VectorZero Active Data Vault 2.0 secures sensitive data

VectorZero announced the newest release of its Active Data Vault, version 2.0, which introduces new cybersecurity features.

This highly secure and isolated environment is designed for storage plus secure and easy use of extremely sensitive data.

Active Data Vault 2.0 provides a comprehensively secure environment that uses best practices, including thousands of security controls. It implements FIPS-140-2-L3 encryption and/or quantum-safe encryption and/or Fully Homomorphic Encryption (FHE).

This environment is compliant (NIST 800-53-5 Classified systems, 800-207 ZTA, FIPS, CIS, SOC2) for current and legacy workloads—and now includes Windows as well as Linux.

The frontroom feature enables users access to a secure environment with privacy-enhancing technologies (PETs) from most devices with internet connectivity. It’s quick and low-risk to set up, with a low cost to sustain.

Active Data Vault offers a cleanroom with confidential computing to run one software with multiple inputs.

Jason Asbury, VectorZero CEO, said, “This helps protect processing from malicious actors, including insiders. Our confidential computing can be used wherever encryption is at rest as well as in transit, so all data and processing is protected all the time.” The environment reduces attack vectors for further security hardening.

The latest version of VectorZero’s security solution provides additional support for OpenVPN clients on all popular operating systems, including Android.

Made possible through an ephemeral infrastructure, Automated Moving Target Defense (AMTD) improves security. Triggered manually by pattern matching and by artificial intelligence/machine language, both the customer servicing and Active Data Vault control plane infrastructures automatically move.

Asbury added, “Another benefit is more flexible networking between enclaves. Through Secure Multi-Party Computation (SMPC), we’re opening new doors for cooperation and collaboration among independent organizations. They can keep sensitive inputs, including software code and machine language models, confidential when performing processing over one or more datasets.”

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