Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

In this year-end Trust Issues podcast episode, host David Puner takes listeners on a retrospective jaunt through some of the show’s 2023 highlights. The episode features insightful snippets from various cybersecurity experts and thought leaders, each discussing crucial aspects of the ever-evolving cyber landscape.

As I look at the range of API Management approaches that are recommended by various analysts, pundits, and vendors, I find it interesting that most don’t really know how to address “security” in the context of API management. In high-level API lifecycle management diagrams securing APIs is rarely called out visually, although it may be addressed briefly in an accompanying paragraph.

Today’s security practitioners face a daunting challenge: Staying ahead of sophisticated adversaries who have turned their attention to the expansive terrain of cloud environments. CrowdStrike observed a 95% year-over-year increase in cloud exploitation in 2022. This trend demands strategic reevaluation of how organizations protect their cloud environments and workloads, and emphasizes the importance of choosing the right cloud security capabilities.

In the intricate world of artificial intelligence, it's essential to distinguish between the different AI technologies at our disposal. Two key domains that often lead to confusion are conversational AI and generative AI. Though their names might sound related, they are fundamentally different in their applications and underlying mechanisms. Let's dive into the realm of AI to elucidate the distinctions between these two intriguing domains.

Trustwave SpiderLabs tested a couple of Android OS-based mobile devices to conduct the research on privilege escalation scenarios. Specifically, we wanted to show a straightforward process attackers may use to exploit vulnerabilities in an Android device’s system services and systems. The testing revealed that, in some cases, exploiting the issues we found were very easy.

Many applications rely on user data to deliver useful features. For instance, browser telemetry can identify network errors or buggy websites by collecting and aggregating data from individuals. However, browsing history can be sensitive, and sharing this information opens the door to privacy risks. Interestingly, these applications are often not interested in individual data points (e.g.

How many of us use ChatGPT? And how many of us use SaaS applications as part of our daily workflows? Whether you know it or not, if you use either of these tools, your data has likely traveled beyond the boundaries of your “fort.” What do I mean by “fort,” exactly? For this guide, consider your “fort” to be somewhere where you can monitor and secure your data. When data leaks outside your “fort,” it presents a myriad of possible risks.

So you want to use AI-generated code in your software or maybe your developers already are using it. Is it too risky? Large language model technology is progressing at rapid speeds, and policy makers are ill-equipped to catch up quickly. Anything resembling legal clarity may take years to come about. Some organizations are deciding not to use AI at all for code generation, while others are using it cautiously — but everyone has questions.

The advent of cloud computing has revolutionized various industries, with cybersecurity being no exception. In the realm of threat intelligence, cloud computing has emerged as a game-changing force, enhancing the way intelligence is gathered, analyzed, and applied. This post delves into the transformative impact of cloud-based solutions on threat intelligence.