Latest

DataMasque has launched an integrated solution in AWS Marketplace, allowing AWS and Guidewire Cloud customers to easily deploy the software directly into their secure environment.

Your AI project needs real customer data to learn meaningful patterns, but legal rightfully says no. This guide explains what GDPR, Australia's Privacy Act, US regulations (CCPA, HIPAA), and New Zealand's Privacy Act require for AI training, why anonymisation alone often fails, and how enterprises use synthetically identical data to unblock their models without compliance risk.

DevOps and platform engineering teams are increasingly asked to provision safe, realistic test data on demand, without slowing pipelines or exposing sensitive customer information. This guide explains the two dominant integration patterns, and how DataMasque's REST API fits into GitHub Actions, Jenkins and GitLab CI workflows.

Data masking and synthetic data generation both reduce the risk of using sensitive data in non-production environments, but they work differently and suit different use cases. Understanding the distinction helps you build a data strategy that covers developing and testing, compliance, and AI without compromise.

DataMasque today announced it has raised US$4 million in funding led by the early-stage fund of Wavemaker Partners, with existing investors OIF Ventures and Icehouse Ventures doubling down.

OpenAI recently released Privacy Filter, an open-weight model for detecting and redacting PII. This post looks at where it works, where it falls short and what production data masking actually requires.

DataMasque has signed a Strategic Collaboration Agreement with Amazon Web Services (AWS). The multi-year agreement deepens the collaboration between the two organizations, ensuring enterprises can access the data they need—safely and compliantly—to power generative AI, agentic AI and large-scale machine learning initiatives.

DataMasque is now available in the new AI Agents and Tools category of AWS Marketplace. Customers can now use AWS Marketplace to easily discover, buy, and deploy AI agents solutions, including DataMasque’s data masking software using their AWS accounts, accelerating agent and agentic workflow development.

DataMasque strengthens its collaboration with AWS and commitment to the financial services industry through obtaining the AWS Financial Services Competency.

DIY data masking may start as a quick fix, but it rarely stays that way. This post unpacks the hidden technical and operational costs of DIY data masking - and why enterprise-grade platforms like DataMasque provide a more scalable, secure and even cost-effective solution.

By using EC2 Image Builder and the DataMasque component from AWS Marketplace, you can automate the deployment of secure, repeatable environments. This setup not only improves efficiency but also enforces consistency across your staging or test environments.‍

DataMasque 2.26 now supports masking Snowflake databases. This expansion means you can apply our secure consistent masking technology to yet another critical data platform in your infrastructure.