Enforcing PHI Compliance for Multi Modal Use Cases in Healthcare using AI

AI is revolutionizing healthcare, enhancing diagnostics, automating workflows, and advancing research. From medical imaging to Healthcare ERP systems, automation opportunities are vast.

One key AI application is semantic segmentation using Deep Neural Networks, like Convolutional Neural Networks (CNNs) and Fully Convolutional Network (FCNs.) These models enable precise medical image segmentation, aiding microbiological research and pathology.

Challenges in HIPAA Compliance for Medical Imaging

Medical imaging devices (CT, X-ray) capture crucial scans but often contain Protected Health Information (PHI)—names, IDs, and timestamps. To comply with HIPAA, PHI must be masked, using manual tools or automated redaction algorithms.

However, compliance gaps remain:

• Manual masking can leave PHI exposed.
• Some regions enforce HIPAA less strictly.
• Older machines lack built-in automation.

Real-World Risks: PHI Exposure

A quick web search can reveal unredacted X-rays with PII visible, often in the top-left corner. This security risk highlights the urgent need for stronger PHI protection in medical imaging.

Data Scientists and Researchers might be oblivious to the Data Governance Policies and may create a pipeline with following flow:

Without strict governance controls, a simple image-to-text prompt can accidentally extract Protected Health Information (PHI) or Personally Identifiable Information (PII) from medical images, financial documents, or legal records. For example:

A routine OCR-based AI model scans an X-ray image containing patient details (OpenAI GPT 4o) :

Unprotected metadata can violate HIPAA, GDPR, and other regulations. To stay compliant, AI pipelines must:

• Label sensitivity levels with Cloud DLP
• Automatically mask sensitive info and redact PHI/PII
• Use RBAC & audit logs to enforce access controls
• Protect workloads in confidential environments for secure AI processing

AI-Powered PHI Redaction

To ensure compliance, healthcare can adopt AI-driven PHI detection & masking using several out of the box tools available by Cloud providers.

Leveraging GCP’s Secured Data Warehouse Blueprint, here is a multi-layered architecture that enforces stringent data governance, security, and compliance.

Following best practices for data isolation and governance, we implement three distinct perimeters to control data flow and access:

1. Confidential Perimeter – Processes and stores unencrypted confidential data under strict access policies.
2. Non-Confidential Perimeter – Handles non-sensitive data with relaxed security constraints.
3. Data Governance Perimeter – Centralized layer for managing data classification, access control, and auditing.

This structured segmentation prevents unauthorized data exposure and ensures controlled data movement across layers.

The pipeline utilizes Vertex AI Gemini API to perform multimodal processing—extracting text from images and analyzing its content. The extracted text is then scanned using Cloud DLP (Data Loss Prevention) Scanner, which applies predefined policies to classify and tag datasets.

Here is an e.g. Tag to classify data in Data Catalog:

• Classification: PHI 
• Sensitivity Level: High

These Data Catalog tags enable downstream systems to dynamically enforce security controls and trigger compliance workflows.

The Data Governance layer acts as a compliance gatekeeper, enforcing policies based on the Data Catalog metadata. If an image fails redaction or cropping, it is automatically rerouted to the Confidential Perimeter, preventing unauthorized processing.

This layer serves as a critical control point, mitigating compliance risks and ensuring adherence to regulatory frameworks.

Conclusion

By leveraging Vertex AI Gemini API, Cloud DLP, and Data Catalog, this architecture creates an end-to-end secure pipeline that enforces automated data classification, compliance-driven processing, and governance monitoring. The multi-layered perimeter model ensures that sensitive data remains protected, while enabling scalable AI-driven transformations.