Maximizing Data Value: The Difference Between Unlocking Data and Freeing Up Value | On-Demand Fireside Chat

Maximizing Data Value: The Key Distinction Between Unlocking Data and Freeing Up Its Value

Top Four Takeaways From the Fireside Chat

The accelerated pace towards cloud migration presents unique challenges for regulated industries. For instance, the insurance sector handles extensive amounts of PII (Personally Identifiable Information) and sensitive health data. To mitigate the risks that come with the shift of insurance data to the cloud, it's essential to implement robust privacy technologies.

Freeing up the data for use requires to consider technologies that address the multi-party aspect of data usage, rather than relying solely on access controls, single-step anonymization, or protection by subtraction.While it may be possible to remove direct identifiers or mask them in an enclosed environment, once the data is out in the open, it becomes susceptible to being combined with other third-party datasets to reveal sensitive information.

A more effective approach involves leveraging technologies like AWS cleanrooms and Anonos that uses Privacy-Enhancing Computation (PEC) technologies such as synthetic data, Statutory Pseudonymisation, and other protections to protect data during computation and ensure that privacy controls travel with the data.

Confidential compute environments have two primary uses. Firstly, they are utilized to to generate confidential results, as is the case with AWS cleanrooms. Secondly, they can be used to protect sensitive data during processing. To achive the protection of data in use, Anonos Data Embassy software can be deployed to create transformed and protected outputs, Variant Twins. These outputs can then be securely used and shared among multiple parties while remaining compliant with regulatory requirements.

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Chad Hersh

Head of Worldwide Business and Market Development, Life Insurance Industry

Mark Little

Chief Data
Strategist

Gary LaFever

Co-CEO and
General Counsel

Key Highlights

40% of all machine learning pipelines have experienced a breach. Sometimes breaches are breaches not because of a bad actor: They're just inadvertent (internal) exposure to people who should not have had access. It's not necessarily even malicious.

Gartner expects that by the end of 2024, 60% of all enterprises will be using Privacy Enhancing Computation (PEC). PEC is the protection of data during use, necessary for in untrusted environments, such as cloud computing. Untrusted environments are those in which you don't have complete control, especially when working with multiple parties.