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Masking data consistently across microservices requires every service sharing an entity identifier to produce the same masked value, across databases, environments, and teams. This guide explains DataMasque's run secret and hash_columns mechanism and how platform engineers apply it across a multi-service data tier.

DataMasque's in-flight masking API accepts a JSON payload at a dedicated REST endpoint and returns a masked version in milliseconds, using the same YAML ruleset and run secret as batch masking jobs. This post covers the four core use cases, how the three-step flow works, and how cross-source identity consistency is maintained across batch and real-time masking.

Using real production data in development, testing, and AI training pipelines creates compliance exposure under GDPR, HIPAA, Australia's Privacy Act, and CCPA; plus security breach risk, contractor data transfer obligations, and AI memorisation risks. This article walks through all five risks with specific regulatory obligations, documented breach patterns, and practical guidance.

When data masking ignores foreign key relationships, your test environments break. This guide explains what referential integrity means in data masking, why it fails with table-by-table approaches, and how DataMasque preserves it automatically across every database in your environment.

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.