Your AI is only as good as the data it trains on — and most enterprise data is not AI-ready. DTS solves unusable data for AI: whether it's restricted by privacy rules, imbalanced, or missing the coverage your model needs. The result is an AI-ready dataset you can actually use.
DTS includes privacy-safe synthetic data generation to expand coverage and repair imbalance when real data is restricted or incomplete.
Synthetic data generation is one capability inside DTS -- not the company identity. DTS uses differential privacy as its mathematical foundation, providing formal guarantees that synthetic outputs cannot be reverse-engineered to individual records. This makes DTS suitable for regulated industries that need AI-ready datasets without exposing raw training data.
DTS is a capability within Cubig's AI-Ready Data Infrastructure -- the infrastructure layer that makes enterprise data usable, privacy-safe, and stable for production AI execution. DTS specifically addresses the Restricted Data and Unusable Data blockers.
Databricks stores your data. Masking removes it. DTS makes it AI-ready -- without removing or exposing it.
| Capability | DTS | Masking | Sampling | Manual |
|---|---|---|---|---|
| Privacy guarantee | ✓ Mathematical DP bound | △ Re-identification risk remains | ✕ No privacy guarantee | ✕ |
| Coverage expansion | ✓ Generate at any scale | ✕ Can't create new data | △ Bounded by real data volume | △ Expensive & slow |
| Rare class augmentation | ✓ Targeted generation | ✕ | ✕ Can't create rare events | △ Very high cost |
| Distribution fidelity | ✓ Validated against real stats | △ Distorted by masking | △ Sampling bias risk | △ Annotator variance |
| Cross-border / external use | ✓ No real data transferred | ✕ Residual risk | ✕ | ✕ |
| SynTitan integration | ✓ Native versioning & binding | ✕ | ✕ | ✕ |
Differential privacy (DP) is a mathematical framework that guarantees any single individual's data cannot be identified from the synthetic output -- regardless of what an attacker already knows.
DTS analyzes the real dataset's statistical properties -- distributions, correlations, marginals -- without storing raw records.
Enterprise AI projects stall when data conditions prevent training, validation, or safe deployment. DTS was built for exactly these situations.
GDPR, PIPA, HIPAA, or internal retention policies prevent the data from reaching models. DTS generates privacy-safe synthetic replacements -- statistically accurate, legally usable, zero real records exposed.
Rare classes are underrepresented. Fraud patterns are too sparse to learn from. Edge cases never appear in training data. DTS fixes class distribution and generates targeted rare-class coverage.
Historical data was deleted per retention policy. DTS generates synthetic equivalents from surviving statistical patterns -- without requiring the original data to still be present.
Classified, patient, or customer data cannot be exported for AI training. DTS's Zero-Access Architecture learns statistical properties in-situ. Only the DP-protected synthetic output crosses the boundary.
The original dataset is too small to train a robust model. DTS augments existing datasets to production-grade volume -- preserving statistical fidelity while adding the volume AI training requires.
In each case, DTS turns data that is restricted or unusable into an AI-ready dataset -- without exposing real records.
A mathematical framework that guarantees any single individual's data cannot be identified from the synthetic output -- regardless of what an attacker already knows. DTS applies DP during generation to produce datasets that are statistically representative but contain no real personal information.
Original data never leaves the client environment. DTS analyzes statistical properties in-situ, generates a DP-protected synthetic model, and only the synthetic output is used downstream. Raw data is never transferred or accessed externally -- suitable for classified, regulated, and air-gapped environments.
DTS is Cubig's enterprise synthetic data engine. It generates privacy-safe datasets using differential privacy to fix class imbalance, fill coverage gaps, expand training data, and replace restricted or non-accessible data. DTS runs as a standalone engine or integrates with the SynTitan platform.
Enterprise AI projects stall when data conditions prevent training, validation, or safe deployment. DTS was built for exactly these situations.
DTS is CUBIG's enterprise synthetic data engine. It generates privacy-safe datasets using differential privacy to fix class imbalance, fill coverage gaps, expand training data, and replace restricted or non-accessible data. DTS runs as a standalone engine or integrates with the SynTitan platform.
DTS turns restricted, unusable, and inaccessible enterprise data into privacy-safe synthetic datasets -- without ever moving the original data. GS Certified. KISA approved. Available on AWS Marketplace.
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