Private financial AI with audit-ready proof.
Confidential compute lets financial teams use cloud AI for analysis, fraud detection, and collaborative risk modeling while sensitive inputs stay sealed and every run can be audited.
sealed analysis
01
Financial data cannot leak
Portfolios, transaction streams, trading strategies, and M&A analysis carry regulatory liability and competitive value.
02
Cloud AI creates audit gaps
If a provider, operator, or shared host can inspect the workload, compliance teams cannot treat the model run as private.
03
Risk models need collaboration
Banks, funds, and payment teams want better models, but raw data sharing concentrates the most sensitive rows in one place.
solution mockup
Compute travels to the financial data. The data does not travel to the operator.
Reusing the private-data solution mockup: each owner seals data locally, the approved analysis runs inside a CVM, KMS releases keys only after attestation, and the output is signed for audit.
Compute-to-Data on dstack
Sealed data stays at source · the model travels · multi-owner approval gates every key release
Sealing at Source
Each owner runs a local sealing CLI: HKDF(kms_root_pubkey, analysis_app_id, analysis_compose_hash, owner_id). Encrypts the dataset, publishes ciphertext. Owners never ship plaintext or keys. Change the recipe → key no longer matches.
use cases from the report
Where finance needs confidential compute.
Portfolio analysis with private inference
Run recommendation, suitability, and exposure analysis while customer holdings stay inside a measured runtime.
Fraud detection on transaction data
Use richer payment signals without moving cardholder or account data into a standard cloud AI boundary.
Cross-institution risk modeling
Let multiple data owners co-approve an analysis build, then compute shared risk outputs without exposing source ledgers.
report
Private Financial AI
Founder welcome video
16 sec
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