DTS is CUBIG's AI-ready data transformation 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 · AI-ready data transformation engine
Rebuild unusable data
Rebuild unusable data
into AI-ready datasets.
Most enterprise data isn't AI-ready. DTS rebuilds restricted, imbalanced, or incomplete data into an AI-ready dataset you can actually use.
It replaces restricted data with privacy-safe substitutes, rebalances skewed datasets through augmentation, and fills coverage gaps by generating new AI-ready data.
Data problems
Three data problems. One engine.
Data that can't be shared, can't be used, or can't be accessed. DTS resolves all three.
Capability
Privacy-safe synthetic data, as a capability.
DTS includes privacy-safe synthetic data generation to expand coverage and repair imbalance when real data is restricted or incomplete. Synthetic data is one capability inside DTS, not DTS's identity. It uses differential privacy as its mathematical foundation, producing AI-ready datasets for regulated industries without exposing raw training data.
Differential Privacy
A formal privacy bound, by design.
What differential privacy means
Differential privacy (DP) is a mathematical framework that bounds how much any single individual's data can influence the synthetic output, so individuals cannot be re-identified, regardless of what an attacker already knows.
DTS applies DP during the generation process itself, not as a post-processing anonymization step. The privacy property is structural, not dependent on masking or field removal.
Unlike masking or redaction, the guarantee is a provable bound, not best-effort obfuscation.
The bound
The probability of inferring any individual from the synthetic dataset is bounded by a mathematically defined epsilon (ε), regardless of external knowledge.
How DTS generates synthetic data
01
Statistical profiling
DTS analyzes the real dataset's statistical properties (distributions, correlations, marginals) without storing raw records.
02
DP noise injection
Calibrated noise is injected into the statistical model according to DP bounds, so individual data points become mathematically unidentifiable.
03
Synthetic generation
New records are sampled from the DP-protected model. Output is statistically representative but contains no real personal information.
04
Fidelity validation
Generated data is validated against the original distribution. Quality and utility metrics confirm suitability for training and validation use.
Deployment
Standalone or integrated with Syntitan.
Mode A · Independent
DTS Standalone
Use DTS without Syntitan, directly against your data sources. Available on AWS Marketplace for enterprise procurement. It fixes AI training-data quality, generating what's missing at scale without touching real data.
- Fix class imbalance: oversample minority classes with distribution fidelity
- Augment sparse datasets to production-grade volume
- Generate edge cases and rare-event samples
- Replace missing values with statistically valid equivalents
Mode B · Integrated
DTS + Syntitan
When privacy or compliance is the blocker (regulated data that can't reach models), DTS runs inside Syntitan to generate privacy-safe replacements. DTS makes the data; Syntitan operates the state around it.
- Replace GDPR, PIPA, HIPAA-restricted data: no original leaves the perimeter (DTS)
- Syntitan versions the synthetic dataset and binds it to a Release State
- Syntitan's change log tracks it from data generation through the AI run
In production
Finance · IBK Industrial Bank
97.6% AI detection rate · 79 patterns → 1,000 records
Fraud and transaction patterns expanded into DP-safe synthetic records. PIPA-compliant, with zero real customer data exported.
Finance · Kyobo Life Insurance
F1 0.92 churn model · 277,249 synthetic records
A 6-month data-retention policy had blocked Kyobo's churn AI. DTS rebuilt DP-safe records from historical data, legally usable after deletion.
Marketing / Sales
90% time reduction · 70% cost saving on trend research
Annual consumer-trend surveys replaced with AI persona agents trained on synthetic behavioral data. Insights in 1–2 days instead of a month.
Defense · Ministry of National Defense
Zero data exports · classified imagery → AI-ready
Deployed on-premise in an air-gapped classified environment. No original imagery left the perimeter; classified data became AI-ready synthetic datasets within clearance.
Comparison
DTS vs. other approaches to restricted data.
| Capability | DTS | Masking / Anonymization | Data Sampling | Manual Labeling |
|---|---|---|---|---|
| Privacy bound | ✓ Formal DP bound (ε) | △ Re-identification risk remains | ✗ None | ✗ |
| 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 | ✗ | ✗ | ✗ |
When to use
Five signals your data is blocking AI.
Enterprise AI projects stall when data conditions prevent training, validation, or safe deployment. If even one of these signals applies, your data is already blocking AI, and DTS was built for exactly these situations.
Restricted Data
Data exists but compliance blocks AI access.
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.
Unusable Data
Imbalanced datasets or coverage gaps distort model behavior.
Rare classes underrepresented, fraud patterns too sparse, edge cases absent from training, so models fail on the exact conditions they were built to catch. DTS fixes class distribution and generates targeted rare-class coverage.
Unusable Data
Retention policies delete what AI needs.
Historical data was deleted per retention policy, so the patterns that trained the previous model no longer exist. DTS generates synthetic equivalents from surviving statistical patterns.
Restricted Data
Sensitive records can't leave the security perimeter.
Classified, patient, or customer data cannot be exported for AI training, even internally. DTS's zero-access architecture learns statistical properties in-situ; only the DP-protected output crosses the boundary.
Unusable Data
Training-data volume is too low for reliable AI.
The original dataset is too small to train a robust model, and collecting more takes months. DTS augments existing datasets to production-grade volume while preserving statistical fidelity.
Outcome
In each case, DTS turns data that is restricted or unusable into an AI-ready dataset, without exposing real records.
See if DTS fits your data
Proof
Proven in production.
+30pp
F1-Score Lift
58.55% → 88.55%
−90%
Time to Deploy
4 weeks → 1 day
97.6%
AI Detection Rate
IBK Industrial Bank
277K+
Synthetic Records
Kyobo Life Insurance
Listed as a Representative Vendor in Gartner®, Emerging Tech: Provider Differentiation Strategy–Trends for Hyper-Synthetic Data (2025).Gartner does not endorse any vendor, product or service depicted in its research publications. GARTNER is a registered trademark of Gartner, Inc. and/or its affiliates.
FAQ
Frequently asked questions
Differential privacy (DP) is a mathematical framework that bounds how much any single individual's data influences the synthetic output, so individuals cannot be re-identified, regardless of an attacker's prior knowledge. DTS applies DP during generation to produce datasets that are statistically representative but contain no real personal information.
Yes. DTS is a full standalone enterprise engine and can be deployed independently. When used alongside Syntitan, DTS-generated datasets are versioned and bound to Release States for full execution traceability.
Three categories: restricted data that cannot be shared due to privacy or compliance rules; data with coverage gaps or class imbalance that make models unreliable; and non-accessible data that exists but cannot reach training pipelines.
Zero-access architecture means 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.
Syntitan performs data-quality refinement as part of execution stability. Syntitan can use a subset of DTS capabilities when privacy-safe synthetic data is needed, while DTS is a full standalone AI-ready data transformation engine.
Restricted data. Usable AI.
DTS turns restricted, unusable, and inaccessible enterprise data into privacy-safe synthetic datasets, without ever moving the original data. GS Certified. KISA approved.