Use cases / Confidential AI & document processing
Process sensitive documents within a defined trust boundary
Assess a protected environment for sensitive document processing, with attestation, controlled secret release, and defined output access.
Illustrative example; not a completed customer project.
- 01Map the disclosure
- 02Verify the environment
- 03Process within the boundary
- 04Measure and decide
Use cases
Workflow steps
Map the disclosure
Identify document inputs, permitted outputs, and external calls.
Verify the environment
Check attestation against the agreed policy before releasing secrets.
Process within the boundary
Run the selected model and inspect logs, storage, and output paths.
The sensitive document problem
A team wants extraction, summarization, or retrieval over confidential material. Identify which infrastructure operator is outside the trust boundary and which application users may receive the results. A TEE can be evaluated against those specific requirements.
Acceptance checks and limits
Test that invalid or outdated attestation blocks secret release, unauthorized users cannot read results, and plaintext does not reach unintended logs or external services. Compare model quality, latency, and cost with the baseline. TEE protection relies on the selected hardware, software, and policy; document content and permitted outputs still need review.
What you receive
What we can deliver
A data-flow and threat model with protected information, trust assumptions, and permitted disclosure.
A method comparison and a bounded prototype or integration using established components.
Recorded correctness checks, negative tests, and security assumptions for the agreed boundary.
Key, attestation, output, and operating requirements, with known limitations and next steps.
Related services and examples
Contact
Scope a confidential AI pilot
Describe the documents, model, infrastructure, permitted users, and disclosure limits. A first study can test one workflow and one environment.
Discuss confidential AI