AI assisted data classification

Built for security with 200+ predefined data classes designed for unstructured, semi-structured, and structured data, tested with Mockingbird and assisted by AI, with a custom data class editor and customizable scans that harness serverless functions to deliver speed and cost-effectiveness.
Finding developer secrets and financial data in AWS and GCP.
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Full sampling at scale

Relying on sampling techniques for classifying unstructured data means potentially leaving hidden sensitive data exposed to cyber threats. With Open Raven, you can fully sample large volumes of unstructured data without sacrificing accuracy or speed. Accuracy is achieved through a combination of machine learning, pattern matching, extensive testing with Mockingbird and optional API-based validation.
Open Raven sampling set to 100% with files piling behind a storage bucket
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Advanced data classes

More than 200 data classes for everything from regulated data to developer secrets, metadata-specific dataclasses, and composite data classes that automatically identify toxic data combinations.
Composite data classes allow for custom combinations like Hospital PII = HICN, Age or PII = Social Security Number, Last Name, Billing Address
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Create custom data classes using a combination of regular expressions, keywords, and keyword adjacency settings. Easily customize data scans to meet time, cost, breadth, and completeness goals, and more.
Create custom data class panel. User is creating an 'Acme Secrets' with custom Regex, keywords, file types, and classification options.

"The initial implementation was fast and effective. We quickly got our data scanned at scale."

— Gary Miller, DVP Information Security, TaskUs