Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Ever get one of those annoying error messages on your phone that gives way too much detail? You know, the ones that tell you the line of code that failed or the exact database query that crashed the app. As an app user, you may dismiss the message and move on. But did you know those overly verbose error messages could be exposing your personal data?

AI has made good on its promise to deliver value across industries: 77% of senior business leaders surveyed in late 2024 reported gaining a competitive advantage from AI technologies. While AI tools allow developers to build and ship software more efficiently than ever, they also entail risk, as AI-generated code can contain vulnerabilities just like developer-written code. To enable speed and security, DevSecOps teams can adopt tools to integrate security tasks into developer workflows.

How good is Symbolic AI as an automated expert system for analyzing code paths and detecting security vulnerabilities? Snyk Code is tested and demonstrates the benefits of rule-based, algorithmic systems.

It's no surprise that 18–29-year-olds are turning to social media for cybersecurity information. As digital natives, this age group naturally gravitates toward platforms where information is fast, accessible, and constantly updated. But how effectively are they absorbing these short snippets—and are they likely to share it forward? More importantly, what happens if that cybersecurity information is inaccurate?

As businesses rely more on technology, the need to identify and remediate vulnerabilities becomes ever more pressing to avoid devastating breaches. Automated penetration testing offers a revolutionary approach to vulnerability detection, utilising cutting-edge tools to mimic hacker behaviour and uncover weaknesses in systems. This method not only enhances the efficiency of assessments but also significantly reduces the time and resources required compared to traditional penetration testing.

With the announcement of Anthropic’s Claude 3.7 Sonnet model, we, as developers and cybersecurity practitioners, find ourselves wondering – is the new model any better at generating secure code? We commission the model to generate a classic CRUD application with the following prompt: The model generates several files of code in one artifact, which the user can manually copy and organize according to the file tree suggested by Claude alongside the main artifact.