The AI Privacy Dilemma
Users tell AI agents their most sensitive information: health questions, financial details, business strategies, personal struggles. This data is gold for building great AI experiences—and a massive privacy liability.
- Data exposure: Conversations stored in plaintext on provider infrastructure
- Insider threats: Employees can access user data
- Model training: User data may be used without explicit consent
- Prompt injection: Attacks can manipulate agents to leak data
CIFER solves this with confidential computing. Your AI agents process data inside hardware enclaves where even infrastructure operators can't access it.
What You Can Protect
Conversation History
Encrypt user conversations so AI agents can maintain context without exposing sensitive discussions to operators or infrastructure.
Agent Memory & State
Protect long-term agent memory including learned preferences, user profiles, and accumulated knowledge across sessions.
Autonomous Actions
Enable agents to perform authenticated actions (API calls, transactions) with encrypted credentials they can use but not expose.
RAG & Knowledge Bases
Secure retrieval-augmented generation with encrypted document stores that only authorized agents and users can query.
Why Build with CIFER
How It Works
User Sends Request
User message is encrypted before leaving their device. Only the TEE can decrypt it.
TEE Decrypts & Processes
Inside the hardware enclave, the message is decrypted, combined with encrypted memory, and processed by the AI model.
Encrypted Response
AI response is encrypted before leaving the TEE. Only the intended user can decrypt it.
Memory Persisted Encrypted
Conversation context and agent state are encrypted and stored. They remain private across sessions.
Frequently Asked Questions
How can AI agents process encrypted data?
CIFER runs AI workloads inside Trusted Execution Environments (TEEs). Data is decrypted only within the hardware-isolated enclave, processed by the AI model, and results are encrypted before leaving. Neither operators nor the AI provider can see the plaintext.
Does this work with existing AI models?
Yes. CIFER provides an encryption layer that wraps existing models (GPT, Claude, open-source LLMs). The model runs inside TEE infrastructure, and CIFER handles encryption/decryption of inputs, outputs, and memory.
What about prompt injection attacks?
Even if an attacker succeeds in manipulating the AI through prompt injection, the encrypted data cannot leave the TEE boundary. The hardware enforces that sensitive information can only be output through encrypted channels to authorized recipients.
Can agents remember across sessions?
Yes. Agent memory and state are encrypted and persisted. When a user returns, the TEE decrypts the relevant context for that specific user. Different users' data remains cryptographically separated.
How do I integrate this with my AI application?
CIFER provides APIs that mirror standard AI provider interfaces. In most cases, you change the endpoint URL and add authentication. Your existing code continues to work while gaining confidential computing guarantees.