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Ontario’s Bill 194, formally known as the Strengthening Cyber Security and Building Trust in the Public Sector Act, 2024, represents a crucial legislative shift, aiming to fortify digital security and elevate trust within public sector entities.

In the rapidly evolving landscape of generative AI, distinct regulatory and ethical approaches have emerged, reflecting the values, ambitions, and constraints of various global players.

On May 3, 2024, the OECD released updated AI Principles that build upon the 2019 version with some notable differences.

Under the General Data Protection Regulation (GDPR), consent is only one of the legal bases that can be used to process personal data, including when using Large Language Models (LLMs) to boost the efficiency of processing personal information.

The healthcare sector has witnessed a remarkable evolution in data privacy legislation from the 1970s to the present, mirroring the technological innovations of the time.

Being able to manage the data and information within a company’s infrastructure is critical for properly assessing when sensitive information is either being mismanaged or to report an “all clear” when company policies are being followed as intended.

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.