GenAI has revolutionized the way businesses interact with data. Thanks to easy accessibility and automation capabilities, it is increasingly becoming a part of more business workflows.

What are false positives and negatives in AI privacy tools?
AI based PII detection tools use machine learning modules to identify sensitive data like PII and PHI.
False positives explained: how it affects PII tools
Overblocking, or incorrectly identifying non PII data as PII results in operational friction. It can block LLMs from accessing harmless content - ultimately impacting the quality of output.
How can PII tools minimize detection of false positives and negatives?
Practically speaking, no tool, no matter how advanced and well trained can identify PII with 100 percent success. After all, it is a machine that depends on context and training to provide the output.
1. Use contextual NLU
2. Confidence scoring and threshold tuning
AI models assign confidence scores to each detection. You can tune thresholds based on your use case:
* High threshold = fewer false positives
* Low threshold = fewer false negatives
The idea is to strike the right balance between the high and low spectrum based on your unique use cases like risk sensitivity, compliance requirements, breach history, and more.
3. Custom entity training
4. Structured and unstructured data handling.

How Protecto's DeepSight uses multi-layered detection to reduce incorrect identification
1. Smart, format-agnostic scanning
2. Seamless integration with minimal effort
3. Customizable sensitivity settings.

Versatile scanning modes with comprehensive logging
DeepSight supports both batch and real-time (streaming) scanning modes:
* Batch mode: Ideal for processing large datasets, such as scanning folders of customer emails for sensitive information.