Generative AI, the transformative technology causing a stir in the global tech sphere, is akin to an enthralling narrative with its charming allure and consequential dark underbelly. Its most notable impact is forecasted in the realm of identity proofing, creating ripples of change that demand our immediate attention.

In the modern, cloud-first era, traditional data protection technology approaches struggle to keep up. Data is rapidly growing in volume, variety, and velocity. It is becoming more and more unstructured, and therefore, harder to detect, and consequently, to protect.

There is no question that cybersecurity is on the brink of an AI revolution. The cloud security industry, for example, with its complexity and chronic talent shortage, has the potential to be radically impacted by AI. Yet the exact nature of this revolution remains uncertain, largely because the AI-based future of cybersecurity is still being invented, step by step.

The rapid and widespread adoption of artificial intelligence (AI) has ushered in a new era of technological advancement, revolutionizing various industries and becoming immensely popular worldwide. AI-driven applications and solutions have streamlined processes, improved efficiency, and enhanced the overall user experience. However, this surge in AI’s popularity also comes with a dark side.

This is the third part of a blog series on AI-powered application security. Following the first two parts that presented concerns associated with AI technology, this part covers suggested approaches to cope with AI concerns and challenges. In my previous blog posts, I presented major implications of AI use on application security, and examined why a new approach to application security may be required to cope with these challenges.

For anyone who follows industry trends and related news I am certain you have been absolutely inundated by the torrent of articles and headlines about ChatGPT, Google’s Bard, and AI in general. Let me apologize up front for adding yet another article to the pile. I promise this one is worth a read, especially for anyone looking for ways to safely, securely, and ethically begin introducing AI to their business.

Six months ago, the question, “Which is your preferred AI?” would have sounded ridiculous. Today, a day doesn’t go by without hearing about “ChatGPT” or “Bard.” LLMs (Large Language Models) have been the main topic of discussions ever since the introduction of ChatGPT. So, which is the best LLM? The answer may be found in a surprising source – the dark web. Threat actors have been debating and arguing as to which LLM best fits their specific needs.

As the world experiences the AI gold rush, organizations are increasingly turning to enterprise AI solutions to gain a competitive edge and unlock new opportunities. However, amid the excitement and potential benefits, one crucial aspect that must not be overlooked is data security — in particular, protecting against adversarial attacks and securing AI models. As businesses embrace the power of AI, they must be vigilant in safeguarding sensitive data to avoid potential disasters.

AI has become a hot topic thanks to the recent headlines around the large language model (LLM) AI with a simple interface — ChatGPT. Since then, the AI field has been vibrant, with several major actors racing to provide ever-bigger, better, and more versatile models. Players like Microsoft, NVidia, Google, Meta, and open source projects have all published a list of new models. In fact, a leaked Google document makes it seem that these models will be ubiquitous and available to everyone soon.

We recently published a blog post detailing how threat actors could leverage AI tools such as ChatGPT to assist in attacks targeting operational technology (OT) and unmanaged devices. In this blog post, we highlight why healthcare organizations should be particularly worried about this.