Private AI Data

Unlock sensitive data—without sharingit

Confidential compute turns siloed and regulated datasets into insight, safely. Combine data sources via compute-to-data and verifiable enclaves.

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Zero-trust architecture

Remote attestation

Encrypted data processing

Why It Matters

Why Private AI Data Matters

Data sharing is blocked by privacy, IP, and regulation. Centralized AI leaks control. Phala keeps raw data sealed while models 'go to the data'—enabling multi-party analysis without exposing sensitive information to any single entity.

Privacy regulations (GDPR, HIPAA, CCPA) prevent data sharing

Traditional cloud infrastructure exposes sensitive information to operators and administrators.

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IP protection demands prevent collaboration

Hardware-enforced isolation prevents unauthorized access while maintaining computational efficiency.

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Centralized AI exposes sensitive business intelligence

End-to-end encryption protects data in transit, at rest, and critically during computation.

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Traditional methods require trust in third parties

Cryptographic verification ensures code integrity and proves execution in genuine TEE hardware.

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Hover to Encrypt

TEE Hardware Encryption

Zero-Trust Computing

Confidential Computing

Hardware-Enforced Encryption for AI & Data Workloads

TEEs with Intel TDX and AMD SEV provide CPU-level memory encryption—your AI models, datasets, and computations stay encrypted in-use. Not even cloud admins or hypervisors can inspect runtime state. Remote attestation proves the enclave is genuine before you send data.

Built on zero-trust principles, our confidential computing infrastructure ensures data remains encrypted throughout the entire computation lifecycle. Hardware root-of-trust, sealed storage, and cryptographic proofs provide verifiable protection against insider threats and infrastructure compromise.

Memory encryption in-use

Remote attestation proofs

Hardware root-of-trust

How It Works

Deploy confidential data workloads in three simple steps

Deploy Data Enclave

Set up confidential data processing environment

Process Private Data

Compute on encrypted data inside TEE

Verify Attestation

Cryptographically verify TEE execution

data-enclave.yaml

services:
  dstack-service:
    image: phala/dstack-service:latest
    restart: unless-stopped
    volumes:
      - /var/run/docker.sock:/var/run/docker.sock
    environment:
      - VPC_NODE_NAME=data-processor-${NODE_IND}
      - VPC_SERVER_APP_ID=${VPC_SERVER_APP_ID}

  data-processor:
    image: python:3.11-slim
    container_name: data-processor
    restart: unless-stopped
    working_dir: /app
    environment:
      - NODE_NAME=data-processor-${NODE_IND}
    configs:
      - source: processor_script
        target: /app/processor.py
        mode: 0644
      - source: requirements
        target: /app/requirements.txt
        mode: 0644
    command: |
      sh -c "pip install -r requirements.txt && python processor.py"
    dns:
      - 100.100.100.100
    dns_search:
      - dstack.internal
    depends_on:
      dstack-service:
        condition: service_healthy

configs:
  processor_script:
    file: ./processor.py
  requirements:
    file: ./requirements.txt

USE CASE

Confidential AI Training

Train proprietary LLMs on confidential datasets without exposing raw data to cloud providers.

USE CASE

Private Inference

Deploy inference APIs for healthcare, finance, or legal AI where model weights and user prompts must remain encrypted end-to-end.

USE CASE

Federated Learning

Run federated analytics on multi-party datasets—each party keeps data local while TEEs combine insights securely.

USE CASE

Data Clean Rooms

Enable secure multi-party computation for joint data analysis without revealing individual contributions.

USE CASE

Regulatory Compliance

Process regulated data (GDPR, HIPAA) in the cloud while maintaining compliance and zero-trust security.

What our Users say Proudly

FEATURES

Instant Implementation
One-Time Payment
Developer Friendly
Fully Responsive
Production Ready
Premium Support
Regular Updates
Customizable Design
Performance Optimized
Accessibility Compliant
Cross-Browser
Documentation Included

A well-designed system (like Vana) uses both crypto consensus where you don't trust hardware, and TEEs for privacy-specific applications

Anna Kazlauskas

Founder of Vana

Phala made it possible for us to build an AI retrieval engine that never exposes what it sees. Our users trust Xtrace because their private data stays encrypted, even while the model is thinking.

Felix Meng

Founder of Xtrace

I'm totally TEE pilled. From OpenAI to Apple, both top-down and bottom-up, the focus has shifted to making TEE tech actually usable and easy to integrate

Conan

Founder of Rena Labs

Vana

DATA SOVEREIGNTY / USER-OWNED AI

User-owned data pools & collective model training via confidential compute.

Read case study

Rena Labs

VERIFIABLE AI / zkTLS

Verifiable AI responses with proof of data source.

Read case study

Xtrace

LLM OBSERVABILITY / PRIVACY

Private LLM observability without exposing prompts.

Read case study

Cloud Attestation API

Developer Experience
IN 5 MINUTES.

Deploy confidential data workloads with familiar tools and infrastructure.

View Docs

verify-quote.sh

# Verify attestation quote
curl -X POST "https://cloud-api.phala.network/api/v1/attestations/verify" \
  -H "Content-Type: multipart/form-data" \
  -F "file=@quote.bin"

# Response - verified TEE attestation
{
  "success": true,
  "quote": {
    "verified": true,
    "header": { "tee_type": "TEE_TDX" },
    "report_data": "0x9aa049fb...",
    "mr_enclave": "a1b2c3d4..."
  },
  "checksum": "9aa049fb9049d4f582ca316206f7cf34ee185c2b..."
}

# Share verification proof
https://proof.t16z.com/reports/9aa049fb9049d4f582...

Industry-Leading Enterprise Compliance

Meeting the highest compliance requirements for your business

FAQ

Common Questions & Answers

Find out all the essential details about our platform and how it can serve your needs.

