Hello everyone.
In this particular section we will be learning about Microsoft Presidio for secure and responsible AI.
You will learn why protecting personally identifiable information, also known as PII, is critical
when deploying large language models and also understand how Presidio detects and anonymizes sensitive
data and gain practical skills to secure your AI pipelines against data leakage and compliance violations.
Having said that, let's first understand what is Microsoft Presidio?
Microsoft Presidio is an open source framework designed to identify, assess, and anonymize personally
identifiable information across various data types.
Presidios acts as a privacy guardrail, and it sits between users and AI systems, ensuring that sensitive
data never reaches LM APIs.
Training pipelines or system logs.
Some of its key features are that it has 50 plus PII recognizers for different entity types.
It has NLP powered detection for customizable roles, flexible anonymization strategies, language agnostic
architecture, and it's easy to integrate with the existing pipelines.
So why is Presidio needed?
Presidio helps in detecting protections.
So why is Presidio needed?
From the main challenges of PII is unstructured data.
LM processes vast amount of free form text, making PII identification and protection exceptionally
difficult without specialized tools.
There are regulatory compliances like GDPR and CcpA, and there are severe penalties for non-compliance
which can include hefty fines and legal actions.
And then privacy by design is part of the architecture that should be maintained for modern AI applications,
where privacy controls should be the heart of the application.
Procedure enables proactive data protection rather than being reactive for each responses.
So what does Presidio solve?
Presidio directly addresses many of the pressing data challenges that emerges when integrating AI systems
into enterprise workflows.
The very first one is the PII detection in the free form text procedure excels at identifying PII in
unstructured data from various sources.
It also prevents data exposure.
It ensures sensitive data is anonymized before it reaches downstream systems like third party API's,
logs, or less secure environments.
It standardizes the protection, provides a consistent framework for protecting PII across diverse data
sets, and it secures AI workflows, which helps enables the safe use of Llms for sensitive tasks by
ensuring that PII is never processed, stored, or revealed by the models.
Presidio is directly connected to the OWASp for those who are deeply concerned with security, procedure
offers reassurance by directly addressing critical vulnerability identified by leading industry standards,
particularly the OWASp top ten.
It aligns with the security standards, preventing sensitive data disclosures and implementing proper
security controls.
OWASp top ten focuses on sensitive information disclosure.
One of the most critical risk in the AI application by sanitizing inputs and outputs procedure mitigates
the risk of models revealing PII through completions or training data leakage.
Now that we learned about Presidio and what it means for LM and AI systems, let's go ahead and understand
its architecture.