Retrieval-Augmented Generation (RAG) systems are rapidly being integrated into healthcare workflows, including clinical decision support, claims processing, and patient engagement. However, real-world deployments reveal a critical gap: systems that perform well in controlled environments often fail in production due to subtle yet high-impact risks. These include exposure of Protected Health Information (PHI), hallucinated or clinically unsafe outputs, and violations of regulatory requirements such as HIPAA—failures that can directly affect patient safety, institutional trust, and legal accountability.
This presentation introduces a governance-first, security-centric architecture for production-grade healthcare RAG pipelines. The proposed framework embeds policy-driven access controls, token-level redaction, and context-aware retrieval filtering to prevent sensitive data leakage. It further integrates hybrid validation layers that combine deterministic rule-based checks with model-assisted reasoning to detect hallucinations and enforce output reliability. End-to-end auditability and traceability are incorporated across ingestion, retrieval, and generation stages to support compliance and operational transparency.
To enhance robustness, the approach incorporates differential privacy techniques and encryption strategies for data in transit and at rest, along with failure-injection testing to simulate real-world risks such as schema drift, prompt injection, and data leakage. Experimental and simulated evaluations demonstrate that embedding governance and security controls directly into the RAG pipeline significantly reduces PHI exposure, improves output reliability, and strengthens compliance readiness.
This work presents a practical blueprint for building trustworthy healthcare AI systems, enabling organizations to mitigate risk and accelerate responsible GenAI adoption in regulated environments.
