Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

AI application security protects AI-powered apps, including those powered by large language models ( LLMs), from unique threats like prompt injection, data poisoning, and model theft. It achieves this by securing the entire lifecycle, including code, data, algorithms, and APIs, using specialized tools and processes that go beyond traditional security measures. It involves securing the AI model’s behavior, training data, and outputs.

The cybersecurity landscape is facing its own perfect storm: AI-powered attacks coupled with resource constraints and regulator pressure, demanding a fundamental shift in SecOps to rise above. With AI showing no signs of slowing down, these issues are not fleeting. They are here to stay, and it is our responsibility to meet them head-on with efficient, AI-powered solutions that allow SecOps teams to conquer the world’s most innovative attacks.

At RSAC 2026, Arctic Wolf set the agenda for the future of cybersecurity and AI. Throughout the week, we were at the center of the industry dialogue, shaping how the market is approaching agentic AI in cybersecurity and setting clear expectations for where the industry is headed next. The launches of the Aurora Superintelligence Platform and the Aurora Agentic SOC raised the bar for the industry.

A recently patched Google Chrome vulnerability is a signal security leaders cannot ignore. But it's only the beginning of a much larger story. In January 2026, a high-severity vulnerability was disclosed in Chrome's Gemini AI integration: CVE-2026-0628. The flaw allowed a malicious browser extension with only basic permissions to escalate privileges and gain access to a user's camera, microphone, local files, and the ability to screenshot any website, all without user consent. Google patched it quickly.

When your CNAPP flags a suspicious dependency in an AI agent container, your WAF logs an unusual API spike, and your SIEM shows a burst of cloud storage calls—are those three separate incidents or one rogue agent attack? Most security teams treat them as three tickets in three queues, investigated by three people who may never connect the dots. By the time someone pieces together that a single compromised agent drove all three signals, the attacker has already moved laterally and exfiltrated data.

Your AI-BOM shows every model, tool, and data source you deployed. But when your SOC investigates an alert about unusual agent behavior, that inventory tells them nothing about what actually happened at runtime. Static AI-BOMs document what you intended to run. Attackers exploit what your AI workloads actually do in production: which APIs they call, what data they touch, and how they use approved tools in unapproved ways.

Fresh off two weeks of back-to-back meetings in Washington, DC, and on the floor/in the wings of the RSA Conference, one theme echoed through nearly every conversation I had with senior government officials and public policy leaders from global technology companies: agentic AI security is the defining emerging security challenge of this moment — and policy is not keeping pace.

In 2025, we embarked on a new journey to secure the most important technology transformation of this decade – generative AI. Our vision is to help companies secure their AI fast, so that they can innovate on the cutting edge and put AI and agentic use cases into production. To do this, we built Evo, the world’s first agentic orchestrator for AI security. The foundation of any product is customer needs.

“AI-powered DAST” is everywhere. It signals progress, but assumes something fundamental was missing. It wasn’t. DAST struggled not from lack of intelligence, but from lack of depth. Most tools never reached inside authenticated, stateful, multi-step journeys where real logic, sensitive data, and critical vulnerabilities exist. That’s the part Appknox solved years ago. AI here is not a reset. It is an accelerator, applied to a system already operating where risk actually lives.