Critical capabilities for AI-generated synthetic test data
AI coding assistants have made the shift-left testing urgent. Synthetic data can help, but only if it captures far more than statistical realism.
Read the storyIdeas for a moreuseful data future.
Research, engineering perspectives, product thinking, and practical applications from the team building the Synthetic Data Vault.
SDV 2.0 : Generative relational models for enterprise relational data
SDV 2.0 turns any enterprise database into a Generative Relational Model, a reusable AI asset trained on your own proprietary data.
Enterprise AI should train where enterprise data lives
Should enterprises access AI through an API which involves sending those labs their proprietary data? Or should they access AI by installing open source models on-premises and further training them on enterprise-specific data?
Enterprise relational data is becoming a new
medium for generative AI.We cover the ideas, and systems shaping what comes next.
What must synthetic data,be able to do?
Explore seven critical capabilities that go beyond statistical realism, from business rules to edge cases.
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Put synthetic datato work.
View all applications
How ING Belgium Uses DataCebo’s SDV Enterprise to Create Synthetic Data for 100x the Test Coverage
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Boosting Fraud-Detection Accuracy with Synthetic Data
Using a model from the Synthetic Data Vault (SDV), a UCLA team has shown that credit card fraud-detection can be dramatically improved by generating synthetic case data consistent with past examples of fraud. They show that they can reduce the false negatives by a factor of 20x.
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Synthesizers are data diversification engines – embrace them!
Synthesizers can create diverse data that is also high quality. Check out how these two vital traits inform each other and drive great business outcomes.
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ML Model Development using Synthetic Data Clones
What happens when you train a machine learning model on synthetic data instead of real data? Let's experiment to find out.
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Curated series · 3 parts
Differential privacyfor synthetic data.
Go from the foundations of synthesizer disclosure to empirical verification and enterprise deployment.
Explore the seriesPart 1
Differential Privacy for Synthetic Data (Part I): Synthesizer Disclosure
Synthesizers are game-changers for data disclosure and differential privacy. Use them to create unlimited, differentially private synthetic data.
Part 2
Differential Privacy for Synthetic Data (Part II): Trust-but-Verify
You can trust that your software is applying differential privacy, but can you verify it for yourself? Use our framework to measure privacy for any synthesizer.
Part 3
7 signs a synthetic data software violates privacy
Are you evaluating synthetic data vendors? Look out for these signs that their software might be violating privacy.
From the teambuilding SDV.
All product storiesContextual Anonymization for GPS Data in SDV
GPS data is often PII but anonymizing GPS data while preserving useful context is difficult. Learn about the challenges and how we solved them in this post.
Meet the SDV Enterprise Multi-Table Synthesizers
To create multi-table synthetic data, it's important to learn the connections between tables. Explore 3 different approaches to the challenge.
Contextual Anonymization: The latest innovation for PII
Can you anonymize PII without sacrificing usability? Explore the latest techniques in anonymization.
Introducing AI Connectors: Database Integration for Synthetic Data
AI Connectors allow users to create robust, referentially sound synthetic data by connecting to an existing database and automatically creating highly accurate metadata, regardless of the underlying database technology.
How do you know synthetic data isuseful?
Explore the methods behind measuring statistical fidelity, downstream utility, and privacy.
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Latest stories
Critical capabilities for AI-generated synthetic test data
AI coding assistants have made the shift-left testing urgent. Synthetic data can help, but only if it captures far more than statistical realism.
SDV 2.0 : Generative relational models for enterprise relational data
SDV 2.0 turns any enterprise database into a Generative Relational Model, a reusable AI asset trained on your own proprietary data.
Enterprise AI should train where enterprise data lives
Should enterprises access AI through an API which involves sending those labs their proprietary data? Or should they access AI by installing open source models on-premises and further training them on enterprise-specific data?
How to generate synthetic survey responses for market research
Trained AI models do what LLMs cannot: Generate survey responses with the statistical variety and demographic accuracy your analysis depends on.
Why synthetic data is not the same as data masking with ETL
Synthetic data does what ETL pipelines cannot: Create unlimited test data with low infrastructure, storage, and system complexity.
ML Model Development using Synthetic Data Clones
What happens when you train a machine learning model on synthetic data instead of real data? Let's experiment to find out.
Why are comparisons to SDV Community misleading for enterprise evaluation?
Many vendors compare against SDV Community to signal enterprise readiness—but those comparisons often hide critical gaps. In real enterprise environments, these shortcuts break down. Here’s how to evaluate solutions the right way.
How DataCebo Supports Enterprises: Fast, Safe, and Effective
See how DataCebo enables enterprises to create generative AI models without needing to access their data. With fast debugging, seamless integration, and robust testing, it makes scalable adoption possible.
