Data security platform discovering, classifying, and protecting sensitive data
DataStealth builds a data-centric security platform that discovers and classifies sensitive data across cloud and on-premises environments, then applies encryption, masking, and tokenization to defend against insider and external threats. The tech stack spans Java, Node.js, Go, and Rust on AWS/Azure/GCP infrastructure—a polyglot, cloud-native approach typical of companies solving complex data protection problems at scale. Active projects show focus on next-generation architecture and cloud-native microservices, while hiring velocity is accelerating across engineering and support, suggesting customer traction outpacing current ops capacity.
Notable leadership hires: Technical Engagement Lead
DataStealth is a Canadian data security platform founded in 2018, headquartered in Mississauga, Ontario. The company serves enterprise and mid-market customers with solutions for data discovery, classification, and protection across hybrid cloud environments. Core capabilities include dynamic data masking, encryption, tokenization, and test data management (TDM), with particular depth in PCI compliance scope reduction and attribute-based access control. The product is positioned as a data-first defense layer—protecting sensitive information regardless of network perimeter compromise—and supports regulatory and compliance requirements across privacy frameworks.
DataStealth runs Java, Node.js, Go, and Rust on AWS, Azure, and GCP cloud platforms. Infrastructure includes AWS EKS, ECS, Lambda, DynamoDB, API Gateway, CloudFront, and VPC, with Cloudflare for edge security and Jamf for endpoint management.
Current projects include next-generation platform architecture, cloud-native microservices, tokenization and encryption feature expansion, critical incident management, and portfolio-level reporting infrastructure.
DataStealth.io's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.