Artificial intelligence is quickly becoming embedded in the tools that organizations rely on to manage data, improve efficiency, and support decision making. For many businesses, the question is no longer whether to use AI, but how to do so responsibly. Selecting the right AI solution partner requires more than reviewing features or marketing claims. It requires a clear understanding of how the technology works, how data is handled, and whether the provider is prepared to operate in a regulated and trust-driven environment.
AI due diligence should be approached as a structured conversation rather than a checklist. The goal is to gain clarity and confidence before integrating AI into processes that affect sensitive data, compliance obligations, and client trust.
Start with the Fundamentals of Data Use and Security
One of the first areas to address when evaluating an AI enabled solution is data flow. Organizations should understand what data the system accesses, where that data is processed and stored, and whether it is shared with external large language models or other third parties. Vendors should be able to clearly explain what information is redacted or anonymized before leaving their system and how data is protected throughout its lifecycle.
Security assurance is another critical starting point. While many vendors reference SOC reports, not all reports offer the same level of assurance. A SOC 2 Type 2 report that covers the specific product being evaluated and includes a clear scope, audit period, and explanation of any exceptions provides more meaningful insight into a vendor’s control environment. Transparency in this area is often an early indicator of vendor maturity.
Understand Where and Why AI Is Used
Not all AI is created or applied in the same way. A key part of due diligence is understanding where AI is used within a product and why it is appropriate for those functions. Vendors should be able to distinguish between normal programming logic is used where accuracy must be exact, and generative or predictive AI used for tasks that allow variability.
This distinction matters because some use cases are not well suited for generative AI. Understanding whether AI features are automatically enabled or offered on an opt-in basis also helps organizations assess risk and user control before adoption.
Look for Transparency, Testing, and Validation
Strong AI vendors welcome detailed questions and are willing to provide supporting artifacts such as SOC reports, sub processor lists, and data flow diagrams. Clear and direct answers signal that the provider has considered the complexities of operating in regulated environments.
Validation testing is equally important. Organizations should request trial periods and test AI behavior using firm specific or anonymized data. This allows teams to evaluate accuracy, consistency, and the potential for hallucinations before relying on the technology in production. It is also essential to understand what happens to data used during trials once testing is complete.
Dig Deeper into Auditability and Governance
Beyond initial evaluations, organizations should assess whether AI-driven actions are explainable and traceable. The ability to review audit trails, export logs, and understand how overrides or adjustments are handled is critical for compliance and regulatory requirements. Black box AI solutions that cannot be explained or audited may introduce unacceptable risk.
Governance questions should also address how vendors monitor model accuracy, handle low confidence results, manage updates, and respond to incidents. Clear escalation paths, human review processes, and rollback capabilities all contribute to long term reliability and trust.
Due Diligence as an Ongoing Process
AI capabilities continue to evolve, including within tools organizations may already use. New AI features should be evaluated with the same level of scrutiny as new products. Updated data flow documentation, confirmation of opt in settings, validation of security controls, and trial testing all remain important, even with long standing vendors.
Due diligence is not about finding reasons to say no. It is about making informed decisions, asking the right questions, and choosing partners that value transparency, security, and responsible innovation.
How YHB Can Help
Evaluating AI solutions takes time, technical insight, and a structured approach. YHB’s Risk Advisory Services team helps organizations navigate AI vendor analysis and consulting with a focus on data protection, governance, and regulatory readiness. To learn more about how we can support your AI strategy and vendor evaluations, visit our Cybersecurity and Technology Advisory Services page or contact our team to start the conversation. For additional information, please refer to the AICPA’s AI Solution Due Diligence Guide for Accounting Firms.
