The buzz around artificial intelligence (AI) is showing no sign of slowing down any time soon. The introduction of Large Language Models (LLMs) has brought about unprecedented advancements and utility across various industries. However, with this progress comes a set of well-known but often overlooked security risks for the organizations who are deploying these public, consumer-facing LLM applications.

Remember asking your teachers when you would need to know history facts outside of school? They probably said that learning history is important in understanding our past and how society has changed and progressed over time, and that we can learn from past experiences and mistakes. They were right, of course (even if it might not have felt like it then). And that’s all equally true when it comes to the history of security.

In the rapidly evolving landscape of cybersecurity, the need for a robust and intelligent assistant capable of analyzing, summarizing, and reacting to events is paramount. This is why we designed Sysdig SageTM, our large language model (LLM)-based cloud security analyst, to be an expert in cloud detection and response (CDR). Sysdig Sage excels at summarizing complex events and providing clear explanations, which is crucial for identifying and promptly reacting to potential threats.

No executive wants to be blindsided by risks that should have been reasonably anticipated, especially the CEO, CFO, and board members. In the CISO Desk Reference Guide, Gary Hayslip, Bill Bonney, and I wrote extensively about how CISOs play a critical role in contextualizing digital and cyber risks to the organization’s broader enterprise risk management practices.

In recent years, almost every major cloud breach has been marked by overly permissive credentials, followed by lateral movement and privilege escalation. These vulnerabilities have allowed attackers to navigate through systems with ease, escalating their privileges to cause significant harm. It’s crucial for cloud threat responders to be aware of threats as they occur and to be able to contain these attacks swiftly and effectively.

In today’s rapidly changing and evolving cloud-native environments, security and infrastructure teams face challenges ranging from managing complex deployments to ensuring capability across their entirety of their diverse infrastructure. EDR and XDR tools cannot provide comprehensive coverage of cloud workloads, making them fundamentally unsuited for cloud security.

Artificial intelligence has taken over almost every aspect of our everyday lives. In cybersecurity, generative AI models with natural language processing are commonly being used to predict, detect, and respond to threats. But AI security assistants, although an upgrade from traditional machine learning, only provide very basic queries and summarization, which is insufficient to fully comprehend modern cloud attacks. As part of an ongoing effort to improve the cloud detection and response (CDR) experience,

Generative AI (GenAI) is a top priority for organizations looking to increase productivity and solve business problems faster. In cloud security, AI chatbots to aid security practitioners are becoming more common, but to date, most of these solutions offer only basic queries and summarization. Diverse cloud environments and evolving threats require more from an AI security analyst.