AI tools are everywhere now. Employees use them to draft emails or summarize documents. Developers use them to write code. Engineering teams build AI-powered applications. Autonomous AI agents are starting to take real actions inside real business systems, such as booking meetings or querying databases. All of this is genuinely useful. However, it creates a security problem that most organizations haven't fully reckoned with yet. AI monitoring is the practice of observing, logging, and analyzing how AI systems behave to help security teams detect threats, enforce policy, and maintain visibility. The term covers two distinct things that are easy to conflate: - Using AI as a tool to help find threats faster - Monitoring your AI systems to make sure they're behaving as intended. Both matter and require attention, but they're different problems that call for different thinking. Introducing the new AI attack surface For most of the history of cybersecurity, the things defenders had to protect were relatively well understood: endpoints, networks, identities, cloud workloads, etc. The advent of AI added a new layer that didn't fit neatly into any of those categories. Vulnerabilities in AI agents and tools When a user types a prompt into a generative AI (GenAI) tool, or when an AI agent reads a document and decides what to do next, something is happening that most traditional security tools can't see. That interaction layer — between users, models, agents, and data — is where a new category of attacks is taking shape. Adversaries noticed the gap quickly. According to the CrowdStrike 2026 Global Threat Report, attacks by AI-enabled adversaries increased by 89% in 2025. This number reflects something important: AI isn't just helping a handful of sophisticated nation-state threat actors move faster; it's raising the floor for everyone. Less-skilled adversaries can now use AI to