CREATE SAFE DATA.
POWER AI WITH CONFIDENCE.
WHAT YOU NEED
TO KNOW ABOUT Synthetic Data
What It Is
Synthetic data is artificially generated data that replicates the statistical properties and relationships of real-world data—without including any actual sensitive records.
When to Use It
Use synthetic data when real data is too sensitive, limited, regulated, or biased for safe and reliable use—particularly for AI/ML training, cross-border data sharing, testing in lower environments, and simulating rare events or edge cases that don’t appear in production datasets.
Why It Matters
By removing privacy and availability roadblocks that slow down innovation, synthetic data lets you train and test models at scale, simulate diverse scenarios, and ensure compliance with GDPR, HIPAA, and other regulations.
Why Our Synthetic
Data is Different
How Synthetic
Data Works
When Should You Use Synthetic Data?
Why Use Synthetic Data?
Synthetic data helps teams use realistic data for AI, analytics, development, and testing without exposing real sensitive records. Instead of moving production data into every workflow, organizations can generate privacy-safer datasets that preserve statistical patterns, relationships, and business logic while reducing reliance on regulated personal data.
Reduce Re-identification Riska
Create new records that reflect the structure and statistical behavior of real data without copying actual personal identifiers. Synthetic data helps reduce the risk that individuals can be linked back to source records during AI training, testing, analytics, or data sharing.
Expand Safe Data Availability
Generate realistic datasets when production data is limited, restricted, or difficult to access. Teams can create larger volumes of safe, representative data for model development, application testing, analytics validation, and scenario planning.
Accelerate AI and Machine Learning
Train, test, and evaluate AI/ML models with data that reflects real-world patterns without exposing sensitive information. Synthetic data supports faster experimentation, safer model iteration, and broader access to usable data for approved AI workflows.
Improve Dataset Balance
Create more representative datasets by simulating rare events, edge cases, missing populations, or underrepresented scenarios. This helps teams test model behavior against a wider range of conditions and reduce dependence on incomplete production data.
Support Secure Data Sharing
Share privacy-safer datasets with internal teams, partners, developers, or external environments without exposing raw personal data. Synthetic data can support cross-border collaboration, vendor testing, lower-environment development, and regulated analytics use cases.
Lower Data Access and Preparation Costs
Reduce the operational burden of sourcing, masking, approving, and moving real production data into every downstream workflow. Synthetic data gives teams a repeatable way to create useful datasets for testing, analytics, AI development, and compliance-sensitive innovation.
Beyond Synthetic Data: COMPREHENSIVE AI PROTECTION
Text To Analytics
Semantic Guardrails
Synthetic Data Generation
Find & Protect
Explore Data-Centric Data Protection
Discovery
Identify sensitive data (PII, PHI, PCI, IP) across structured and unstructured sources using ML and rule-based classification.
Learn MoreGovernance
Define and manage access and protection policies based on role, region, or data type—centrally enforced and audited across systems.
Learn MoreProtection
Apply field-level protection methods—like tokenization, encryption, or masking—through enforcement points such as native integrations, proxies, or SDKs.
Learn MorePrivacy
Support analytics and AI by removing or transforming identifiers using anonymization, pseudonymization, or synthetic data generation—balancing privacy with utility.
Learn More