Blog

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.

We’ve talked a lot about how technology is transforming healthcare. From ambient listening devices to voice-based assistants, we’re seeing a data explosion. This shift (what many call a “data revolution”) is especially visible in the rise of AI scribes: tools that automatically generate clinical notes from doctor-patient conversations.The promise is big: faster diagnoses, more personalized care, and a lighter admin load for clinicians. But there’s a catch: most of this data never gets used.

We discussed how new technology is transforming healthcare: As the volume of electronic data continues to increase, many sectors refer to this phenomenon as a data revolution. And though this revolution promises faster diagnoses and personalized care, it comes with a catch: most of that data is never even used.Welcome to healthcare’s quiet crisis: the abandonment of unstructured data.

The way healthcare organizations collect and use critical data is changing, and changing fast. From smartwatches to AI-powered documentation, new technologies are transforming how patient information flows, creating new opportunities for continuous health monitoring, early intervention, and improved clinical outcomes. But with so many tools entering the space, it can be hard to keep up.

Clinical notes. Imaging reports. Lab results. Transcripts from patient conversations. It’s all full of critical insights, but none of it fits neatly into rows and columns – it’s all unstructured data.

We’ve said it before, and we’ll say it again: healthcare data has the power to transform care. It can personalize treatments and speed up diagnoses in ways we’ve only dreamed of.But here’s the part nobody really likes to talk about: most healthcare data strategies fail before they even get off the ground.

Toronto, Canada – [February 20, 2025] – Private AI, a leader in privacy-preserving artificial intelligence, is proud to announce its integration with NVIDIA NeMo Guardrails to bring advanced privacy and compliance capabilities to enterprises leveraging large language models (LLMs), enabling them to unlock the potential of generative AI while safeguarding sensitive data.

The European Data Protection Board (EDPB) recently released its comprehensive Guidelines 01/2025 on Pseudonymisation, a document rich with practical insights into the application of pseudonymisation under the General Data Protection Regulation (GDPR).

On December 27, 2024, the U.S. Department of Health and Human Services (HHS), through its Office for Civil Rights (OCR), issued a proposed rule to enhance the cybersecurity measures required under the HIPAA Security Rule. This Notice of Proposed Rulemaking (NPRM) seeks to bolster the defenses of the U.S. healthcare system against the rising tide of cyberattacks, particularly those targeting electronic protected health information (ePHI). The changes aim to address critical weaknesses, clarify obligations, and align the Security Rule with modern cybersecurity practices.

Anonymized and pseudonymized medical data are at the heart of cutting-edge research and innovation in healthcare. By stripping away personal identifiers and adding additional privacy-preserving measures, these data allow for advanced studies without compromising the privacy of individuals.In Japan, as elsewhere, the path to leveraging this valuable resource has been complex due to the need to balance large-scale data use with privacy protection. Thus, under the Act on the Protection of Personal Information (APPI) healthcare providers have faced challenges in sharing and processing medical data for research and innovation purposes, primarily due to strict consent requirements.

As the world gears up for the AI Action Summit in Paris in February 2025, global policymakers, researchers, and industry leaders are turning their attention to a landmark publication: The International AI Safety Report 2025. This report, a collaborative effort by 96 AI experts from around the world, represents the most comprehensive scientific assessment to date of the risks posed by general-purpose AI—a rapidly advancing form of AI with the ability to perform a wide range of tasks.

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.

The European Data Protection Board (EDPB) has recently provided critical guidance on ensuring GDPR compliance during the development and deployment of AI models.