to learn and, as the name suggests, generates new content. Think of tools like ChatGPT, Gemini, DeepSeek, or Claude. These tools can chat, summarize documents, or draft assessment responses. This type of AI is commonly used in many TPRM platforms today. We’ll refer to this as Standard AI for the sake of this discussion
- AI built for Quality analyzes and extracts information from existing documents using technologies like machine learning and natural language processing (NLP). It classifies and highlights data without “guessing” or inventing content. Let’s call this Secure/Quality AI for the sake of this discussion.
Why AI Built for Quality Is a Better Tool for TPRM
When reviewing vendor documentation (especially when it contains PII, sensitive business data, or compliance reports), data security, accuracy, and control are critical. This is non-negotiable in regulated industries such as healthcare and banking. Oftentimes, when comparing AI Vendors, we can be swayed by the idea that the tool that promises immediate implementation and the ability to pull data in mere minutes.
AI is supposed to make everything faster. The issue isn’t speed; it’s what’s sacrificed for levels of speed that are unnecessary. If we focus solely on speed, we run the risk of using tools that prioritize speed over accuracy. In TPRM, we cannot afford to sacrifice accuracy. Some things to consider:
| Security Factor | Standard AI | Secure/Quality AI |
|---|---|---|
| Data Accuracy | Can “hallucinate” or fabricate and create content X | Extracts only what’s present in ✓
documents: NO HALLUCINATIONS |
| Auditability | Harder to trace how outputs were formed since content can be generated. X | Decisions are traceable and explainable with a direct mapping to evidence used to deliver results. ✓ |
| Data Privacy | Typically hosted in external, public cloud environments. X | Usually deployed on-prem or in private clouds. Not shared with multiple organizations. ✓ |
| Regulatory Alignment | Can introduce unvalidated content into assessments. X | Designed to stay within strict data governance rules. ✓ |
Bottom Line: Secure /QualityAI provides greater transparency and control, making it better suited for use cases involving sensitive or regulated third-party data such as Vendor/Supplier documents and data found in the TPRM process.
Trade-offs: Is Spending More Time for the Guarantee of More Secure AI Worth It?
From my perspective, as a TPRM practitioner, especially in regulated environments like healthcare, finance, and critical infrastructure, it’s definitely worth it. Secure/Quality AI delivers significant advantages:
- MORE Secure, LESS Risk
- Ensures data stays where it belongs
- Doesn’t generate inaccurate or unverified content
- Easily audited and regulator-friendly
- Trusted by compliance and legal teams
Ultimately, if you’re handling regulated data, evaluating vendor controls, or reviewing sensitive documents, your AI tools must meet the same bar as your other security systems. Secure/Quality AI is an investment in data integrity, auditability, and peace of mind.
So, how do you know if the tools you are assessing are Standard AI or Secure/Quality AI?
| Question to Ask | Why You Should Ask It |
|---|---|
| How does your AI ensure it only extracts information explicitly present in documents, without generating or inferring data that isn’t there? | You want to be able to validate that the system is deterministic. You want zero hallucinations. Every output should be grounded in real, reviewable evidence. |
| Can you show me how each decision or recommendation made by your AI can be traced directly back to specific evidence within the source documentation? | Validate that the platform provides transparent logic paths, mapping conclusions directly to document excerpts. This ensures auditability and defensibility in regulated environments. |
| Is your solution deployed in a single-tenant environment (on-prem or private cloud), and can you guarantee that my data is not shared or used across other clients’ environments? | Isolation and privacy are important. The AI should never use your vendor data for model training or operate in shared multi-tenant environments unless strictly configured. |
| How does your platform fit within our TPRM program or control frameworks? How customizable is the tool to meet our specific needs? How often can we make changes to our controls, questionnaires, etc? | You need to confirm that the vendor supports custom requirements, is agile and can accommodate real time changes and updates. Validate they are agile and able to partner with you. You shouldn’t have to change for them, they should build to suit you. |
| Who supports the onboarding, configuration, and ongoing use of your AI? Will I be working with TPRM domain experts or just account managers and solution engineers? | Its imperative to learn the WHO behind the tool. That you’ll receive support from people who understand third-party risk, not just generic software reps. You want real guidance from risk professionals, not a sales pipeline. |
In third-party risk, getting it fast is helpful, but getting it right is essential.
