A guest post by James Berthoty the founder of Latio Tech. The shift to cloud has meant an explosion in cloud security-related acronyms – so many that it can be difficult to know what you currently have versus what’s missing or available. First we bought CSPMs (Cloud Security Posture Management), then CWPPs (Cloud Workload Protection Platforms), then CNAPPs (Cloud Native Application Protection Platform), then CDRs (Cloud Detection Response), and now KDRs (Kubernetes Detection Response).

Over the last year there has been a prevailing sentiment that while AI will not necessarily be replacing humans, humans who use AI will replace those that don’t.

Today, every organization has significant investments in their overall tech stack, both financially and operationally, and as technology evolves, it is crucial that we keep pace to ensure success. For many organizations, legacy applications house some of their most mission critical applications, and replacing or retiring them would be incredibly costly, monetarily and in terms of productivity. With application modernization, businesses can now skip the hassle of completely rebuilding applications.

Everyone knows that running outdated computer applications comes with annoyances and risks. An outdated application might face performance issues or just become slower than modern versions. It might face compatibility issues and have limited functionalities. It might lose technical support and even fail to meet current industry standards and regulations, which could put your organization at risk for non-compliance penalties and legal action.

We recently wrote about npm audit fix, which is an add-on to the excellent npm audit, that has become a fundamental tool for managing software packages in Node.js projects. However, developers working with other languages also require specialized tools for Software Composition Analysis (SCA). At Jit, our tool of choice for SCA scanning across a diversity of programming languages is OSV Scanner, a best of breed OSS solution maintained by Google.

Cybersecurity has always been a complex field. Its adversarial nature means the margins between failure and success are much finer than in other sectors. As technology evolves, those margins get even finer, with attackers and defenders scrambling to exploit them and gain a competitive edge. This is especially true for AI.

In the 50th episode of the Trust Issues podcast, host David Puner interviews Justin Hutchens, an innovation principal at Trace3 and co-host of the Cyber Cognition podcast (along with CyberArk’s resident Technical Evangelist, White Hat Hacker and Transhuman Len Noe). They discuss the emergence and potential misuse of generative AI, especially natural language processing, for social engineering and adversarial hacking.

There has been widespread discussion about the potential impact of AI on society. While the UK Government has played an active role in shaping the conversation around the security and safety of AI, it has also demonstrated its intention to embrace AI where productivity and efficiency benefits may be found.

Generative AI is an exciting technology that is now easily available through cloud APIs provided by companies such as Google and OpenAI. While it’s a powerful tool, the use of generative AI within code opens up additional security considerations that developers must take into account to ensure that their applications remain secure. In this article, we look at the potential security implications of large language models (LLMs), a text-producing form of generative AI.