Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

As the adage goes, time is money, and nowhere does this ring more true than in an evolving threat landscape. The faster companies detect, respond, and recover from data breaches, the better for their pockets. Using AI and security automation to shorten the breach lifecycle has been shown to save $2.2 million more on average compared to not employing these technologies.

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.

As cyber threats evolve in scale and sophistication, governments and regulatory bodies are tightening cybersecurity and data protection regulations. Compliance is not only about avoiding fines but also about building trust, enhancing operational resilience, and safeguarding long-term business success. Data breaches and cyberattacks can disrupt operations and as such, organisations should prioritise compliance to mitigate financial and legal risks whilst fostering customer confidence.

Please see part Part I for our introduction to leveraging AI in the enterprise and Part II for why the legacy landscape falls short in the age of GenAI.

The transition of AI from experimental to production is not without its challenges. Developers face the challenge of balancing rapid innovation with the need to protect users and meet strict regulatory requirements. To address this, we are introducing Guardrails in AI Gateway, designed to help you deploy AI safely and confidently.

Security through obscurity is based on the idea that if attackers don’t know how a system works or even if it exists, they’ll have a harder time breaching it. Despite repeatedly broken implementations and lacking support from standards bodies, this concept continues to be widely used. Secret doesn’t always mean safe – and it can even give a false sense of security.

While malware growth is something we have been experiencing over the past few years, the increase observed by WatchGuard's threat lab team in Q3 2024 was the highest to date. Q3 saw astronomical growth in total endpoint malware threats, reaching 300.48% with 420,304 threats. The previous high was recorded in Q1 2024, when there was an 81.77% rise, almost double the previous quarter. However, Q3 almost quadrupled the figure for Q2 2024 when 104,951 threats were detected.

Reverse engineering the AsyncRat malware infection, exploring the means of deployment and staging.

A critical heap buffer overflow vulnerability in the AWS C Common library was discovered autonomously through an AI-automated fuzz testing solution, CI Fuzz, and has been fully addressed with a patch. In this post, we explore the vulnerability and its potential impact on embedded systems.