AI and Compliance: What Regulated Firms Need to Know
The Compliance Question Every Firm Is Asking
Across every regulated industry, the same question is coming up in partner meetings, compliance reviews, and board discussions: how do we adopt AI without creating regulatory exposure?
The regulatory frameworks governing professional services were not written with AI in mind. But the principles underlying those frameworks — data protection, client confidentiality, duty of care, auditability — apply directly to how firms deploy and use AI tools. Here is a practical survey of what the major regulatory frameworks require and how private AI infrastructure addresses those requirements.
HIPAA and Healthcare AI
For healthcare practices, HIPAA creates specific obligations around protected health information (PHI). Any technology that processes, stores, or transmits PHI must meet HIPAA's requirements for administrative, physical, and technical safeguards.
Consumer AI tools like ChatGPT are explicitly not HIPAA-compliant. OpenAI's consumer terms disclaim any HIPAA obligations. The critical requirement is the Business Associate Agreement (BAA). Any entity that processes PHI on behalf of a covered entity must sign a BAA. Most consumer and even many enterprise AI providers do not offer BAAs, or offer them only for limited use cases.
How private AI addresses this: When AI models run on HIPAA-compliant infrastructure within the covered entity's control, PHI is never transmitted to an external AI provider. With a signed BAA from the infrastructure provider, encryption at rest and in transit, access controls, and comprehensive audit logging, the deployment can meet HIPAA's technical safeguards from the ground up.
ABA Model Rule 1.6 and Legal Ethics
For law firms, the ABA's Model Rule 1.6 establishes the duty of confidentiality. Attorneys must "make reasonable efforts to prevent the inadvertent or unauthorized disclosure of, or unauthorized access to, information relating to the representation of a client."
The key phrase is "reasonable efforts." Bar associations across the country have been issuing guidance on AI, and the consensus is moving toward a clear position: firms must understand where client data goes when they use AI tools. Feeding client documents into a consumer AI tool — where data is processed on the provider's infrastructure and possibly used to improve future models — is an increasingly difficult argument to defend as "reasonable."
How private AI addresses this: When the AI runs on infrastructure the firm controls, the confidentiality analysis is straightforward. Client data is processed within the firm's environment. No external party has access. The firm can demonstrate to any bar authority exactly where client data was processed — because the answer is "within our own infrastructure."
SEC and FINRA for Financial Services
Financial advisory firms operate under SEC and FINRA oversight, with specific requirements around data handling, record retention, and client communications.
SEC Regulation S-P requires firms to adopt written policies to safeguard customer records. FINRA's cybersecurity rules impose additional obligations around data protection. Both regulators have signaled increasing attention to AI governance. If a financial advisor feeds client portfolio data into a consumer AI tool, that data has left the firm's controlled environment — and "we have a policy that says don't use ChatGPT" is not a satisfying answer to examiners.
How private AI addresses this: Private infrastructure gives financial advisory firms a clear compliance narrative. Client data is processed on infrastructure the firm controls. AI interactions can be fully logged for compliance review. And the firm can demonstrate to examiners that no client data flows to external AI providers.
Insurance Industry Considerations
Insurance agencies handle sensitive personal and financial information daily — health histories, financial disclosures, claims details. State insurance regulations create obligations around how this information is stored and processed. The NAIC has been active in framing principles for AI use in insurance, and the common thread is clear: firms must demonstrate how they protect policyholder data when using AI tools.
How private AI addresses this: By keeping AI processing within the firm's controlled environment, insurance agencies can demonstrate clear data boundaries to regulators. Policyholder information never leaves the firm's infrastructure, and every AI interaction can be logged and audited.
The Common Thread
Across every regulated industry, the compliance requirements for AI adoption converge on a few key principles:
Know where your data goes. Private AI gives you a clear answer: it stays in your environment.
Maintain audit trails. Private AI infrastructure provides comprehensive logging of all AI interactions — who accessed what, when, and what was generated.
Demonstrate reasonable safeguards. Whether HIPAA, ABA, or SEC standards, firms must take affirmative steps to protect client data. Running AI on your own infrastructure is one of the strongest safeguards available.
Control your vendor relationships. Private AI eliminates the dependency on a third-party platform's security posture and terms of service.
Moving Forward
The regulatory landscape around AI is evolving rapidly, but the direction is clear. Firms that deploy private AI infrastructure now are positioning themselves ahead of the curve — adopting AI for productivity while maintaining the data sovereignty and auditability that regulators will increasingly expect.