Thanks to a wide range of use cases that automate manual activities, enterprises are rushing to integrate GenAI into their IT stack, only to realize they’ve hit a privacy wall. A concerning number of use cases involve the use of sensitive data like PII and PHI, putting data privacy and compliance at risk.
Enterprises today are becoming increasingly aware of these multifaceted risks associated with unfiltered AI usage and turning to the common solution available in the market – AI privacy tools.
Challenges traditional AI privacy platforms face in identifying PII
A number of AI privacy tools have infiltrated the market to cater to these growing security needs. While these tools do a fairly decent job of identifying and blocking AI from accessing sensitive information, they fall short when it comes to identifying data in complex edge cases.
A common issue with privacy tools is incorrect data processing due to lack of context – resulting in false positives and negatives.
- False positives occur when the tool wrongly flags non sensitive data as sensitive, resulting in unnecessary restrictions. This compromises output quality.
- False negatives are the failure to detect actual sensitive information, resulting in unintentional data exposure risks compliance.
What is Deepsight by Protecto?
Built by Protecto, DeepSight is an AI-native sensitive data identification engine designed specifically for today’s unstructured, high-volume, context-rich environments. It plugs directly into your data pipeline, understands real world use cases, and identifies PII, PHI, and proprietary data without breaking workflows.
How Protecto’s Deepsight reduces erroneous PII identification
Protecto combines three machine learning modules to maintain a high percentage of accuracy for PII identification across structured and unstructured data. These modules have been trained with large data sets to correctly understand patterns, context, and semantics. Moreover, these modules are continuously undergoing training with actual use case inputs to continuously improve output accuracy.
Here’s a detailed breakdown on the technology behind DeepSight:
Context aware semantic scanning
Compared to most AI privacy tools that scan for strings, DeepSight is trained to scan for meaning. Its transformer-based models look beyond keyword patterns and analyze sentence context to determine whether something is actually sensitive.
For example,
- “John Doe lives in 1234 Elm St” is flagged as high-risk PII.
- “John Doe won the award in 1999” is not flagged as sensitive or high risk PII.
This context awareness massively cuts false positives.
Handling unstructured and obfuscated data
Sensitive data is usually presented in two formats; structured and unstructured. In most real life cases, data looks unstructured, like these:
- “9876-XXXX-4321” (masked credit card)
- “rahul(dot)mehta(at)email(dot)com” (obfuscated email)
- “Customer id: 123ABC!@#” (proprietary ID)
Since DeepSight’s models are trained on wide range of data – including poorly formatted, unstructured data, they can recognize it with ease to parse and tokenize.
Faster custom entity training
Each business uses a specific set of sensitive data. It could include a combination of SPI (Sensitive Personal Information) codes, social security numbers, driver’s license numbers, and another sensitive number unique to the business. Unless your AI privacy tool allows you to set a custom value, the unique sensitive entity becomes a privacy risk.
DeepSight’s model fine-tuning pipeline allows users to define custom entities. You can create and ready custom detection models in days by uploading examples, labeling them with tags, validating the output, and finally shipping them. This eliminates the need for machine learning operations or spending months to manually label your data.
How DeepSight enables developers and businesses build smarter
DeepSight equipes developers with the tools and technologies to seamlessly integrate robust privacy controls into their stack.
Precision scanning
Quickly scan large volumes of data with high accuracy and without the need to manually spend hours setting up. It works with equal precision and accuracy irrespective of the type of file; PDF, JPG, Excel file, or plain text. DeepSight automatically and accurately adjusts to the type of data fed into it, so your team does not have to configure the settings.
Faster, seamless integration
DeepSight’s plug and play approach eliminates the need for coding, enabling developers to quickly integrate in hours, instead of days. Users simply need to insert the input and the system replies with the list and type of sensitive information. It uses REST API to simplify the response:
POST /scan
{
“text”: “Hey, this is my PAN card: ABCDE1234F”,
“entities”: [“PII”, “PAN”, “EMAIL”]
}
The response includes entity type, confidence score, context window, and recommended redaction or token.
Configure custom sensitivity thresholds
DeepSight allows users to set a custom threshold for identifying sensitive data as per their business requirements and compliance obligations.
For example, if your business processes medical records of patients, you can set the detection threshold to a higher level of caution. In contrast, if you are working on product insights which generally does not include highly sensitive information, you can dial down the threshold to a low caution to avoid false alarms.
Data scanning mode compatibility
DeepSight works by protecting data in two ways:
- Batch mode: Scans large volumes of data or logs before they are used. For example, you can scan a folder containing customer emails for sensitive data.
- Streaming mode: Here, you scan and protect data in real time as it flows into your system. Examples include sensitive information in a live chat, traffic from application, or voice transcriptions.
No matter the mode, DeepSight logs every detection, letting your security team audit what’s been scanned, flagged, and redacted.
Architecture & Tech Stack Integration
- Language Support: Works across English and multiple international languages.
- Deployment Options: Cloud API (default), On-prem (for regulated industries), VPC deployment (for data residency)
- Integrations: Apache Spark / Databricks, Kafka / Confluent, REST APIs, S3, GCS, Azure Blob, Snowflake / BigQuery connectors
- Security: Tokenized data never leaves your system (if you choose on-prem mode). Supports audit logging, role-based access, and end-to-end encryption.
Try DeepSight for free
Not sure if Protecto is the right tool for your business? No worries, you can get started with:
- Free scanning API access (limited tokens)
- Sample redaction/playground UI
- White-glove onboarding for enterprises
DeepSight sees what other tools can’t – that is the difference between an AI win and a compliance failure. Talk to our data privacy experts to discuss your needs.
