HIPAA (USA)  ·  GDPR (EU)  ·  PHIPA (Ontario)  ·  PIPEDA (Canada)  ·  EU AI Act (EU)  ·  NIST AI RMF  ·  FDA AI/ML  ·  SOC 2  ·  ISO/IEC 42001  ·  ISO/IEC 27001  ·  OECD AI Principles

Where IT Teams Should Begin with Healthcare AI Compliance

Launching AI in healthcare isn’t just about technology — it’s about trust, legality, and security from day one. For IT teams, the first challenge is knowing where to begin. Compliance requirements like HIPAA, GDPR, PHIPA, and the EU AI Act can feel overwhelming, especially when combined with security frameworks like NIST AI RMF, ISO 27001/42001, and HITRUST.

The key is to approach compliance as a structured roadmap, not an afterthought. By starting with clear inventories, risk assessments, and cross-team alignment, IT teams can prevent compliance failures, reduce regulatory exposure, and build confidence with stakeholders.

Updated: September 20, 2025 

Four Key Starting Points

These four starting points are just that a beginning. They won’t solve every compliance issue, but they create the visibility and structure IT teams need to move forward responsibly. From here, the real work continues: testing, monitoring, and adapting as laws evolve and new risks emerge. By treating these steps as the foundation rather than the finish line, IT teams can build the resilience needed to keep healthcare AI safe, ethical, and compliant over the long term.

1. Map AI Assets and Data Flows

The Challenge: IT teams often lack visibility into which AI tools are in use, what data they access, and how that data moves through systems. Shadow IT use — such as unvetted AI APIs — creates serious compliance gaps.

What’s Being Done

  • NIST AI RMF: Recommends asset and data inventories as the first step in AI risk management.

  • HIPAA & GDPR: HIPAA requires documentation of how PHI is collected, stored, and shared. Under GDPR, many organizations (especially those engaged in high-risk or large-scale processing) must maintain a Record of Processing Activities (RoPA) showing how personal data is handled.

  • Practical Action: IT teams are building central registries of AI models, vendors, and datasets, noting which contain PHI, anonymized data, or third-party APIs.

2. Conduct Initial Risk Assessments

The Challenge: AI introduces risks not captured by traditional IT risk models: bias, model drift, adversarial manipulation, and lack of explainability. Without assessment, these issues remain hidden until failure occurs.

What’s Being Done

  • HIPAA Security Rule: Requires covered entities to perform risk analyses of systems handling PHI.

  • EU AI Act: Once in force, it will require risk assessment and post-market monitoring for high-risk AI systems (including most healthcare tools).

  • ISO 42001: Establishes organizational AI risk management programs, requiring teams to document risks like bias and transparency gaps.

  • Practical Action: IT teams are adopting hybrid assessments, combining privacy impact assessments (PIAs) with AI risk assessments aligned to NIST AI RMF.

3. Establish Governance Policies

The Challenge: Without governance, AI development proceeds in silos, leaving inconsistent security, privacy, and compliance practices across teams.

What’s Being Done

  • HIPAA & PHIPA: Require covered entities to implement administrative safeguards, including policies and workforce training.

  • NIST AI RMF: Calls for governance structures that define roles, responsibilities, and oversight of AI systems.

  • HITRUST CSF: Maps compliance policies across multiple laws and frameworks, helping healthcare orgs standardize governance.

  • Practical Action: IT leaders are drafting AI-specific governance policies covering dataset sourcing, model testing, explainability, and breach response.

4. Build Security and Training Foundations

The Challenge: Technical safeguards must evolve to cover AI-specific risks, and staff need to understand compliance implications before tools go live.

What’s Being Done

  • HIPAA Security Rule: Requires technical safeguards such as access control, audit logs, and encryption.

  • NIS 2 Directive & EU AI Act: Require “state of the art” cybersecurity protections for high-risk AI.

  • ISO 27001/42001: Emphasize technical controls paired with staff training as core compliance pillars.

  • Practical Action: IT teams are training developers, clinicians, and compliance officers on responsible AI use, while implementing logging, anomaly detection, and adversarial testing.

 

 

 

A Four-Phase Roadmap

One industry model (TechRadar) suggests a four-phase approach to AI compliance in healthcare. While not a regulatory framework, it can help structure early compliance planning.

Assessment – Map AI assets, tools, and data flows, including shadow IT.

Policy Development – Create cross-team governance frameworks tied to HIPAA, GDPR, and ISO standards.

Technical Controls – Implement AI-specific cybersecurity, monitoring, and bias mitigation.

Education & Training – Train IT staff, clinical teams, and compliance officers to maintain vigilance.

 

Key Takeaways

  • Starting AI compliance requires visibility first: map tools, vendors, and datasets before anything else.

  • Risk assessments must cover AI-specific concerns like bias and drift — not just traditional IT vulnerabilities.

  • Governance policies connect legal obligations to technical implementation, ensuring alignment with HIPAA, GDPR, PHIPA, PIPEDA, and the EU AI Act.

  • Security and training foundations ensure that compliance is sustainable — not just a launch-time checkbox.

Relevant Resources

TechRadar – Four-Phase AI Security Approach: https://www.techradar.com/pro/the-four-phase-security-approach-to-keep-in-mind-for-your-ai-transformation

HHS HIPAA Security Rule Summary: https://www.hhs.gov/hipaa/for-professionals/security/laws-regulations/index.html

European Commission – GDPR Overview: https://commission.europa.eu/law/law-topic/data-protection_en

EU AI Act Portal: https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai

ISO/IEC 42001 – AI Management Standard: https://www.iso.org/standard/81230.html