Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

It usually starts the same way. The CISO comes back from a board meeting having signed off on agentic AI for production. The SOC lead is told, in roughly that many words, to build detection for the agents. And the security stack she has — CNAPP for posture, EDR on the nodes, container runtime sensors, a SIEM ingesting everything — was architected before AI agents existed as a workload class.

OpenAI recently introduced Daybreak, a cybersecurity initiative designed to apply frontier AI models to vulnerability discovery, secure code analysis, and earlier remediation across the software lifecycle. By combining advanced reasoning and planning capabilities, Daybreak aims to help organizations identify and address weaknesses before they reach production. This is a meaningful step forward, but it is also a continuation of a long-standing approach.

Author: Alexander Ivanyuk, Senior Director, Technology Generative AI is no longer a side experiment inside businesses. It is moving into normal work: writing, summarizing, coding, research, customer support, internal search and repeatable workflows. OpenAI says it now serves more than one million business customers, more than seven million ChatGPT workplace seats, and roughly 8x growth in weekly enterprise messages since November 2024.

As organisations continue integrating AI capabilities into customer-facing applications, internal tooling, and operational workflows, the security implications of these systems are becoming increasingly important. Large Language Models (LLMs), AI assistants, and automated decision-making features are now appearing across SaaS platforms, support systems, and enterprise applications, often connected directly to sensitive data and business processes.

AI agents are already inside most enterprise environments. They complete tasks, connect to live systems, and make decisions that used to require a human. Gartner projects that 40% of enterprise applications will include task-specific AI agents by the end of 2026, up from less than 5% today. What was an experiment two years ago is now a core part of how work gets done. If your organization is adopting AI agents or planning to, security is not something you can figure out later.

This Anti-Ransomware Day, it's important to recognize the ever-changing landscape of cyber threats and how organizations can fortify their defenses. The evolution from traditional ransomware to cyber extortion over the last few years reflects a professionalized, decentralized ecosystem. To arm your organization against this danger, understanding the current landscape and implementing robust defense strategies is essential.

When a new vulnerability is announced, the race begins. Security teams jump into action, checking exposure, triaging events, identifying affected systems, and figuring out how quickly they can patch. The clock is ticking and they know it. At the same moment, threat actors are doing their own version of that work. They’re reading the same advisories, watching the same feeds, and asking a much simpler question: Who is still vulnerable?

SaaS-only platforms are betting everything on automation. But when the threat landscape demands judgement, data volume alone is not the answer. For years, a certain category of threat intelligence vendor has sold the same idea: feed your data into our platform, let the AI process it, and your security team will have everything they need. It is a compelling proposition, particularly for organisations under pressure to demonstrate coverage without expanding headcount.