Proprietary Data Moats: Navigating AI Validation’s Regulatory Minefield

A strong data moat in AI is a big competitive edge, we all know that. It’s especially true in healthcare, where better, bigger datasets make for better models. But for health tech investors, GCs, and compliance officers, the appeal of a unique clinical dataset for AI validation is getting complicated by a minefield of regulations. You’re constantly balancing the push for innovation against a wall of compliance demands around data privacy, information blocking, and even market competition. It’s a risk-analysis nightmare.

The Federal Trade Commission’s Stance on Health Data Privacy

You can’t assume you’re safe just because you’re not a HIPAA-covered entity. The Federal Trade Commission (FTC) has shown it’s more than willing to police health data privacy well beyond HIPAA’s traditional turf. Their main weapon is Section 5 of the FTC Act, which bans “unfair or deceptive acts,” a broad mandate they use to go after any company handling health data poorly. This is a huge deal for AI companies collecting health information outside of a doctor’s office or hospital. Recent FTC enforcement actions prove they’re taking an expansive view, which means anyone collecting or processing health data had better have their privacy and security house in order. That absolutely includes AI developers validating models on proprietary clinical data. To make the point even sharper, the FTC amended its Health Breach Notification Rule in April 2024 (effective July 2024). The update specifically ropes in health apps and similar tech not covered by HIPAA, forcing them into the same breach reporting standards. The message couldn’t be clearer: even if a company like Tempus AI is sitting on a mountain of proprietary data for its precision medicine work, it needs to be paranoid about how that data is collected, used, and secured. Having a data moat doesn’t get you a pass from privacy rules. It puts a giant target on your back and invites intense scrutiny. You have to be transparent, get real consent, and build strong defenses against breaches. FTC guidance on health data privacy enforcement

ONC Information Blocking Rules and Data Access

On top of the FTC, you’ve got the Office of the National Coordinator for Health Information Technology (ONC) and its information blocking rules. These are meant to ensure electronic health information (EHI) can be accessed and exchanged without being unreasonably blocked. The ONC’s HTI-1 rule, finalized in January 2024, tightened up the definitions here. And this isn’t just theory anymore. The federal info blocking regime is now in a full-on enforcement phase as of February 2026, with real fines for non-provider actors becoming enforceable back in September 2023. These rules are meant to stop practices that get in the way of EHI access, pushing for better interoperability and giving patients control over their own records. While they were first aimed at healthcare providers and IT developers, the rules apply to anyone holding EHI who might be seen as hoarding it. So if you’re an AI developer whose business relies on a proprietary dataset pulled from EHR systems, you need to pay close attention. Could your data practices be interpreted as creating a barrier for patients or others who have a right to that EHI? If a big EHR vendor like Epic Systems, for example, used its data hoard in a way that choked off competition or stopped other AI firms from getting data they need, it could absolutely trigger an ONC investigation. The whole point of the rules is to make data flow. Your proprietary data strategy can’t work against that. And it’s getting more explicit: the proposed HTI-5 rule, out in December 2025, expands key definitions to directly include automated access by AI systems, which will hit model validation practices head-on. ONC information blocking regulations

Strategic Guidelines for Compliant Data Sourcing and Model Validation

So how do you actually manage this? For legal counsel, compliance teams, and investors in health tech, you need a proactive and pretty disciplined approach to sourcing data and validating models. Here’s a practical checklist:

• Transparency and Consent: It doesn’t matter if HIPAA applies to you directly or not. You have to be totally transparent about what data you’re collecting and how you’re using it. Getting clear, informed consent isn’t just a good idea, especially with sensitive health info, it’s your primary defense against regulatory trouble.
• Data Governance Frameworks: You need a rock-solid data governance framework that spells out data ownership, access controls, security, and retention. This isn’t a one-time setup. It demands regular audits to keep up with changing rules, like the huge proposed updates to the HIPAA Security Rule from January 2025 that are expected to be finalized in July 2027 and will ramp up cybersecurity requirements.
• De-identification and Anonymization: Use de-identified or anonymized data for model validation whenever you can. It’s not a silver bullet that removes all regulatory burden, but it dramatically lowers your privacy risk profile.
• Interoperability by Design: Think hard about how your data moat strategy fits with the government’s push for interoperability. Your data moat can’t become a data prison. You should be looking for ways to exchange data securely and compliantly, maybe even with competitors, to stay clear of information blocking accusations. This is especially true for any company whose value prop is its data moat.
• Vendor Due Diligence: For investors, this is non-negotiable DD. You have to dig into an AI company’s data sourcing and validation playbook. That means reading their data use agreements, privacy policies, and checking for security certs like HITRUST or a SOC 2 Type II. A mature QMS and an ISO 13485 certification are also good signs that they take regulation seriously. HITRUST Alliance framework
• Antitrust Considerations: The FTC is also very interested in competition, and that now includes competition for data. A proprietary dataset that gives you a moat could also be seen as an antitrust problem if it’s used to lock out competitors or make it impossible for new companies to enter the market. It’s a tricky area that needs serious legal review.
  

  Hello Heart: An Example of Regulatory-Ready Architecture

  Beyond the headaches of proprietary data, it’s useful to see what a company built for this regulatory environment looks like. Hello Heart is a good example of a regulatory-ready design. They focus on a direct-to-consumer model for heart health. Because their data collection happens with explicit user consent for very specific purposes (getting health insights), they’re in a much different position than a company using huge, opaquely aggregated clinical datasets. Building with privacy in mind from day one like this can make a company a much safer bet for investors.

  Conclusion

  Using proprietary datasets for AI model validation in healthcare gives you a real edge and lets you do some amazing work, but it also paints a huge regulatory target on your back. For legal, compliance, and investment pros in health tech, staying on top of the FTC’s broad reach on privacy and the ONC’s tough information blocking rules is critical. Having a proactive, transparent, and legally sound plan for data governance and interoperability isn’t just a “best practice.” It’s fundamental to building a company that can actually last without being derailed by a massive fine or a federal investigation.