How does Phala keep my data private during computation?

Phala runs every workload inside a Trusted Execution Environment (TEE). Data, model, and code are decrypted only inside this enclave's CPU memory and are re-encrypted before leaving. Neither cloud operators nor Phala nodes can access the plaintext at any point.

What's the difference between data encryption and confidential computing?

Encryption protects data at rest and in transit. Confidential computing extends that protection to 'data in use.' Even while an AI model processes the data, the enclave keeps it sealed from the host OS, hypervisor, and other tenants.

Can Phala process encrypted data without decrypting it first?

Yes—within the enclave the decryption keys are injected only after remote attestation proves the correct code is running. From the outside, the data remains opaque; decryption happens solely in verified hardware.

How is data deleted or retained after a computation?

When a task finishes, the enclave's memory is automatically cleared. Storage volumes can be configured for ephemeral or persistent use; all are encrypted with customer-owned keys so deletion is cryptographically final.

Can multiple organizations collaborate without sharing raw data?

Yes. Phala's 'compute-to-data' model lets each party keep its dataset local while a joint enclave aggregates gradients or insights. No participant ever gains access to another's raw files.

How do I monetize data safely through a marketplace on Phala?

Data providers publish encrypted datasets with usage policies enforced by smart contracts. Buyers run approved models against those datasets in enclaves and pay automatically for each job, while the raw content never leaves its owner.

What prevents others from reverse-engineering my datasets?

Enclaves disable debugging, tracing, and memory inspection. The only accessible output is the model result explicitly defined by the provider. Intermediate tensors and parameters stay sealed.

How does Phala handle data ownership and access rights?

Ownership is defined on-chain through tokenized access keys. Every compute job references these keys, producing auditable logs that prove who accessed what and under which policy.

Is Phala compliant with GDPR, HIPAA, or other regulations?

Phala's architecture supports compliance by design: data minimization, encryption, and deterministic audit trails. Actual certification depends on the workload and jurisdiction, but the platform satisfies the technical controls required by most frameworks.

Can I use Phala with my existing cloud or data lake?

Yes. Phala connectors let you attach S3, GCS, or on-prem sources through secure API gateways. Data stays encrypted until loaded into the enclave.

Does confidential computing affect model performance or cost?

Hardware-assisted TEEs add minimal overhead—typically <5%. GPU TEEs keep acceleration intact, so you pay roughly the same cloud rate while gaining privacy guarantees.

How can I verify that my computation truly ran inside a secure enclave?

Each job exposes a remote-attestation report signed by the CPU vendor. You or your clients can validate this proof to confirm the enclave type, firmware version, and the exact code hash that executed.

Explore Our Solutions

Discover how Phala Network enables privacy-preserving AI across different use cases

Private AI Data

Monetize and analyze sensitive data with TEEs and remote attestation—without exposing the raw data

Private AI Inference

Run AI models with end-to-end encryption to protect user inputs, outputs, and model IP

Fine-Tuned Models

Train and deploy custom AI models in secure enclaves while protecting your proprietary data

Confidential Training

Train AI models on sensitive data without exposing it, ensuring privacy and compliance

AI Agents

Build autonomous AI agents with cryptographic privacy guarantees for enterprise workflows

Ready to unlock your data?

Deploy confidential data workloads on Phala's trusted execution environment. Start with our free tier or talk to our team about enterprise deployments.

Get Started

Deploy in minutes
Remote attestation built-in
Enterprise support available
SOC 2 / GDPR ready
Open source tools

Build AI People Can Trust.

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Unlock sensitive data—without sharingit

Why Private AI Data Matters

Privacy regulations (GDPR, HIPAA, CCPA) prevent data sharing

IP protection demands prevent collaboration

Centralized AI exposes sensitive business intelligence

Traditional methods require trust in third parties

Confidential Computing

Hardware-Enforced Encryption for AI & Data Workloads

How It Works

Deploy Data Enclave

Process Private Data

Verify Attestation

Confidential AI Training

Private Inference

Federated Learning

Data Clean Rooms

Regulatory Compliance

What our Users say Proudly

User-owned data pools & collective model training via confidential compute.

Verifiable AI responses with proof of data source.

Private LLM observability without exposing prompts.

Developer ExperienceIN 5 MINUTES.

Industry-Leading Enterprise Compliance

Common Questions & Answers

How does Phala keep my data private during computation?

What's the difference between data encryption and confidential computing?

Can Phala process encrypted data without decrypting it first?

How is data deleted or retained after a computation?

Can multiple organizations collaborate without sharing raw data?

How do I monetize data safely through a marketplace on Phala?

What prevents others from reverse-engineering my datasets?

How does Phala handle data ownership and access rights?

Is Phala compliant with GDPR, HIPAA, or other regulations?

Can I use Phala with my existing cloud or data lake?

Does confidential computing affect model performance or cost?

How can I verify that my computation truly ran inside a secure enclave?

Explore Our Solutions

Private AI Data

Private AI Inference

Fine-Tuned Models

Confidential Training

AI Agents

Ready to unlock your data?

Developer Experience
IN 5 MINUTES.