Human interaction guides AI for more accurate data classification to increase data value and reduce data risk
BigID, the leading data intelligence platform that enables organizations to know their enterprise data and take action for Data Security, Privacy, Compliance, and Governance, today introduced classifier tuning. BigID’s classifier tuning allows human interaction to adjust ML models in real time, without coding, for improved accuracy in data classification.
Organizations have a vast amount of data in different data sources that may be stored in cloud, local, and in hybrid environments. In addition, data may be in any structured or unstructured format. Organizations need to know what data they have across all of these varied sources and types of data for security, privacy, compliance, and governance initiatives – and they need to know what data needs to be protected and what data is good to use.
Manual classification is not scalable for a complete view of all data, so organizations depend on BigID for auto-classification. While other vendors repeatedly suggest incorrect classifiers or domains, BigID’s patented ML auto-classification was already more accurate, and now has a solution to set an even higher standard by introducing human interaction to tweak classification models with no coding needed for more precise results.
With BigID’s classifier tuning, organizations can:
- Increase trust in data for privacy, security, and governance initiatives
- Adjust AI models to scale work across the data environment
- Deliver highly accurate results with speed for business advantage
“Classifier tuning allows BigID customers to have more accurate classification with the combination of human intelligence and artificial intelligence,” said Maor Pichadze, Senior Product Manager, BigID. “Data owners can stop wasting time manually correcting inaccurate classification again and again from other systems.”
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