Next up in our DataHaven Partner Spotlight Series is a partner solving one of the most difficult challenges in AI today: confidential computation. Phala brings hardware-secured, privacy-preserving compute to the AI stack, while DataHaven delivers the private verifiable storage layer that sensitive applications rely on. Together, they form a path toward end-to-end confidential AI—where data stays protected, actions remain verifiable, and builders gain the trust guarantees required for real-world deployment.
Phala provides confidential compute through TEEs and secure enclaves, enabling AI models to process highly sensitive information—medical data, financial records, regulated datasets, or private agent memory—without ever exposing the underlying inputs. It is the foundation for privacy-preserving inference and secure, compliant AI logic.
DataHaven complements this by acting as the secure, private repository where results, intermediate computations, and sensitive state can be stored verifiably and confidentially. Two critical AI resources—trusted compute and private verifiable storage—delivered side by side.
“More often than not, AI is working with highly critical, highly sensitive data that could be catastrophic if leaked. Phala’s TEE allows that data to be processed inside a trusted and secure enclave without risk of exposure. DataHaven furthers this offering by acting as the trusted private storage layer.” ~Ryan Levy, VP of Global BD & Partnerships, DataHaven
In the Wilderness
Phala is the silent sentinel of the Haven: the watchtower built from steel and intention. Its enclaves are the guarded chambers where sensitive work gets done, hidden from prying eyes yet verifiable to all who depend on it. It keeps the most fragile signals safe: whispered secrets, private memories, delicate logic meant only for the agent that carries it.
DataHaven is the vault beneath the forest floor, quiet, sealed, and unshakeable. It’s where the knowledge emerging from those chambers rests, protected and preserved, ready for AI systems to use without ever compromising trust.
One guards the moment of creation. The other safeguards what endures. Together, they form the secure heart of confidential AI.
The Path Ahead
DataHaven’s testnet is available now as a base verifiable storage platform, giving teams a place to anchor private datasets, inference outputs, and agent memory with integrity guarantees.
On mainnet, DataHaven will introduce encryption, verifiability, and privacy, making it simple for companies using Phala’s confidential compute to pair secure processing with secure storage when building AI pipelines and privacy-preserving applications. While we are not co-building infrastructure, our GTM alignment ensures Phala and DataHaven appear together as the confidential AI foundation builders can rely on.
The Bottom Line
AI is rapidly moving toward use cases that depend on confidentiality: healthcare diagnostics, financial intelligence, sensitive enterprise automation, regulated LLMs, and privacy-first agents. Phala is a category leader in trusted execution environments, and by partnering on go-to-market, DataHaven enters the same rooms with the same enterprise-facing builders looking for secure, compliant infrastructure.
This partnership isn’t just alignment; it’s opportunity. With Phala securing computation and DataHaven securing storage, developers gain a trusted path to build the next wave of confidential AI.
How to Join the Journey
Follow Phala on X (@PhalaNetwork) or explore their confidential compute offerings to see how teams are deploying privacy-preserving AI in production. And stay tuned: as DataHaven mainnet approaches, secure compute and private verifiable storage will stand shoulder to shoulder.
Website: phala.network
