Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

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.

More news about Artificial Intelligence (AI)? We know. It’s hard to avoid the chatter — and that’s for good reason. The rise of AI has many people excited for things to come. But many others are, quite understandably, concerned about the ethical implications of this powerful technology. Fortunately, the Biden Administration is working to address the concerns of the American people by governing the development and use of AI.

In the fast-paced world of software development, too often security takes a backseat to meeting strict deadlines and delivering new features. Discovering software has accrued substantial security debt that will take months to fix can rip up the schedules of even the best development teams. An AI-powered tool that assists developers in remediating flaws becomes an invaluable asset in this context.

Since announcing Charlotte AI, we’ve engaged with many customers to show how this transformational technology will unlock greater speed and value for security teams and expand their arsenal in the fight against modern adversaries. Customer reception has been overwhelmingly positive as organizations see how Charlotte AI will make their teams faster, more productive and learn new skills, which is critical to beat the adversaries in the emerging generative AI arms race.