Hello everyone!
Welcome.
In this video we will learn about procedures.
Architecture.
Presidio is engineered with a modular and extensible architecture, allowing it to be highly adaptable
to various environments and specific data protection needs.
It introduces two primary engines.
The very first one is the analyzer engine.
It scans text to identify PII entities using pattern matching NLP models and custom recognizers.
The second engine is the Anonymizer engine, which transfers or transforms detected PII using configurable
operators like masking, redaction, replacement, encryption or hashing based on your requirements.
It also provides recognizer library which which is an extensible collection of built in detectors for
common PII types.
And it offers flexible deployment strategies such as Tylenol.
Standalone services integrate into API gateways, and you can also embed it in the Python applications
or run as containerized microservice in Kubernetes.
So here I have a text as input.
Hi, my name is David and my number is this.
The request is first collected and sent to the PII analyzer using the API calls.
Right.
PII analyzer would extract the text features.
Here are the artifacts that it collects as an entity as David.
Tokens, lemmas and keywords as part of the input requests.
Then once the extraction has happened, it fetches all the recognizers.
In this case here it has credit card recognizer, crypto recognizer and other default recognizers that
comes as part of the the library.
Then it also runs the recognizer on the input text that was given.
It identifies this as a phone number.
It also identifies this as name.
Once they are recognized as the r r patterns, it aggregates the results by saying entity types.
The first one was person start and end.
This is the confidence score entity type as phone number start and end and the score that determines
the confidence score.
And then once they are determined, it actually anonymizes the entities using the anonymization techniques.
Where here it says person and the phone number.
And then finally it sends the results back using R API's streams or data storage.
Thank you.