Your ML team wants to train on the real data: actual transactions, actual claims, actual interactions, actual outcomes. Today they get a sanitized extract that strips the signal, a synthetic surrogate that drifts from reality, or a months-long clean-room build. The high-value models stay trapped behind the data classification policy. This solution unlocks the real corpus. Train, evaluate, score, and run analytics on protected data in the compute environments you already trust: Databricks, SageMaker, Snowflake, Vertex AI, Azure ML, your own on-prem GPUs. Enigmata Anonymizer for workflows where reversal would never be permitted. Enigmata Cipher for workflows where authorized reveal is required. Audit on every step.
Features
Train and fine-tune models on protected data without ever decrypting it into the training environment. Same accuracy as the plaintext baseline on representative workloads.
Run batch analytics, feature engineering, and population-level scoring against encrypted indexes. Compute happens in your existing stack; raw values stay protected.
Evaluate models against protected test sets using the same crypto core, so eval is faithful to production conditions and the metrics are audit-ready.
Compose with Enigmata Converge for training-efficiency gains and Enigmata Unlearn for verifiable record-level retraction when regulations require it.
Cloud-agnostic: SageMaker, Databricks, Snowflake, Vertex AI, Azure ML, your own on-prem clusters. Enigmata ships native containers and SDKs for each.
Every training run, eval, and analytics job links to the policy version it ran under, with the audit trail your model-risk team can defend in front of regulators.
Benefits
Unblock the models your data classification policy keeps blocking.
Train on the real corpus instead of degraded surrogates that miss the signal.
Cut the data-prep ↔ legal-review ↔ infosec-review loop from quarters to days.
Use the cloud ML platforms you already pay for, without expanding the data classification scope they sit in.
Generate the model-risk dossier (SR 11-7, EU AI Act, sectoral) every regulated ML project needs, automatically.
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Train the model your data classification policy has been blocking.
Sixty-day pilot on the dataset your ML team wants but legal won’t release. Enigmata Anonymizer + Enigmata Cipher + Enigmata Converge stand up the training and eval pipeline in your existing cloud (SageMaker, Databricks, Snowflake, Vertex AI, Azure ML, or your own GPUs), match or beat your plaintext baseline on model accuracy, and produce the model-risk dossier your CISO, legal team, and regulators can sign off on.