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.
If something sounds too good to be true, there’s usually a catch. GenAI works by continuously processing and improving on the data fed into it – often sensitive data, making privacy a tradeoff.
Tools like Gemini, Claude, and ChatGPT are becoming the most common shadow IT tools. This raises several consumer privacy and security concerns, pressuring businesses to implement a safety wall to protect consumer confidentiality.
The solution, however, is not as simple as adding another tool to the IT ecosystem. The privacy wall has two major roadblocks – false positives and false negatives.
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. These are trained to identify sensitive words and numbers from large volumes by understanding the context of the sentence.
Given that AI tools depend on contextual understanding and training, it is prone to error. In case the input contains edge cases, it may escape the detection radar, resulting in false positives and negatives.
False positives explained: how it affects PII tools
False positives occur when AI privacy detection tools incorrectly identify non sensitive data as sensitive. For example, the tool may tag “John Doe” in a public press release as PII, even though it’s not contextually sensitive or private.
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.
False positives impact the analytical capabilities and training quality of the dataset. For example, if the original input was “John Doe from California bought a Tesla”, and Tesla is falsely marked as PII, it becomes “<REDACTED> from <REDACTED> bought a <REDACTED>”. Running sentiment analysis recommendation models and output like this significantly deteriorates the correctness of the output.
Moreover, machine learning models depend on statistical pattern and reputation to continuously improve the quality of output. Redacting non PII data with tokens or inconsistent formats can impede its ability to recognise patterns correctly.
For compliance teams, the biggest drawback of false positives is too many false alerts and notifications that interrupt their productivity. Compliance noise leads to inflated risk reports, which results in alert fatigue and loss of trust in the system.
False negatives explained: its impact on security and compliance
A false negative occurs if the AI privacy detection tool fails to identify and flag data that is actually sensitive and should have been redacted.
For example, if the data reads “Call me at 555 900 1234”, AI may fail to flag the phone number as PII. This may happen as it is not formatted irregularly and does not include the context such as “number” or “phone” which is generally included in a data set containing phone number details.
False negatives create a number of privacy challenges for businesses using privacy tools to avoid security gaps and manage compliance. If your tool fails to identify a certain set of sensitive data, it may result in serious security incidents if unauthorized individuals or malicious actors gain access to the data. Security breaches consume an IT team’s bandwidth, slow down critical operations, and result in financial losses.
In addition, if your business processes data that fall within the scope of regulated sectors like healthcare, defence, or banking, missed detections are compliance risks. If you violate regulations like HIPAA, DPDP, or GDPR, you are looking at hefty penalties and reputation damage.
From a technical perspective, false negatives generally stem from poor generalization of models trained on clean synthetic data. The second most common cause is inability to detect data that has been obfuscated or malformed.
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.
Having clarified that, it is possible to minimize the number of cases of false identification. AI tools can reduce false positives and false negatives in PII detection by shifting from brittle pattern-matching to context-aware, learning-based approaches. Here are some ways to improve the rate of correct identification:
Use contextual NLU
NLU (natural language understanding) is the technology AI uses to understand the context of a dataset, rather than just the individual words within the sentence or paragraph. This way, AI tools actually understand what we are trying to communicate.
For example, advanced AI tools are trained to distinguish between a question and a command. Similarly, it should understand that “I want to cancel my order” is not the same as “I want to avoid canceling my order”.
In short, AI tools should be trained to spot the difference in context between “Contact John at 555-1234” (phone number and therefore a PII) and “John scored 555 points” (not a PII and not sensitive).
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.
Custom entity training
Organizations often use proprietary identifiers like employee codes, health insurance numbers, account tags, and internal metadata. Generic models may miss these identifiers as non PII data (false negative) or misclassify similar strings (false positives).
Custom AI entity training helps to reduce the chances of either incorrect identification by conducting custom training using domain specific examples. In this method, you add your custom entities so that the AI module can identify custom PII data specific to your use case. Combine custom entity training with feedback loops – a process where users can flag incorrectly flagged items to allow the model to continuously learn and improve over time.
Structured and unstructured data handling
One of the reasons that leads to false negatives is the way data is fed into the system. For example, the tool may not be able to find PII in unstructured data (chat logs, notes, transcripts, documents, images, audio files). Modern tools use transformer-based models (like BERT, RoBERTa) to extract entities from unstructured, messy, free form content – not just clean tables.
How Protecto’s DeepSight uses multi-layered detection to reduce incorrect identification
As previously outlined, no tool can offer 100 percent accuracy in PII detection. Combining a number of advanced techniques can minimize occurrences of false identification.
Protecto (AI privacy guardrail tool) uses a technology “DeepSight” to detect PII with high accuracy. It applies all technologies we discussed above to deliver high-precision, low-latency sensitive data scanning in even the most complex enterprise data streams. Here’s how it works:
Smart, format-agnostic scanning
DeepSight swiftly analyzes vast amounts of data across various formats like PDFs, images, spreadsheets, and plain text without the need for manual configuration. Its intelligent system automatically adapts to the data type, ensuring consistent accuracy and reducing setup time.
Seamless integration with minimal effort
Designed for ease of use, DeepSight offers a plug-and-play integration approach. Developers can incorporate it into their systems within hours using a simple REST API. Users can receive detailed insights, including identified sensitive entities, confidence scores, contextual information, and recommended actions, just by sending data to the /scan endpoint.
Customizable sensitivity settings
DeepSight allows users to set custom sensitivity thresholds as different industries have varying privacy requirements. For instance, healthcare providers can opt for higher sensitivity to protect patient data, while businesses focusing on product analytics might choose lower sensitivity to minimize false positives.
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.
- Streaming mode: Provides real-time protection by analyzing data as it flows into your system, suitable for live chats, application traffic, or voice transcriptions.
In both modes, DeepSight maintains detailed logs of all detections, enabling security teams to audit and review scanned, flagged, and redacted data effectively.
Your chance to try DeepSight for free
Not sure if DeepSIght is right for your business use cases? Not ready to make a decision? No worries, you can get started with:
- Free scanning API access (limited tokens)
- Sample redaction/playground UI
- White-glove onboarding for enterprises
Talk to our data privacy experts to discuss your custom business requirements.
