Large language models (LLMs) are transforming how we work and are quickly becoming a core part of how businesses operate. But as these powerful models become more embedded, they also become prime targets for cybercriminals. The risk of exploitation is growing by the day. More than 67% of organizations have already incorporated LLMs into their operations in some way – and over half of all data engineers are planning to deploy an LLM to production within the next year.

There’s an age-old saying you can tell an engineer’s age by their preferred CI/CD (continuous integration and continuous delivery) tool. Depending on who you talk to, the battle-tested Jenkins remains their weapon of choice, while GitHub Actions is the new kid on the block turning heads. However, here’s something that might surprise you – about half of all developers spend less than 20 hours per week on actual software development tasks.

In February 2024, Change Healthcare, one of the biggest IT solution companies in the U.S. healthcare system, suffered from a ransomware attack resulting in a complete shutdown of their IT system. Because of this attack, hospitals and pharmacies experienced interruptions in patient treatments, as well as in payments for several weeks. This is a nightmare for any software developer, security engineer or a company.

Have you ever built software without encountering a single vulnerability? Unlikely. Vulnerabilities are an unavoidable fact of DevSecOps life, and the stakes are higher than before. Cybercrime expenditures are expected to exceed $9.5 trillion globally. Cyber risk quantification has become the need of the hour, not just for security teams and executives but also for developers.

Logs tell the hidden story of your IT infrastructure – what’s working, what’s breaking, and what could be under attack. You’re left sifting through a chaotic stream of events, risking missed insights crucial for maintaining security and operational stability. And the stakes couldn’t be higher. The average global data breach cost hit $4.45 million, with U.S. companies facing an even steeper $9.48 million per incident.

Are you caught in a Yarn versus NPM debate? It is not only because of personal choice – the selection can alter the course of your development. Yarn and NPM are the most common tools for managing virtual dependencies in the JavaScript ecosystem. However, it is important to point out that each has its own set of strengths and trade-offs.

AI code generators are revolutionizing the way developers write and maintain code. These advanced tools leverage machine learning (ML) and natural language processing (NLP) to significantly boost productivity, improve code quality, and enhance security. However, they can sometimes introduce subtle vulnerabilities if not carefully monitored. With generative AI, software developers can complete coding tasks up to x2 faster.

Ever wonder what lurks in your code that static analysis can’t find? That’s where Dynamic Code Analysis (DCA) comes into play. Unlike static analysis, which inspects code without running it, DCA examines software during execution. For developers, DCA is invaluable because it provides real-time insights into how your code operates under actual conditions.

Imagine you’re all prepared to roll out your latest feature, and suddenly, right before launch, you discover a security vulnerability concealed in your code. Depending on the severity, developers can spend anywhere from 7 hours to days or even months finding and fixing these vulnerabilities. A critical vulnerability could set your release back by weeks, while a simple fix might take a day.

We’ve all been there—staring at code, hoping no hidden traps are waiting to cause chaos down the line. That’s where secure code reviews come in. Think of them as your last chance to catch those pesky bugs and vulnerabilities before they wreak havoc. And here’s a little reality check—those cutting-edge LLMs? They suggest insecure code 30% of the time. So, even with AI on our side, we still need to stay sharp.