Eight real-world use cases where hardware Trusted Execution Environments solve security and compliance problems that encryption and access controls alone cannot.
Each use case below describes a security or compliance problem that standard cloud architecture cannot solve — and the specific TEE capability that resolves it.
Run LLM inference on regulated data — financial records, health data, legal documents, classified content — inside a hardware TEE. Prompts and responses are encrypted in memory at the CPU level, invisible to the host OS, hypervisor, and cloud operator. The model processes your data without the infrastructure ever seeing it in plaintext.
Multiple organizations train or run inference on a shared AI model without any party seeing the others' raw data. Each organization's input is sealed in a TEE. The model processes all sealed inputs and returns only the result. No data crosses organizational boundaries — not even to the TEE operator. Required for cross-bank AML, multi-hospital research, and cross-agency intelligence analysis.
When a fine-tuned LLM runs inside a TEE, its weights are loaded into hardware-encrypted memory and cannot be read by the host, the cloud operator, or any privileged process. The model serves inference without its weights ever being accessible outside the enclave. Essential for organizations deploying commercially valuable proprietary models on shared or co-located infrastructure.
Process Protected Health Information with AI inside the hospital infrastructure — without a third-party cloud processor receiving PHI. Clinical decision support, summarization, and RAG over internal knowledge bases run inside a TEE or on-premise. Presidio PII/PHI detection redacts identifiers before they reach the model. Audit trail satisfies HIPAA audit control requirements.
Run risk models, fraud detection, and financial analysis on confidential data with hardware-level isolation. DORA (in force January 2025) requires EU financial institutions to manage ICT third-party risk — on-premise confidential computing eliminates the third-party AI vendor from the risk register entirely. Cross-bank AML analytics via Prism AI allow joint fraud detection without sharing raw transaction records between competing institutions.
Government AI workloads on citizen data, policy content, and classified information require hardware isolation and cryptographic proof of integrity. TEE-based deployment with remote attestation provides evidence that the correct, unmodified AI system is running — stronger than any contractual assurance. Air-gapped TEE deployment for classified missions combines physical network isolation with hardware memory encryption.
Research institutions collaborate on shared AI models without pooling participant data. Each institution's records stay sealed in a TEE — no other party, including the research coordinator, can access them. Only aggregate model improvements or statistics are returned. Enables multi-site clinical trials, cross-border genomics research, and federated studies that would otherwise require legally complex data sharing agreements.
Cryptographic keys and certificates managed inside a TEE cannot be exfiltrated even by a privileged host process. The enclave performs signing, decryption, and key derivation operations without the key material ever leaving hardware-encrypted memory. Remote attestation verifies that the correct key management software is running before any key operation is authorized — a stronger guarantee than software HSMs.
Every use case above depends on two properties that only hardware TEEs can provide: encrypted computation and remote attestation. Neither property is available from any software-only solution.
How confidential computing worksCocos AI provides the TEE foundation. Cube AI delivers private AI inference and governance. Prism AI enables multi-party collaboration. Each is open-source at its core.
The open-source TEE abstraction layer — AMD SEV-SNP, Intel TDX, remote attestation, and key management. The foundation all other use cases build on.
Explore Cocos AIPrivate LLM inference, RAG, guardrails, governance, and audit — running inside a TEE on infrastructure you control. The platform for private AI inference use cases.
Explore Cube AIMulti-party AI without data sharing. Run joint models across organizational boundaries with every party's data sealed in a TEE. The platform for multi-party use cases.
Explore Prism AIThe primary use cases are: (1) private AI inference — running LLMs on sensitive data without exposing it to the infrastructure operator; (2) multi-party AI collaboration — training or evaluating shared models across organizations without any party seeing the others' data; (3) financial analytics — fraud detection and risk modeling on confidential financial records; (4) healthcare AI — clinical data processing within HIPAA and GDPR constraints; (5) secure key management — cryptographic material managed inside hardware that cannot be extracted.
In multi-party AI, multiple organizations contribute data to a shared model without any party — including the TEE operator — being able to read the other parties' inputs. Each organization's data is sealed inside a Trusted Execution Environment. The model trains or runs inference over all sealed inputs. Only the aggregate result is returned. No raw data crosses organizational boundaries at any point.
Healthcare confidential computing enables two things standard encryption cannot: (1) AI inference on Protected Health Information inside a hospital's own infrastructure without exposing PHI to the inference platform operator; (2) multi-institution AI research where each hospital's patient records stay sealed in their own TEE, allowing shared model training without pooling records. Both satisfy HIPAA requirements that standard cloud API deployments cannot meet.
Yes. When an LLM runs inside a TEE, model weights are loaded into hardware-encrypted memory. The host OS, hypervisor, and cloud operator see only ciphertext. The weights cannot be read or exfiltrated by anyone with privileged access to the hardware. This is the primary use case for organizations serving proprietary fine-tuned models on shared or third-party infrastructure.
In financial services, TEEs enable: (1) risk model inference on confidential financial data without exposing the data to the infrastructure team; (2) cross-bank AML collaboration — multiple banks run shared fraud detection on transaction data without sharing raw records; (3) confidential trading strategy execution — strategy logic and position data stay encrypted in use; (4) DORA-compliant AI deployment with hardware-level evidence of data isolation.
Yes. Defense and intelligence agencies use confidential computing for classified AI workloads where hardware isolation is a hard requirement. TEEs provide cryptographic proof — via remote attestation — that the correct, unmodified AI system is running in an isolated enclave. This is stronger than any software-level or contractual assurance. Air-gapped TEE deployment combines physical network isolation with hardware memory encryption for maximum protection.
Talk to the team about your specific workload, data classification requirements, and the right TEE architecture for your deployment.
