Use Case
Confidential AI for Regulated Enterprise
ML inference pipelines that pass internal data-governance review and produce cryptographic audit evidence for external regulators — without slowing down your engineering team.
The data-governance bottleneck
Insurers running claims-fraud models, telecom operators training churn-prediction models on subscriber data, logistics companies running routing optimisation on commercial cargo manifests — all face the same structural constraint: internal data-governance policy restricts which compute environments can access sensitive operational data, and cloud AI inference infrastructure does not clear the technical-control bar.
The practical outcome is a slow lane for AI adoption. Every new workload that touches governed data requires a DLP review, a risk assessment, and typically a lengthy approval cycle. Engineering teams respond by building data masking and stratified-sampling pipelines to produce "safe" training and inference datasets. Those pipelines are expensive to maintain, degrade model quality by removing the signal that makes the AI useful, and still require periodic re-approval as data classification policies evolve.
Shifting the question from data safety to infrastructure safety
Almure's enclave boundary moves the compliance conversation from "is this data safe to expose to the inference layer?" to "is the inference layer architecturally safe to receive the data?" When a workload runs inside a hardware TEE with cryptographic attestation, the evidence that data was processed under specific technical controls is generated automatically — no manual data masking, no pipeline preprocessing step, no post-hoc compliance documentation.
- The enclave measurement hash proves which exact code version ran on the data — no approved-build substitution is possible without detection
- The attestation report, hardware-signed by the CPU, proves the execution environment's isolation properties at the moment of execution
- The sealed key lifecycle proves data decryption was scoped to that specific execution context and no key material left the hardware boundary
Together, these three controls answer the question that blocks data-governance approval: "How do we know only the approved model ran on this data, inside an isolated boundary, with no pathway for the cloud provider to access the plaintext?"
Pilot narrative — insurance technology group, claims data
An insurance technology group building a claims-anomaly detection model had been working with a 30-day delayed, partially masked copy of its claims database for model training and inference — a dataset that required a dedicated ETL pipeline and lost the temporal and contextual signals the model needed. The data-governance team would not approve inference on live claims data because the cloud inference host had no documented technical isolation from the cloud provider's management plane. After deploying Almure, the model runs inside a hardware enclave on current claims records. The attestation log produced by each execution became the technical evidence the data-governance team needed: it shows exactly which model version ran, on which data, inside which verified boundary. The approval cycle for new model versions dropped from six weeks to one — the attestation report answers the audit question directly.
SIEM integration and regulatory audit
Almure's structured attestation logs integrate with standard SIEM pipelines. Each execution record contains workload identifier, measurement hash, policy version enforced, and result disposition. For external regulatory inspections — FISC audits for Japanese financial institutions, or internal governance reviews in other regulated sectors — the log provides hardware-grounded evidence that data was processed under documented technical controls, without requiring manual attestation from operators.
Almure does not claim certification against any specific regulatory framework. We design the platform with reference to APPI and FISC technical-control requirements. Compliance determination remains with the deploying organisation and its legal and compliance team.
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