For most companies today, the question isn’t whether a data breach will occur, but rather when it will occur. This predicament is primarily due to the sheer volume of data, the challenges associated with monitoring sensitive data, and the transition to remote work. Consequently, IT security teams are constantly navigating a dynamic and enduring risk landscape, making it exceptionally challenging to maintain data security and implement effective sensitive data protection strategies.

Enterprise data is a tremendous asset, but did you know it could also cause great data privacy-related financial risks? The need for sturdy enterprise data protection cannot be emphasized enough. With local data privacy laws such as GDPR being strictly enforced by countries worldwide, companies are seeing heftier fines for data breaches. Companies now need to be extremely cautious about how they manage privacy risks by carefully controlling access to personal and sensitive data.

Since its debut in 2022, ChatGPT has radically reshaped the way we interact with technology. Generative AI (genAI) platforms like ChatGPT, Google Gemini, and Meta AI have rapidly gained in popularity, offering capabilities that range from rewriting text to generating creative content. While these tools have created new opportunities for enhanced productivity, they’ve also introduced new security risks — particularly when users unknowingly share sensitive information.

Large Language Models (LLMs) transform how organizations process and analyze vast amounts of data. However, with their increasing capabilities comes heightened concern about LLM security. The OWASP Top 10 for LLMs offers a guideline to address these risks. Originally designed to identify common vulnerabilities in web applications, OWASP has now extended its focus to AI-driven technologies. This is essential as LLMs are prone to unique LLM vulnerabilities that traditional security measures may overlook.