AI Infrastructure Buying Guide for Regulated Industries

Generic AI evaluation criteria aren't enough here

Model accuracy, price per token, and integration ease are the criteria most AI buying guides focus on — and they matter, but they're incomplete for banking, healthcare, government-adjacent, and other regulated buyers, where a technically excellent AI product can still be an unusable or non-compliant choice. Regulated buyers need a checklist with an additional layer specific to compliance and accountability.

What to add to the evaluation

Data residency and processing location

Confirm exactly where data is processed and stored, not just where the vendor is headquartered — many AI platforms route inference through infrastructure in multiple jurisdictions, which can conflict with data residency obligations even when the vendor contract is with a domestic entity.

Training-data usage rights

Confirm in writing whether the vendor's contract allows customer data — including data submitted in prompts — to be used to train the vendor's future models. For regulated data, this needs to be an explicit contractual "no," not an assumption based on general reputation.

Explainability and audit trail depth

For any AI output that informs a regulated decision — credit, clinical, employment — confirm the platform can produce a record of what informed that output sufficient to satisfy an examiner or auditor, not just a confidence score.

Vendor's own compliance posture, not just the product's

A compliant product from a vendor with no SOC 2 report, no breach history disclosure, and no clear incident-response process is still a compliance risk — the evaluation needs to cover the vendor as an organization, not only the specific AI feature being purchased.

Human-in-the-loop requirements specific to your regulator

Some regulated decisions require human review regardless of AI confidence level — confirm the platform supports the specific human-review workflow your regulatory framework requires, rather than assuming full automation is permitted.

Exit and portability terms

Confirm what happens to historical data, model fine-tuning, and audit records if the vendor relationship ends — regulated industries often need to retain records well beyond a typical contract term, and the vendor's default data-retention-at-termination policy may not match that requirement.

Why this evaluation shouldn't run inside the AI vendor's own sales process

These questions are the ones a vendor's sales team is least equipped — or least incentivized — to answer with full candor. An independent, vendor-neutral evaluation against this checklist, before signing, is where MALA's AI infrastructure advisory work concentrates. Talk to an advisor before your next regulated AI purchase.