Identify & Protect Sensitive Data Across Public Sector Systems

DataMasque is a next-generation data masking and sensitive data discovery solution designed to help public sector organizations meet the growing demands of data privacy, compliance, and cybersecurity. Our platform enables agencies to securely de-identify sensitive data—such as Personally Identifiable Information (PII), Protected Health Information (PHI), and financial records—before it is replicated to non-production environments like development, testing, and analytics. This dramatically reduces the attack surface without compromising data utility.

Public sector agencies are under increasing pressure to modernize systems while complying with strict regulations such as FISMA, HIPAA, IRS Publication 1075, and state-level privacy laws. DataMasque helps achieve this by automating data masking in complex enterprise environments—across databases, files, and cloud infrastructure—ensuring consistent, irreversible masking that retains referential integrity across systems. Whether data is hosted on-premise, in AWS GovCloud, or other hybrid environments, DataMasque seamlessly integrates into DevOps pipelines to ensure privacy by design.
Our discovery engine also enables agencies to locate and classify sensitive data in structured and semi-structured formats—often a foundational requirement for privacy impact assessments and audit readiness. By knowing where sensitive data resides and ensuring it is properly de-identified in lower environments, public sector teams can confidently innovate, train, and test without the risk of exposing live citizen data.

DataMasque is trusted by global healthcare, finance, and defense organizations and we are now excited to extend this value to U.S. federal, state, and local government agencies through our partnership with Carahsoft.
DataMasque empowers the public sector to meet compliance mandates, minimize data risk, and safely accelerate digital transformation. Our goal is simple: protect sensitive data while enabling operational efficiency across government systems.