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June 20, 2018

Immuta Cashes In on Data Privacy Scramble

George Leopold

(Popartic/Shutterstock)

Immuta Inc., the data management for AI vendor, said it will use a $20 million funding round to establish a European beachhead in London as it sharpens its focus on helping enterprises develop “ethical” AI algorithms that comply with new European privacy rules as well as possible U.S. data privacy regulations.

The Series B funding round announced Wednesday (June 20) was led by DFJ Growth and joined by new investors Citi Ventures and Dell Technologies Capital. The startup’s customers include banks such as Barclays (NASDAQ: DTYS), insurers and U.S. intelligence agencies.

Immuta’s data management platform is designed to provide greater control of the data fed into algorithms, speeding deployment as well as increasing visibility into how automation tools are functioning.

The goals include “controls around data science” Immuta CEO Matthew Carroll said in an interview, along with helping enterprises get a handle on what he called “analytics ethics.”

“Privacy is a right in Europe,” Carroll noted. “In the U.S. it’s a sales feature.”

The startup argues that relying on cognitive algorithms exposes companies to risks ranging from critical errors to fraud. “The law cannot keep pace with technology,” Carroll added. “As analytics and AI advance faster than any other technology category, the gap between regulation and analytics also increases.”

Hence, Immuta’s data management platform is aimed at replacing legacy systems largely focused on applications, an approach that slows down data science teams struggling to keep up with high-velocity data.

Meanwhile, as the startup tightens its focus on helping companies develop “ethical AI” and “guard rails” needed to comply with new data privacy rules, Carroll said the company is expanding its reach into risk management by adding and “outcome-based risk” framework to its platform. In one scenario, Carroll said companies could use it to determine if applications are “drifting” from the intent of a trained model. “Right now, there are no checks and balances,” he added.

The startup cites market research predicting that most data analytics use cases will soon require links to distributed data, further complicating data access, metadata management and policy enforcement. Hence, the startup worked with experts at Yale University Law School to incorporate rules such as the European Union’s General Data Protection Regulation into its platform as well as steps for enforcing those rules.

The result is an automated data management platform with policy controls able to respond to evolving data privacy rules. Further, the company and its investors foresee greater use of predictive AI models that will require a fuller understanding and management of data feeding into algorithms in real time. All this must be accomplished while ensuring compliance with data rules, Carroll stressed.

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