Privacy and convenience have always been at odds, especially regarding digital onboarding or online sign-ups. For modern organizations, striking a balance between the two has become increasingly important. At the same time, a recent report said 53% of customers suggest that they would give up their interaction with a brand if it took longer than two minutes, while as many as 87% expect basic levels of privacy to be met.

Since the technological “birth” of Artificial Intelligence and ChatGPT, many people are wondering what on earth they would do without AI in their lives. Since July 2024 ChatGPT has had 200 million weekly active users worldwide and attracted nearly 2.5 billion site visitors. However, ChatGPT is not the only AI out there.

In today’s digital world, protecting sensitive data is more critical than ever. Organizations handle vast amounts оf information daily, much оf which includes sensitive data like Personally Identifiable Information (PII), financial details, and confidential business records. The exposure of this data can lead to severe consequences, including identity theft, financial loss, and reputational damage.

As Generative AI (GenAI) and Large Language Models (LLMs) become integral to modern apps, we face a critical challenge of protecting sensitive user data from inadvertent exposure. In this article, we’ll explore the importance of content filtering in LLM-powered apps, and provide strategies for its implementation. Looking for step-by-step tutorials on prompt sanitization for OpenAI, Langchain, Anthropic, and more? Skip down to the “Tutorials & further learning” section below.

Quebec’s Law 25, also known as Bill 64, imposes strict rules on how organizations handle personal information. With the final phase of implementation now in effect (September 2023), businesses need to ensure Law 25 compliance to avoid hefty fines and maintain customer trust. Here’s what you need to know, along with answers to frequently asked questions.

As Large Language Models (LLMs) continue to evolve and become integral to various applications, preserving data privacy remains a crucial concern. Below are fundamental principles and best practices for maintaining data privacy in LLMs.