Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

In today’s rapidly evolving digital landscape, organizations not only seek out, but need to harness the power of emerging technologies to stay ahead of the competition. Two of the most promising trends in the tech world are generative AI and low-code/no-code development. Generative AI, in particular, has generated the majority of the headlines, with seemingly infinite use cases to spur productivity for end users and business.

Over the past few months, our collective fascination with AI has reached unprecedented heights, leading to an influx of information and discussions on its potential implications. It seems that wherever we turn, AI dominates the conversation. AI has captivated the imaginations of tech enthusiasts, researchers, and everyday individuals alike. At the tender age of 11, I received my very first computer, the legendary ZX Spectrum. Looking back, it's hard to believe how much has changed since then.

With the advent of complex technology ecosystems like agile development processes, cloud-native platforms, and the rising use of open-source software, the importance of continuous Security and compliance has increased more than ever. As a result, leaders in the software industry must advise their teams to incorporate developer-friendly security tools into their DevSecOps pipelines.

Verizon has renewed its warnings to customers about the threat of smishing, a social engineering approach that relies upon texts as opposed to other communication channels like the email used in phishing. The smishing problem may be smaller than the phishing problem, or the robocall nuisance, but it represents a comparable threat that organizations should address in their risk management process.

Patient data privacy is of utmost importance in today’s healthcare environment. Security is equally critical, forming the foundation of trust between patients and providers. Healthcare organizations handle incredibly sensitive information, including medical histories, diagnoses, and treatment plans. Mishandling this data carries significant risks far beyond just financial implications. These threats come in the form of significant monetary fines under some regulations.

Generative AI models have a long history in artificial intelligence (AI). It all started back in the 1950s with Hidden Markov Models and Gaussian Mixture Models, and it really evolved with the advent of Deep Learning. In the past five years alone, we have gone from models with several millions of parameters to the latest being GPT-4, estimated to have over 100 trillion parameters.

ChatGPT is all the rage these days. Its ability to magically produce coherent and typically well-written, essay-length answers to (almost) any question is simply mind-blowing. Like any marketing department on the planet, we wanted to “latch onto the news.” How can we connect Cato and ChatGPT? Our head of demand generation, Merav Keren, made an interesting comparison between ChatGPT and Google Search.