Patricia Thaine

As AI adoption accelerates, privacy can no longer be an afterthought. This post explores the strategic importance of advanced PII detection—showing why regex, open-source, and general-purpose solutions fail, and how specialized AI like Private AI provides comprehensive coverage across PII, PHI, and PCI. Discover why privacy must be the foundation of both compliance and trust in AI-driven businesses.

Contact centers, chat logs, and email systems carry some of the most sensitive customer data, from credit cards to health details. This post explores the unique PII detection challenges across these communication channels, shows benchmark results comparing leading solutions, and explains why purpose-built AI is critical to reducing compliance risks and protecting customer trust.

Healthcare data contains some of the most sensitive and complex information—from patient names and family members to medical conditions and free-form clinical notes. This post explores why medical data is the ultimate test for PII detection systems, compares benchmark results across major providers, and shows how specialized AI like Private AI ensures accurate de-identification and HIPAA compliance.

General-purpose cloud tools miss a large share of sensitive data, leaving organizations exposed to compliance and security risks. In this post, we share benchmark results comparing Private AI’s purpose-built PII detection to AWS, Azure, Google DLP, and Microsoft Presidio—highlighting why specialization delivers far higher recall and greater protection against data breaches.

Organizations often choose PII detection vendors without a fair way to measure accuracy, leading to costly compliance and security risks. This post outlines Private AI’s research-based benchmarking methodology—covering datasets, precision/recall metrics, and real-world testing—so you can make informed decisions about the tools that protect your sensitive data.

Traditional methods like regex fall short when it comes to detecting hidden PII in real-world contexts such as call transcripts and unstructured data. This post explores the risks of missed PII, why open-source solutions struggle, and how AI-driven approaches provide the accuracy and scalability needed to protect sensitive information and maintain trust.

I’m thrilled to announce Private AI 4.0. This release redefines how healthcare businesses harness their unstructured data while maintaining fine-grained privacy controls and the highest standards of compliance. With Private AI 4.0, we are turning challenges into opportunities by enabling analytics confidently and securely to improve patient outcomes, accelerate research, and drive operational efficiency.

In the fast-evolving world of AI, where data is at the core of decision-making, one challenge remains constant: ensuring appropriate privacy and protection of an individual’s and an organization’s data. At Private AI, we’ve been working on solving this challenge with the introduction of Coreference Resolution as part of our 4.0alpha release.

Discussing developer responsibility, Bill C-11, positive consent, and the importance of Privacy by Design

Understanding key tech for data protection regulation compliance

We cover the basics of homomorphic encryption, followed by a brief overview of open source HE libraries and a tutorial on how to use one of those libraries (namely, PALISADE).

A number of people ask us why we should bother creating NLP tools that preserve privacy. Apparently not everyone spends hours thinking about data breaches and privacy infringements.

A very brief overview of privacy-preserving technologies follows for anyone who’s interested in starting out in this area. I cover symmetric encryption, asymmetric encryption, homomorphic encryption, differential privacy, and secure multi-party computation.