DataMasque raises $5.6 million to tackle one of AI’s biggest enterprise hurdles

De-identification startup DataMasque has secured $5.6 million in a fresh funding round, led by Wavemaker Partners, with existing investors OIF Ventures and Icehouse Ventures also participating.

DataMasque says its platform allows organisations to generate synthetic versions of customer data that can be used for AI training, testing and analytics while preserving the characteristics of the original information.

“Using synthetically identical data, where personally identifiable information is masked but relationships and values are maintained, is critical for ensuring models are developed on accurate data,” co-founder and CEO Grant de Leeuw told SmartCompany.

He pointed to one example that changes an individual’s date of birth while preserving their age, allowing organisations to protect sensitive information without affecting the usefulness of the underlying dataset.

Unlike traditional privacy tools that redact sensitive information, DataMasque replaces it with synthetic data designed to maintain consistency across different systems and datasets. De Leeuw said many organisations still rely on custom scripts to manage the process internally.

According to the startup, it has achieved six-fold annual recurring revenue growth and tripled its headcount since its previous $2.3 million seed round back in 2023.

De Leeuw said a significant amount of that growth had been driven by AI projects requiring access to customer data. Privacy and security concerns often prevent organisations from moving forward.

“One company we are now working with had over 100 blocked AI initiatives that required access to customer data,” he said.

He went on to say more than 95% of DataMasque’s revenue now comes from production deployments rather than pilots or proof-of-concept projects.

“Enterprise AI projects are increasingly blocked by data access. This funding validates the need for synthetically identical customer data to feed into AI models or use to evaluate, test, and train AI,” de Leeuw said.

Alongside the funding announcement, DataMasque unveiled a new capability designed to de-identify unstructured data, including call transcripts, emails and logs. The company claims the feature enables organisations to safely use a broader range of enterprise information for AI and analytics projects.

De Leeuw also said maintaining the utility of data was critical when training AI models, arguing organisations needed to preserve relationships and characteristics within datasets while protecting personal information.

“As enterprises feed more data into AI systems, they need a way to protect sensitive information without compromising datasets used for testing and analytics,” Paul Santos, co-founder and managing partner at Wavemaker Partners, said.

While cloud providers including AWS, Google and Snowflake offer their own de-identification capabilities, de Leeuw argued organisations can run into problems when data is handled differently across multiple systems.

“If you de-identify your data in AWS with different values to what is stored in Snowflake, you break data consistency,” he said.

“Data needs to maintain its utility across data stores and that is why the likes of AWS have signed a Strategic Collaboration Agreement with DataMasque.”

DataMasque said the fresh cash injection will be used to accelerate growth across key markets, including Australia. It also plans to expand its platform capabilities for highly regulated industries such as financial services, telecommunications and government.

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