Confident Security
Provably private, secure, and compliant AI inference engine.
| Date | Investors | Amount | Round |
|---|---|---|---|
* | $4.2m | Seed | |
| Total Funding | 000k | ||
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Confident Security provides a technology layer that enables provably private AI interactions for businesses with sensitive data. Founded by Jonathan Mortensen, a two-time founder who sold previous companies to BlueVoyant and Databricks, the San Francisco-based startup publicly launched in July 2025. The company's team includes experts with backgrounds at Google, Apple, Databricks, RedHat, HashiCorp, Stanford, and Johns Hopkins. In July 2025, Confident Security announced it had raised $4.2 million in a seed funding round backed by Decibel, South Park Commons, Ex Ante, and Swyx.
The company's core product, CONFSEC, is an enterprise-grade implementation of Apple's Private Cloud Compute (PCC) architecture designed to wrap around AI inference engines. It ensures that user prompts and metadata are not logged, used for AI model training, or seen by any party, including system operators. The technology achieves this through a combination of techniques including Oblivious HTTP (OHTTP), blind signatures, remote attestation, Trusted Platform Modules (TPMs), and Confidential VMs running in hardware-based trusted execution environments (TEEs). CONFSEC is deployable on any cloud or on-premise bare-metal environment, offering a technical guarantee of privacy rather than just contractual promises.
Confident Security's business model targets LLM providers, hyperscalers, governments, and enterprises in highly regulated or privacy-sensitive sectors such as healthcare, finance, and legal services. It acts as an intermediary vendor, allowing these clients to offer stronger privacy assurances to their end-users. This approach aims to unlock AI adoption in markets where data privacy concerns have been a significant barrier. The system is designed to be externally audited, and its software is publicly logged for expert review to verify its privacy and security guarantees.
Keywords: AI privacy, confidential computing, trusted execution environment, TEE, AI inference, data security, enterprise AI, secure AI, encryption, private cloud compute, data protection, privacy-enhancing technology, AI security, LLM security, provably private, information security, sensitive data, regulatory compliance, cloud security, bare-metal deployment