As AI innovation accelerates, one of the thorniest problems facing enterprises is the safe use of customer data. While large language models promise transformation, regulated industries such as finance, healthcare and payroll services are struggling to operationalize AI due to strict privacy, sovereignty and compliance concerns.
DataMasque’s Grant de Leeuw discusses synthetic data for AI training.
How does data masking solutions company DataMasque Ltd. remediate these problems and support the safe development and testing of AI agents?
“What DataMasque solves for is that we do a dark site deployment into the client’s private zone of their data,” said Grant de Leeuw (pictured), co-founder and chief executive officer of DataMasque. “We’re not asking them to send it outside of their secure zone, and we will imitate their customer data but create it synthetically. What that allows organizations to do is create synthetically identical customer data, which they can then use for development, training and testing AI agents before rolling them out.”
De Leeuw spoke with theCUBE’s John Furrier at theCUBE + NYSE Wired: AI + Cloud Leaders Media Week event, during an exclusive broadcast on theCUBE, News Media’s livestreaming studio. They discussed how the global startup is helping enterprises overcome regulatory and privacy roadblocks to AI innovation.
Simulating synthetic customer data with a dark-site model
Unlike contemporary approaches that rely on SaaS-based models requiring customer data to leave its secure environment, DataMasque deploys within the enterprise’s infrastructure, either on-premises or in the cloud. This “dark site” model ensures that personally identifiable information and other sensitive data stay protected, even during testing and development, according to de Leeuw.
“We’ll make a recommendation as to how to protect that information,” he said. “Then once that’s been agreed and is being signed off by the customer, we will create that synthetically identical data where that sensitive data was. The rest of the data is actually identical, and one of the key values of DataMasque is the fact that we actually mask down to a field level.”
This approach doesn’t just protect organizations from breaches, it also allows them to surface valuable edge cases, such as messy birthdates or conflicting addresses, critical for robust AI agent performance in the real world, de Leeuw added.
Here’s the complete video interview, part of News’s and theCUBE’s coverage of theCUBE + NYSE Wired: AI + Cloud Leaders Media Week event:
Photo: News
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