HIPAA compliance and AI in digital health: What privacy officers must understand

Artificial intelligence (AI) is becoming central to digital health, offering new tools for diagnosing conditions, managing patient data, and automating communication. While these advancements improve care delivery and operational efficiency, they also introduce unique privacy risks, especially when systems handle protected health information (PHI). Privacy Officers must now assess not just whether digital platforms are HIPAA-compliant, but also how AI technologies align with the law’s privacy and security standards.

As health tech companies race to adopt AI tools, regulators are sharpening their focus. For HIPAA-covered entities and their business associates, maintaining compliance isn’t just about ticking boxes—it requires understanding how AI systems operate, what data they use, and how to manage them responsibly.

Understanding HIPAA’s role in the age of AI

HIPAA was enacted to safeguard patients’ health data in both physical and digital environments. It applies to covered entities—like healthcare providers, insurers, and clearinghouses—and to business associates who handle PHI on their behalf. With AI-driven platforms increasingly taking on roles in telehealth, clinical decision support, and remote monitoring, these technologies often fall under HIPAA’s regulatory umbrella.

Despite AI’s complexity, its use does not alter HIPAA’s foundational rules. The Privacy Rule continues to govern how PHI can be accessed, used, and shared. The Security Rule demands safeguards—administrative, physical, and technical—to protect electronic PHI. AI tools that process such information must comply with both rules, no matter how advanced their capabilities.

One of HIPAA’s core tenets, the Minimum Necessary Standard, remains especially relevant. AI models typically seek comprehensive datasets to perform effectively, but HIPAA requires systems to access only the data essential for a specific purpose. Designing AI solutions that adhere to this principle, while still delivering accurate results, is a growing challenge.

Privacy risks in AI-driven digital health

The integration of AI into healthcare services brings heightened risks, particularly around data transparency, bias, and unauthorized disclosures. Generative AI tools—such as chatbots, symptom checkers, and voice assistants—can inadvertently collect PHI without adequate safeguards. If not properly configured, these tools may violate HIPAA, particularly when data is stored in unsecured environments or transmitted without encryption.

Another major concern is the black-box nature of AI models. Many advanced systems offer little visibility into how decisions are made, making it hard for Privacy Officers to verify whether PHI is being used appropriately. Without clear documentation or interpretability, ensuring compliance becomes difficult, especially during audits or breach investigations.

Moreover, algorithmic bias poses ethical and legal risks. AI models trained on non-representative data may reinforce existing disparities in healthcare access or outcomes. For example, an AI triage tool might perform well on the general population but fail to accurately assess symptoms for specific racial or ethnic groups. This not only challenges healthcare equity but may also violate anti-discrimination laws and trigger regulatory scrutiny.

Lastly, the growing use of de-identified data by AI systems must be approached carefully. HIPAA allows the use of such data under the Safe Harbor or Expert Determination methods. However, when combined with other datasets, the risk of re-identification increases. Privacy Officers must ensure that de-identification processes are robust and regularly evaluated, especially in environments where data-sharing is common.

HIPAA AI compliance tips

To minimize legal exposure and ensure patient trust, organizations must embed privacy considerations throughout the AI lifecycle. This begins with early-stage design and continues through vendor selection, deployment, and monitoring.

Conduct AI-focused risk assessments: Standard risk analyses may not account for AI’s complex data workflows or training mechanisms. Privacy Officers should tailor assessments to examine how data flows through AI systems, what PHI is stored, and whether access is appropriately restricted. Dynamic elements—like model updates or new data sources—should trigger reassessments to ensure ongoing compliance.

Additionally, special attention must be paid to generative tools or patient-facing platforms that gather information in real time. These systems require clear data intake protocols and audit trails to avoid unauthorized PHI collection or breaches.

Strengthen oversight of AI vendors: Many digital health companies rely on external AI vendors, who must sign a Business Associate Agreement (BAA) outlining data usage, protection, and AI-specific clauses. These include responsibilities for model training, PHI usage, audit rights, security standards, and transparency in AI processes. Periodic audits of vendors can help identify vulnerabilities, especially with model updates or new data-sharing agreements.

Promote transparency and explainability: Wherever possible, organizations should adopt AI systems that support explainability, meaning the logic behind decisions can be traced and understood. This not only aids compliance but also supports clinical accountability and patient trust.

For example, in diagnostic support tools, clinicians should be able to understand the reasoning behind a recommendation. If the system cannot provide that transparency, it may be unsuitable for clinical use under HIPAA and other health privacy laws. Documentation of system outputs and internal review processes also supports audit readiness and regulatory reporting.

Navigating the regulatory horizon

As the use of AI in healthcare accelerates, federal and state regulators are paying closer attention. While HIPAA itself has not been overhauled to address AI-specific issues, guidance from the Department of Health and Human Services (HHS) and enforcement by the Office for Civil Rights (OCR) suggest increased scrutiny is on the horizon.

In parallel, state-level privacy laws—like the California Consumer Privacy Act (CCPA) and Virginia Consumer Data Protection Act (VCDPA)—introduce new obligations that may overlap with HIPAA. Some require transparency around automated decision-making, which may affect AI tools that are not directly regulated by HIPAA but still handle sensitive information.

Privacy Officers should monitor both federal and industry-wide AI regulations. FTC warnings about false AI claims and poor data practices highlight the growing overlap between AI, privacy, and consumer rights. Globally, laws like the EU’s AI Act and Canada’s AI and Data Act aim to govern high-risk AI. U.S. companies operating abroad must consider how these rules impact data handling, especially across borders.

Balancing innovation with accountability

Artificial intelligence offers powerful new capabilities in digital health, but it also heightens the need for rigorous data governance. HIPAA compliance is not static—it must evolve to meet the complex demands of AI technologies that analyze, predict, and interact with patient information.

For Privacy Officers, success lies in balancing progress with protection. This means conducting in-depth risk assessments, maintaining strong oversight of AI vendors, and fostering transparency in how AI decisions are made. It also means preparing for evolving regulations by staying informed and advocating for privacy-first innovation.

As patient expectations rise and regulators respond to rapid technological change, organizations that invest early in responsible AI governance will be best positioned to lead, not just legally, but ethically and operationally, in the future of digital health.