No Trust Required: How Tinfoil Makes AI Truly Private

As AI becomes embedded in everything from personal finance assistants to enterprise coding tools, one question looms large: how can we embrace the full power of cloud AI without compromising privacy? Tinfoil, a San Francisco-based startup founded in 2024, offers a bold solution—confidential computing that guarantees provable privacy, no code changes required. Built on top of NVIDIA’s latest secure hardware and backed by deep cryptographic verifiability, Tinfoil is redefining how AI and privacy can coexist.

What Problem Does Tinfoil Solve?

The status quo for AI deployment forces a painful tradeoff. Run models on the cloud and you risk sensitive data exposure. Run them on-prem and you sacrifice the flexibility, scale, and raw power of the cloud. This binary choice is especially concerning as AI workflows begin to touch increasingly sensitive domains: personal health, financial records, proprietary code, and more.

Traditional solutions—such as data protection agreements (DPAs), redaction tools, or isolation workarounds—are either legally flimsy, functionally restrictive, or operationally unsustainable. Tinfoil offers a new approach: run your AI in the cloud with the same level of security and privacy as on-prem environments, with cryptographic proof.

How Does Tinfoil Ensure Provable Privacy?

Tinfoil’s innovation lies in combining cutting-edge secure hardware with a full-stack software platform designed for privacy from the ground up. Its system runs on NVIDIA’s Hopper and Blackwell GPUs, leveraging their confidential computing modes. These hardware-based enclaves encrypt data even during processing, meaning that not even Tinfoil—or the cloud provider hosting the workload—can access your data.

But hardware is only part of the equation. Tinfoil makes its security verifiable through cryptographic attestation. Using tools like Sigstore for transparent, auditable logs, users can verify that their data was processed only in secure environments, and nowhere else. This moves beyond "trust us" models of cloud privacy to a "prove it" paradigm.

Who Are the Founders Behind Tinfoil?

The Tinfoil team brings together formidable academic credentials and deep industry experience. Tanya Verma, formerly a systems engineer at Cloudflare, contributed to security protocols used across the internet and helped build the Workers AI platform. Jules Drean and Sacha Servan-Schreiber earned their PhDs at MIT, focusing on secure hardware and privacy-preserving computation. Jules also spent time on NVIDIA’s confidential computing team. Nate Sales, who began building internet infrastructure as a teenager, brings years of hands-on experience with performance and scalability.

This team didn’t just identify a market need—they experienced the frustrations firsthand. Their combined expertise in internet protocols, cryptographic systems, and scalable infrastructure forms the bedrock of Tinfoil’s technology and vision.

What Use Cases Does Tinfoil Enable?

Tinfoil’s architecture unlocks a variety of applications across personal, startup, and enterprise domains—each unified by the need for privacy and performance.

For Individuals:

• Private Chat: Talk freely with AI about mental health, finances, or other sensitive topics, knowing no one else can access the data.
• Private Analysis: Securely process sensitive health or financial datasets for personal research or planning.
• Smart Home Assistants: Use AI at home without it “listening in” for marketing or surveillance purposes.

For Startups:

• Privacy-Preserving Products: Launch AI products that users can trust with their data—because no one, not even the startup, can see it.
• Verified Moderation Tools: Apply AI for content moderation where privacy is guaranteed and verifiable.
• Enterprise-Ready Compliance: Meet the strictest compliance requirements from day one without sacrificing speed or capability.

For Enterprises:

• AI Assistants for Code and Docs: Use AI to help developers write proprietary code—without risking leaks to third parties or model providers.
• Secure AI Collaboration: Collaborate across teams with confidence that sensitive IP remains fully protected.
• Cloud Providers: Offer clients secure AI services without needing them to trust your infrastructure blindly.

What Sets Tinfoil Apart from Other AI Security Tools?

Most AI “security” tools rely on access controls, user policies, or contractual guarantees. But these mechanisms are only as trustworthy as the provider—and are often unverifiable.

By contrast, Tinfoil uses hardware-enforced enclaves and cryptographic attestation to create a secure, verifiable execution environment. Apple’s Private Cloud Compute offers something similar, but it’s closed to Apple’s ecosystem. Tinfoil brings this level of privacy and assurance to the broader AI ecosystem, especially for those using open-source models.

Unlike third-party APIs like OpenAI’s or Anthropic’s, which inevitably send your data to opaque backends, Tinfoil ensures your data never leaves the hardware boundaries you control. That’s not just privacy—it’s sovereignty.

Is Tinfoil Easy to Integrate?

Yes. A key part of Tinfoil’s value is ease of use. No redaction. No data handling modifications. No retraining models. You simply point your existing API calls—those compatible with OpenAI’s Chat Completions API—at Tinfoil’s endpoints, and everything else is handled under the hood.

This means developers can secure their applications in hours, not weeks. With built-in support for open-source models like Llama and DeepSeek, or even customer-supplied models, Tinfoil fits into almost any AI stack.

How Does the Technology Work?

Tinfoil’s privacy architecture is built on three pillars:

1. Trusted Execution Environments: Enclaves on GPUs (like NVIDIA Hopper and Blackwell) and CPUs (e.g., AMD SEV, AWS Nitro) isolate the runtime from the host OS and cloud provider.
2. Cryptographic Attestation: Each runtime emits attestations proving its integrity. These are logged via tools like Sigstore, enabling third-party verification.
3. Open-Source Software Stack: Tinfoil’s core components are auditable, enabling transparency and fostering community trust.

Combined, these features ensure that AI inference, training, or RAG workflows can run in a way that is not only private—but provably so.

What Limitations Should Users Be Aware Of?

Tinfoil currently supports open-source or custom models only. It’s not compatible with proprietary models like GPT-4 or Claude, whose APIs require data to leave user-controlled environments. Similarly, tools like GitHub Copilot or Perplexity, which operate as hosted services, cannot be routed through Tinfoil unless they’re reimplemented using open-source counterparts.

However, this tradeoff is intentional. Tinfoil prioritizes verifiability and data ownership over convenience, offering unmatched control to those who value security above all.

What Are Tinfoil’s Analytics and Observability Capabilities?

Tinfoil balances privacy with visibility. It offers Prometheus-compatible metrics using privacy-preserving analytics and secure aggregation protocols. This means developers can debug, monitor, and scale their AI applications without compromising user data.

No personally identifiable information (PII) is ever exposed, even during monitoring. Observability doesn’t have to come at the cost of confidentiality.

What Does the Future Hold for Tinfoil?

As AI becomes more powerful, the risks of data misuse and leakage grow exponentially. Tinfoil represents a shift in how we think about trust in AI. Rather than relying on corporate policies or third-party audits, Tinfoil offers cryptographic certainty.

Its architecture could soon become the default infrastructure layer for any AI product that deals with sensitive information, just as TLS once revolutionized the internet by enabling secure online payments.

In the founders’ own words, “If OpenAI is creating ‘God in a box,’ we are putting God in a black box, so Satan can’t spy.” That may sound cheeky, but for those working with AI at the frontier of privacy, it’s deadly serious.