DataMasque raises $5.6M: NZ data privacy play targets enterprise AI spend
**DataMasque closed $5.6 million** led by Wavemaker Partners, with OIF Ventures and Icehouse Ventures participating. The New Zealand startup builds data masking software for enterprises that need to use sensitive customer information for AI training, testing, and analytics without compliance headaches. **The pitch:** synthetically identical data that preserves relationships and statistical properties while stripping personally identifiable information. CEO Grant de Leeuw describes it as changing a date of birth but keeping the age intact, so AI models train on realistic data without touching real customer records. **Deployment model matters here.** DataMasque runs inside the customer's environment (cloud, hybrid, on-prem) rather than shipping data externally. That addresses a common enterprise objection around exfiltration risk, particularly for regulated industries where data residency and compliance drive procurement. **Growth metrics:** six times ARR growth and triple headcount. Actual revenue and team size were not disclosed. The company appears to sell direct to enterprise and has leveraged AWS Marketplace for distribution, suggesting a partner-assisted enterprise motion alongside direct sales. **Market context:** this sits in the data security posture management and synthetic data category, competing with broader privacy platforms and specialist vendors. The regulatory compliance angle (particularly in banking and healthcare) is driving enterprise spend here. Gartner tracks this space under data security platforms and DSPM vendors. **Previous funding:** DataMasque raised $2.7 million from OIF Ventures and Icehouse Ventures previously. Whether the $5.6 million is cumulative or a fresh round was not specified. **What this means for sales teams:** if you are selling into regulated enterprises (financial services, healthcare, government), expect data privacy and AI compliance questions in procurement. Solutions like this enable deals that would otherwise stall on data governance concerns. Also worth tracking which competitors are building similar capabilities into existing platforms versus buying point solutions like DataMasque.