New Oloid privacy architecture to protect enterprise biometrics unveiled

Many enterprises scrambling to adopt biometrics to defend against fraud are struggling to ensure regulatory compliance and the trust of their users as they do so. A new and enhanced privacy architecture from Oloid is intended to help them meet those requirements with biometric technologies that the company says redefine how enterprises implement biometric privacy at scale.

The privacy architecture has been designed, Oloid says, to align with the strictest regulations applying to biometrics, such as GDPR, CCPA, HIPAA, and BIPA. The company’s privacy and data security bona fides also received a boost with its DEA EPCS certification just weeks ago.

The new technologies for Oloid’s FaceVault platform include customer-managed encryption, which Oloid refers to as “BYOK” (bring your own keys), zero-image face biometric capture modes and synthetic likenesses made with AI.

Oloid provides options for biometric template storage and matching on-device in secure enclaves or trusted platform modules, as well as on-premises biometric vaults and edge-only deployments, according to the announcement. FaceVault also offers granular consent and revocation controls.

“Privacy is a fundamental human right, and the face is our most personal identifier, so safeguarding facial biometrics must be an absolute priority,” says Madhu Madhusudhanan, co-founder and CTO of Oloid. “We built Oloid FaceVault on a simple principle: a facial template should never become a liability. Our architecture lets each customer decide exactly what is stored, who can decrypt it, and when it can be used. Stripped of context and sealed behind customer-owned keys, a stolen template is worthless to hackers and even to us. That’s privacy by design with zero impact on user convenience.”

The company also shared a roadmap for future-proof cryptography, with planned upgrades to protect against insider threats and vulnerabilities from quantum computing. Those upgrades include biometric matching for fully homomorphic encryption, federated learning for templates, zero-knowledge face biometric verification, and differentially private analytics.

Article Topics

biometric matching  |  biometric template protection  |  biometrics  |  data privacy  |  data protection  |  face biometrics  |  Oloid