Scan Enormous Databases to Identify Where PII Resides and Mitigate Risk

The Problem:

Discovering Personally Identifiable Information Within Large Quantities of Diverse Data

For any large organization, it’s incredibly difficult to pinpoint where PII resides across the various data silos, data lakes, and other data stores that inevitably exist. Not to mention the nightmare this causes when it comes to maintaining a security and compliance program.

Enter Private AI:

Quickly and Accurately Discover and Classify PII Within Massive Datastores

Private AI has been deployed by a number of Fortune 500 companies to scan enormous databases, identify where PII resides within their data infrastructure.

We do this with far higher accuracy than DLP solutions, which typically use regexes instead of AI. Plus, given how quickly Private AI can process text, image, audio, and video data, getting the job done won’t break your cloud-processing budget.

Why Private AI


We identify 50+ types of PII, PCI, and PHI across 47 languages, even if the language changes from one to another within a single document.


We deploy via a container directly into our clients systems, so data never leaves their environment and is never shared with an external third party (including Private AI).


Private AI can process 70,000 words per second using our GPU-optimized models, or 2,000 words per second on a single core with our CPU-optimized models.

Customizable Outputs

Output formats match the original, or can be easily transformed to meet the requirements of data lake creation and modernization efforts.

Unrivalled Accuracy

Private AI uses the latest advancements in machine learning to achieve remarkable accuracy out of the box. See how we stack up against our competitors in our technical whitepaper

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Ready to get started? Talk to one of our privacy experts today:

The docker image was really easy to integrate into our data workflows and we had it up and running in just a few hours. Our data involves mental health chat transcripts, so we were very happy to see that we were hitting impressive accuracy numbers out-of-the-box on a wide range of entity types that matter to our customers, saving us an enormous amount of time compared to building it ourselves.

Quinn Underwood
CEO, Autumn AI


Tested on a dataset composed of messy conversational data containing sensitive health information. Download our whitepaper for further details, as well as how we perform on precision and F1-score or contact us to get a copy of the evaluation code.


Testé sur un ensemble de données composé de données conversationnelles désordonnées contenant des informations de santé sensibles. Téléchargez notre livre blanc pour plus de détails, ainsi que nos performances en termes d’exactitude et de score F1, ou contactez-nous pour obtenir une copie du code d’évaluation.

99.5%+ Accuracy

Number quoted is the number of PII words missed as a fraction of total number of words. Computed on a 268 thousand word internal test dataset, comprising data from over 50 different sources, including web scrapes, emails and ASR transcripts.

Please contact us for a copy of the code used to compute these metrics, try it yourself here, or download our whitepaper.