Denver, Colorado, is home to beautiful mountain views and an airport with an interesting Wikipedia page. You might know it as home to multiple sports teams or as the Mile High City (1.34 KM High City) due to its famous elevation. Or you might know it as the home of the Great American Beer Festival.

In the busy domain of artificial intelligence (AI), the emergence of Private Language Model Models (LLMs) marks a pivotal development in addressing the escalating concerns surrounding data protection. As organizations harness the power of AI to glean insights and streamline operations, the need for safeguarding sensitive information has become more pronounced than ever.

The updated NIST Cybersecurity Framework (CSF) 2.0 was published February 26, 2024. Previously, this content was also known as the “Framework for Improving Critical Infrastructure Cybersecurity.” As stated in the framework: In summary, the updated NIST Cybersecurity Framework is organized into the following functional categories.

Artificial Intelligence (AI) being used to carry out cybercrime isn’t new, but as AI becomes more advanced, so do the tools that cybercriminals are using. One of the most recent AI-enabled cyber threats we’ve seen is cybercriminals using voice-cloning technology to carry out scam calls. You can protect yourself from AI voice-cloning scam calls by blocking spam calls, not picking up calls from unknown numbers and creating a safe word with your family in case a legitimate emergency occurs.

How can we clear our minds of preconceived notions about Artificial Intelligence? How can we approach this ongoing technological revolution without apprehension? AI has integrated into our lives more swiftly than a lawyer’s “Objection, Your Honor!” in a surprise courtroom confession. We ask AI to do a multitude of things: write email responses, research our homework, inquire about recipes, complain about our tough days, compose school papers, and create art.

Technology can change in the blink of an eye, and nowhere is this more evident than in the rise of “citizen developers.” Often without formal technical training, these individuals leverage user-friendly platforms to create, innovate, and deploy AI-driven solutions. But with the support of intuitive interfaces, templates, and code snippets come challenges. Security can be a challenge hidden in the simplicity of drag-and-drop designs.

As the adoption of generative AI (GenAI) accelerates across enterprises, one of the most promising applications emerges in customer support. GenAI enables automated responses, allowing businesses to engage in natural conversations with customers and provide real-time chat support. However, this convenience comes with inherent risks, particularly concerning data privacy.

The modern enterprise relies on hundreds of SaaS apps, email services, generative AI (GenAI) tools, custom apps, and LLMs, which often contain sensitive data. For too long, security teams have been forced to patch together point solutions for coverage across these channels, increasing their workloads and creating opportunities for sensitive data to slip through the cracks. This is precisely where Nightfall’s single-pane-of-glass solution comes into play: With Nightfall Sensitive Data Protection.

Since the discovery of CRIME, BREACH, TIME, LUCKY-13 etc., length-based side-channel attacks have been considered practical. Even though packets were encrypted, attackers were able to infer information about the underlying plaintext by analyzing metadata like the packet length or timing information. Cloudflare was recently contacted by a group of researchers at Ben Gurion University who wrote a paper titled “What Was Your Prompt?