Opaque Systems Employee Directory

Technology, Information and InternetCalifornia, United States11-50 Employees

AI is driving a massive platform shift, but like every major shift (PC, mobile, cloud), it demands a trust layer upgrade. AI is no different, except this time these systems are autonomous, non-deterministic, and operating at machine speed. Today, Enterprise AI leaders face a critical conflict. CEOs mandate AI adoption everywhere for competitive advantage and productivity gains, but CISOs, security and compliance teams rightfully block production deployments of AI agents using sensitive data due to risks of leaking data and violating compliance policies.

OPAQUE’s Confidential AI Platform solves this with verifiable runtime governance, enabling enterprises to securely use valuable proprietary data to power AI agents and workflows, and ground AI to triple model accuracy, all while maintaining policy compliance. OPAQUE is the only Confidential AI platform designed specifically for AI workloads to deliver cryptographic proof of data and model weight privacy, and policy enforcement. OPAQUE unblocks AI value by making trust verifiable so organizations can responsibly harness AI while preserving data privacy, sovereignty, and compliance. OPAQUE embeds verifiable trust directly into the AI runtime, enforcing security, privacy, and policy compliance across the entire lifecycle—before, during, and after runtime. 

Born from UC Berkeley’s RISELab, OPAQUE is the Confidential AI company that solves the core challenge blocking AI adoption at scale: security concerns about data leaks or compliance violations. Its Confidential AI Platform extends traditional data governance tools with real runtime verification, enabling teams to responsibly deploy AI using their most valuable proprietary data, and move from pilot to production 4-5X faster. Customers and partners include ServiceNow, Anthropic, Encore Capital, Accenture, and leaders across high tech, financial services, insurance, and healthcare. Learn more at opaque.co.

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