Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

As developers continue to adopt AI tools to transform their workflows, AI-generated code has become more common. In fact, 96% of developers reported using AI coding assistants to streamline their work. Although generative AI (GenAI) tools like ChatGPT can speed up workflows and boost productivity, the security and quality of the outputs aren’t guaranteed.

For years, IT teams have been stuck in a reactive mode, scrambling to fix network performance issues only after users start complaining. Despite an abundance of monitoring tools, the real challenge has always been identifying and resolving issues before they impact productivity—without spending countless hours on manual troubleshooting.

In line with our philosophy of delivering an exceptional customer experience, Cato Networks has added a knowledge-base AI assistant as part of the Cato SASE Cloud Platform. The AI assistant provides accurate, relevant answers to questions about using Cato’s many capabilities with detailed, step-by-step instructions uniquely suited to the user’s situation and circumstance.

Enterprises are racing to integrate AI into applications, yet transitioning from prototype to production remains challenging. Managing ML models efficiently while ensuring security and governance is a critical challenge. JFrog’s integration with NVIDIA NIM addresses these issues by applying enterprise-grade DevSecOps practices to AI development. Before exploring this solution further, let’s examine the core MLOps challenges it solves.

APIs facilitate communication between different software applications and power a wide range of everyday digital experiences, from weather apps to streaming services and everything in between. They are also a critical ingredient of AI. However, if not structured and standardized properly, APIs can become inconsistent, insecure, and difficult to maintain. This is where API specifications come into play.

For Star Trek fans, space may be the final frontier, but in security, discovering Application Programming Interfaces (APIs) could be the technology equivalent. In the iconic episode “The Trouble with Tribbles,” the legendary starship Enterprise discovers a space station that becomes overwhelmed by little fluffy, purring, rapidly reproducing creatures called “tribbles.” In a modern IT department, APIs can be viewed as the digital tribble overwhelming security teams.

Generative AI (GenAI) is transforming the cybersecurity landscape, requiring Chief Information Security Officers (CISOs) and their teams to adapt quickly to both opportunities and challenges, according to the Gartner report 4 Ways Generative AI Will Impact CISOs and Their Teams. As organizations integrate GenAI into business processes, it is critical to secure not only the technology’s development but also its consumption across the enterprise.