How to Maintain Privacy While Using GenAI in Healthcare

Generative AI has made its way into healthcare - drafting intake forms, structuring risk assessments, generating patient education content, and even analyzing unstructured notes for insights. But for all the time it saves and potential it offers, there’s one boundary that cannot be crossed: patient privacy.

That’s not just a compliance checkbox - it’s the foundation of trust between every healthcare provider and their patients.

So the question is: How do you use GenAI without compromising data privacy? Here’s a guide that breaks down exactly how to do that - backed by insights from data governance frameworks, HIPAA standards, and best practices across the industry.

1. Compliance with Regulations (HIPAA, GDPR, HITECH)

If you’re using a consumer GenAI tool like ChatGPT, it’s important to know: it’s not HIPAA-compliant by default. This doesn’t mean it’s unusable - it means you need to be strategic.

Under HIPAA, any tool handling Protected Health Information (PHI) must:

• Encrypt data at rest and in transit

• Maintain detailed access logs and audit trails

• Enable breach notifications within specified timeframes (e.g., HITECH’s 60-day rule)

For global operations, GDPR adds further layers like the right to erasure, consent management, and data minimization. If your AI use case touches any of this—you need frameworks in place from the start.

2. De-identification & Data Masking

Before you drop anything into a prompt, pause.

Remove names, addresses, contact numbers, dates of birth, and medical record numbers. Either use HIPAA’s Safe Harbor method (removing 18 types of identifiers) or the Expert Determination approach, which uses statistical methods to prove data can’t be re-identified.

And yes, this still applies even when you're "just experimenting." Because what starts as a test run can quickly turn into a production-level process.

3. Robust Data Security Controls

Data security doesn’t stop at the input prompt. The entire system around your AI tooling needs to be secure. Here’s how:

• Encrypt everything - AES-256 for stored data and TLS 1.3 for in-transit communications

• Use role-based access control (RBAC) to make sure only authorized people can access AI tools

• Layer on multi-factor authentication (MFA) so credentials alone aren’t enough

• Set up anomaly detection and real-time monitoring - because even with perfect controls, things can still go wrong

In other words: your GenAI stack should be held to the same security standards as your EHR system.

4. Data Governance & Lifecycle Management

Generative AI makes it tempting to hoard data. Don’t.

Establish a data governance framework that covers:

• How data is sourced, used, and stored

• When it’s deleted or archived

• Who owns the responsibility for each stage

Build metadata catalogs to track where data came from and where it’s been. Not just to stay organized - but to prove you’re accountable if anything is ever questioned.

5. Bias Audits & Model Integrity

If your GenAI model is helping generate health questionnaires or decision-support tools, it better not carry the same biases that plague healthcare.

Audit your training data and outputs for representativeness across age, race, gender, and socioeconomic status. Test with diverse scenarios to check how the model responds. Bias isn’t just an ethics issue - it’s a risk to clinical accuracy and patient outcomes.

6. Continuous Monitoring & Incident Response

Even with airtight policies, breaches can happen. What matters is how quickly you catch and contain them.

• Monitor GenAI interactions in real time, especially those accessing backend data or patient portals

• Maintain audit logs for every prompt, access point, and user session

• Build and test an incident response plan with clear steps for mitigation, internal alerts, and regulatory reporting

• Align response timelines with HITECH and regional laws (e.g., 60-day breach reporting)

Let’s Build Healthcare Solutions the Right Way 

Privacy is non-negotiable in healthcare. Generative AI can absolutely be part of the transformation journey - but only if it’s built on secure foundations.

At Code District, we help healthcare organizations design and build secure, compliant software solutions - so you can focus on improving outcomes, not dodging privacy risks.

Let’s create healthcare software where peace of mind is part of the package.