Industry-Specific Compliance
Last updated: 2026-05-18
Industry-Specific AI Compliance
General AI compliance rules are a baseline. Regulated industries stack additional requirements on top. This guide covers healthcare, finance, education, legal, and government. Each section covers what rules apply, what to look for in vendors, and the mistakes people commonly make. It's a starting point, not an exhaustive treatment.
Healthcare (HIPAA)
HIPAA governs protected health information (PHI). Any AI tool that receives PHI must be HIPAA-compliant, and you'll need a Business Associate Agreement with the vendor before using it for that purpose.
What to look for in vendors: willingness to sign a BAA, encryption at rest and in transit, access controls, no use of PHI for model training without explicit agreement, and audit logs.
Common mistakes: using consumer AI tools like standard ChatGPT for anything that touches patient information, assuming that a HIPAA-compliant cloud hosting environment makes the whole stack compliant, and using AI tools that haven't been evaluated for healthcare use.
Finance (MAS, SEC, PCI-DSS)
Singapore's MAS has guidelines covering AI in financial services, focused on fairness, governance, and accountability. The SEC's rules around AI in advice, trading, and disclosure are still evolving. For anything involving cardholder data, PCI-DSS rules apply regardless of the AI layer.
What to look for: vendors that understand the regulatory environment, proper data handling for financial records, audit trails, and human oversight for any AI touching regulated advice or transactions.
Common mistakes: using AI to generate investment recommendations without proper registration or oversight, passing card numbers to AI tools, and assuming general-purpose AI is inherently compliant with financial regulations.
Education (FERPA)
FERPA protects student education records. Schools and educational platforms that use AI tools touching student data need to ensure those tools protect records appropriately, with proper consent and access controls.
What to look for: FERPA-compliant vendors with clear data handling policies, no training on student data without consent, and proper consent mechanisms when minors are involved.
Common mistakes: using AI for grading or feedback without reviewing vendor data practices, sharing student records with vendors that train on user data, and not getting required consent.
Legal
Legal work involves privileged client information. Any AI tool that handles that data must protect confidentiality. Some jurisdictions are also starting to issue guidance on AI use in legal practice.
What to look for: no training on your input, no data sharing with third parties, data residency controls, and specific confidentiality commitments. Some vendors offer legal-specific deployment configurations worth considering.
Common mistakes: passing privileged material to consumer AI tools, assuming the vendor doesn't train on your data without checking the terms, and using AI for client advice without human legal review.
Government (FedRAMP)
FedRAMP is the US federal authorization program for cloud services used by government agencies. If you're deploying AI tools within a federal context, they need FedRAMP authorization. State and local governments often have similar requirements.
What to look for: FedRAMP authorization or FedRAMP-ready status, and full compliance documentation.
Common mistakes: using commercially available cloud AI tools for government data without checking authorization status, and assuming cloud hosting is sufficient without FedRAMP authorization.