Unlike traditional software, where testing is relatively straightforward, Gen AI apps introduce complexities that make testing far more intricate. This blog explores why traditional software testing methodologies fall short for Gen AI applications and highlights the unique challenges posed by these advanced technologies.

By James Rees, MD, Razorthorn Security Times must change (and always will) and nowhere is this more true than in the realm of technological advancement. Thirty years ago, the technological landscape was vastly different from what we have today and technological change has outpaced Moore’s Law for some time now. Information security must keep pace with these advancements. This has become especially true with the advent of AI.

Worried about GitHub Copilot’s security and privacy concerns? Learn about potential risks and best practices to protect yourself and your organization while leveraging AI.

After speaking to a wide spectrum of customers ranging from SMBs to enterprises, three things have become clear: Add that together, and we get Shadow AI. This refers to AI usage that is not known or visible to an organization’s IT and security teams. Shadow AI comes in many forms, but in this blog we’ll stick to a discussion of Shadow AI as it pertains to applications. Application security teams are well aware that AI models come with additional risk.

In the early days of cybersecurity, implementing a Security Information and Event Management (SIEM) system was akin to constructing a house from scratch. The SIEM was a blank slate, and transforming raw data into actionable insights was a long and arduous journey. It began with the daunting task of ingesting data from various disparate sources and formats. From there, security teams had to craft detections — rules designed to identify malicious or suspicious activity.

In this age of digital supremacy, keeping our data safe and respecting privacy are super important. As more and more people and businesses use online platforms, it’s crucial to understand what types of data need that extra layer of protection, especially when it comes to PII vs PHI vs PCI. Understanding the distinctions between PII (Personally Identifiable Information), PHI (Protected Health Information), and PCI (Payment Card Information) is crucial.