Explore synthetic data governance in RWE with strong provenance, privacy checks, and compliance to eliminate zombie data risks and protect A
Synthetic data is becoming an essential component of real-world evidence (RWE), enabling healthcare organizations to accelerate research while protecting patient privacy. However, as its adoption grows, so do the challenges of maintaining data integrity, regulatory compliance, and trust.
One of the most significant emerging risks is "zombie data"—synthetic or imputed records with unclear provenance that can weaken AI models, compromise research quality, and create compliance concerns. Without strong governance, organizations may struggle to verify data lineage, assess re-identification risks, and meet evolving regulatory expectations from frameworks such as the European Health Data Space (EHDS), FDA, and EMA.
Effective synthetic data governance requires more than de-identification. It depends on verifiable data provenance, rigorous privacy assessments, secure processing environments, and continuous oversight throughout the data lifecycle. By implementing these practices, healthcare and life sciences organizations can strengthen data quality, improve AI reliability, and ensure that real-world evidence remains accurate, secure, and compliant.
As synthetic data becomes increasingly central to healthcare research, governance will play a critical role in enabling innovation while preserving trust, privacy, and regulatory confidence.












