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The healthcare sector in the United States has seen a profound transformation in its approach to data privacy, paralleling significant technological advancements, in particular the electronic health record (EHR).

There are lots of risks involved with implementing AI systems into an organization’s environment, some of which are not even known or knowable at this time.

Twilio Segment is a great centralized platform for storing customer data. However, it’s unlikely Segment will be your only application that needs the data.

The WHO’s publication of its Ethics and governance of artificial intelligence for health: Guidance on large multi-modal models from January 18, 2024 is an almost 100 page document, including over 20 pages of footnotes, which takes a deep dive into the risks and challenges of Large Multi-Modal Models (LMMs) developed, provided, and/or deployed for health care purposes.

Many of the security measures businesses put in place to protect the personal information of their customers and employees will also help safeguard confidential corporate information.

RAG is a popular approach that improves the accuracy of LLMs by utilizing a knowledge base. In this blog post, we illustrate how to implement RAG without compromising the privacy of your data.

In recent years, the emergence of artificial intelligence (AI) and machine learning technologies has created new possibilities for various fields, including the legal sector.

In recent years, the advent of artificial intelligence (AI) and machine learning technologies has revolutionized a range of industries, not least of which is Human Resources (HR).

In recent years, the emergence of artificial intelligence (AI) and machine learning technologies has opened up a wealth of opportunities across various sectors, including the banking industry.

The landscape of data protection and privacy has undergone significant transformations worldwide, with Quebec’s Law25 emerging as a pivotal legislative piece within Canada.

While the recent Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence advocates for a federal privacy law to be enacted, at this time it is still the states leading the way.

In recent years, the global landscape of data privacy regulations has evolved significantly, with countries enacting stringent laws to protect individuals’ personal information.

Tokenization is an increasingly popular method used in data security, especially in areas that require the handling of sensitive data like financial transactions. But what exactly is tokenization, and how does it bolster data protection?

The European Union’s General Data Protection Regulation (GDPR) regulates what kind of processing is permitted that involves “special categories of data,” which includes race, origin, religious belief, and health data, among others.

Private AI, a leading provider of data privacy solutions, has introduced a Cloud API for its cutting-edge PII detection, redaction, and data minimization solution. This innovative tool is set to transform how businesses safeguard and manage sensitive customer data.

End-to-end privacy management refers to the process of protecting sensitive data throughout its entire lifecycle, from the moment it is collected to the point where it is destroyed.

The evolving landscape of data protection in Canada sees provinces enacting or refining their respective privacy legislations to address the challenges of the digital age.

At Private AI, we pride ourselves on making scalable solutions for large companies, small businesses, and government institutions that need to securely handle the potentially sensitive data and information that is flowing through their infrastructure.