Safe Sensitive Data Sharing with Gretel
Create highly representative datasets with mathematical guarantees of privacy for safe data sharing.
The data sharing challenge
Today, the vast majority of digital initiatives, from new application development and third party tool evaluation to the latest R&D are blocked by a single point of failure — lack of safe data access. These data bottlenecks, created by privacy and security concerns, stifle organizations ability to rapidly extract value from data, and prevent critical initiatives from ever taking off.As a result, data remains out of the hands of the teams that need it most and critical initiatives never take off or get stuck in innovation labs.
- Data leaks are a looming risk
Improper exposure of sensitive information is harmful for individuals and expensive for organizations. Traditional anonymization and de-identification methods have been shown to be reversible.
- Unshareable data slows innovation
Research and development thrives on open data sharing and collaboration. Locked-up insights limit the value of data and stagnate innovation.
- Data compliance drains time
Compliance with GDPR, CCPA, and other privacy regulations has high administrative costs. Time spent on compliance is time not spent on development.
Solutions
- New application development
Give developers access to safe and high-quality data to build in lower environments.
- Safe AI training
Train leading AI models with safe and high-quality synthetic training data.
- Third party tool evaluation
Evaluate third party tools with insights, not personally identifiable information.
- Secure research and analytics
Accelerate cross-team research, analytics, and innovation with safe data sharing.
- Privacy sandboxes
Empower data collaboration with secure cross-border sandboxes.
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