> ## Documentation Index
> Fetch the complete documentation index at: https://docs.phala.com/llms.txt
> Use this file to discover all available pages before exploring further.

> Explore real-world applications and use cases for TEE technology.

# Overview

TEE provide hardware-based isolation for sensitive computations, ensuring data confidentiality and integrity. This document explores various use cases where TEEs can be combined with other technologies to create powerful, secure solutions.

## Industry Success Stories

Explore real-world implementations across different sectors:

* [Financial Services](https://phala.com/success-stories/financial-services) - Confidential AI for banking and trading
* [Healthcare & Research](https://phala.com/success-stories/healthcare-research) - HIPAA-compliant AI and medical data privacy
* [B2B SaaS Platforms](https://phala.com/success-stories/ai-saas-platform) - Enterprise AI privacy and multi-tenant security
* [Decentralized AI](https://phala.com/success-stories/decentralized-ai) - Web3 and blockchain AI applications

## TEE + AI

TEEs can protect AI model training and inference, preserving both model privacy and data confidentiality. This combination enables secure machine learning on sensitive data while preventing unauthorized access to proprietary models.

TEE also can be used to build unruggable AI Agents that operate with the same level of trustworthiness as smart contracts, check [the article by Phala CEO Marvin Tong](https://x.com/marvin_tong/status/1866319231350100330) to see why we get there.

Check out [Building Confidential AI with TEE](/phala-cloud/cases/tee_with_ai)

## TEE + FHE/MPC

TEEs can complement Fully Homomorphic Encryption (FHE) and Multi-Party Computation (MPC) to create hybrid systems with enhanced performance and security guarantees. These combinations allow for secure computation on encrypted data with reduced overhead.

Check out [TEE with FHE and MPC](/phala-cloud/cases/tee_with_fhe_and_mpc)

## Private Proving with TEE

**Private Proving** runs zero-knowledge provers inside TEE to protect sensitive data during proof generation. While ZK proofs verify computation correctness, they don't prevent the prover from accessing your private data. Running ZK provers in TEE solves this critical problem - your transaction data, API keys, AI models, and business logic stay hardware-encrypted even during proving.

Phala Cloud provides production-ready infrastructure for private proving with GPU TEE (H100/H200), enabling developers to generate ZK proofs at hardware speed while keeping sensitive inputs encrypted at the silicon level.

Check out [Private Proving with TEE](/phala-cloud/cases/tee_with_zk_and_zkrollup)