We solved ML training on high-quality non-anonymized data for the US Department of Homeland Security (DHS).
The Challenge for DHS:
- Operational data is often sensitive and cannot be shared across departments due to privacy, security, and regulatory constraints.
- Traditional anonymization techniques fail to fully protect against re-identification risks, limiting the ability to conduct effective cybersecurity and infrastructure protection exercises.
Thus the Department of Homeland Security (DHS) required high-quality data to train ML models, test critical systems, and simulate real-world scenarios.
Our Solution - Large Tabular Model (LTM):
Through our foundational model, ‘Large Tabular Model (LTM)’, DHS can generate high-fidelity synthetic data that mirrors the statistical properties of real datasets, without carrying any sensitive or personally identifiable information (PII).
- Zero-shot and few-shot adaptability: Unlike rule-based or deep learning-based synthetic data generators that need retraining for each use case, LTM can adapt to new datasets with minimal input.
- Built-in privacy audits: Every synthetic dataset generated undergoes rigorous privacy assessments, ensuring compliance with DHS’s stringent security and privacy standards
The Impact:
Through the adoption of Betterdata’s LTM, DHS is achieving game-changing outcomes:
- Faster Cyber Defense Simulations: DHS can now simulate sophisticated cyber-attack scenarios using realistic yet risk-free datasets, accelerating training and strategic planning.
- Enhanced Anomaly Detection: ML models trained on synthetic data identify anomalies and threats more accurately, without ever accessing real user information.
- Secure Interagency Collaboration: Agencies can share synthetic datasets freely, breaking down data silos without risking policy violations.
- Regulatory Compliance: DHS remains fully aligned with national cybersecurity mandates while advancing its AI-driven threat intelligence programs.
Synthetic data has the potential to transform industries by enabling government agencies and enterprises to innovate without any restrictions. Because it allows enterprises and government agencies to work with accessible, fair, and scalable data. Something that was not possible in the not-so-distant past. With data protected and utility maintained (or even improved in some cases), innovation is not a question of how but when.
