What is AI Monitoring? Core Concepts and Best Practices

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:

1. Using AI as a tool to help find threats faster
2. 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 execute attacks that previously required expertise they didn't have.

The report also documents adversaries going after AI systems directly. Since April 2025, multiple threat actors have exploited a critical vulnerability in Langflow AI — a widely used tool for building AI agents and workflows — to establish persistence, steal credentials, and deploy malware. The AI tool wasn't incidental to the attack. It was the target.

Shadow AI

At the same time, employees are using AI tools that IT never approved, sometimes uploading internal documents or customer data to consumer AI services with no enterprise data agreements in place. This is what the industry calls shadow AI, and it's a meaningful data exposure risk that traditional data loss prevention tools often can't detect.

AI-powered cyberattacks

The threat landscape has another dimension: adversaries using AI to accelerate their own operations. AI is helping them write more convincing phishing emails, translate lures into additional languages, generate malware, and run post-exploitation scripts with less manual effort.

According to the CrowdStrike 2026 Global Threat Report, the average eCrime breakout time (the window between initial access and lateral movement) fell to just 29 minutes in 2025, a 65% increase in speed from the year before. The fastest observed breakout took 27 seconds. Speed matters because it shrinks the window defenders have to respond. With AI on the attacker's side, that window shrinks even further.

Two problems under one name

When security practitioners talk about AI monitoring, they're usually talking about one of two things, and it's worth being clear about which is which.

AI as a defensive capability

This is the use of machine learning and AI-powered tools to help security teams detect threats they'd otherwise miss. Traditional rule-based detection systems struggle with modern attacks because adversaries increasingly operate without malware. The CrowdStrike 2026 Global Threat Report revealed that 82% of detections in 2025 involved no malware at all, up from 79% the year prior. Attackers use legitimate tools, stolen credentials, and hands-on-keyboard techniques that appear normal to rule-based systems.

AI-powered behavioral analysis addresses this by establishing a baseline of what normal looks like for a given user, host, or process and then identifying deviations from that baseline. Because the model learns what's normal rather than relying on a static set of rules, it can surface subtle, low-signal anomalies that other tools ignore. These anomalies are often the earliest signs of an intrusion.

This is AI working on behalf of defenders, and it's now a foundational part of modern threat detection.

Monitoring your AI systems as assets to protect

This is the newer problem. As organizations deploy AI tools and agents, these systems also need to be observed. That means:

• Logging prompts and responses for forensic and compliance purposes
• Tracking which employees are using which AI tools and how
• Detecting when an AI agent takes an unexpected action
• Catching when sensitive data flows into a model that shouldn't have access to it

These two dimensions of AI monitoring aren't competing ideas. They work together. When organizations focus solely on using AI for threat detection but fail to monitor their own AI systems, they leave a significant blind spot.

What can go wrong on the AI attack surface

A few specific threat categories are worth understanding clearly.

Prompt injection

Prompt injection is an attack in which malicious instructions are embedded in content that an AI system reads — such as a document, a webpage, or an email — causing the AI to execute those instructions rather than its intended task. If an AI agent reads a manipulated document and then takes actions based on hidden instructions within it, the agent effectively becomes a vector for the attacker.

CrowdStrike researchers have cataloged more than 180 known prompt injection techniques. Adversaries have also begun using prompt injection offensively against defenders, embedding hidden instructions in phishing emails specifically designed to confuse or mislead AI-based triage systems.

Malicious AI infrastructure

In 2025, threat actors published a fake Model Context Protocol (MCP) server that impersonated a legitimate one, silently forwarding users' emails to an attacker-controlled address. As AI tools proliferate and organizations integrate third-party AI services and agents into their workflows, the attack surface includes the AI infrastructure itself, not just the data flowing through it.

Shadow AI

Employees may use AI tools that haven't been approved by IT or security teams, often without realizing the risk. Uploading internal contracts, customer records, or source code to a consumer AI tool can expose that data in ways that are difficult to detect and hard to remediate.

Jailbreaks and model manipulation

Attackers may attempt to manipulate an AI model by crafting inputs designed to bypass its built-in guardrails. They aim to coerce the model to produce output or take actions it otherwise wouldn't.

Data exfiltration through AI interactions

Either accidentally or deliberately, AI-leveraging organizations risk having sensitive information — credentials, regulated data, and intellectual property — flow out through API calls to external AI models.

AI agents as non-human identities

This final category is gaining attention quickly. Autonomous agents operate with real system permissions. Hijacking an agent effectively means gaining access to whatever the agent has access to. As agents take on more privileged roles, they become high-value targets.

What to look for in an AI monitoring approach

When evaluating whether your organization has adequate visibility into AI activity, consider the following key questions:

• Can you see what AI tools are running in your environment, whether they’re sanctioned or not?
• Can you tell what data is flowing into those tools and where it's going afterward?
• If an AI agent behaves unexpectedly, do you have logs that show what inputs triggered what actions?
• Can you apply consistent security policies across AI usage the same way you would for any other endpoint or identity in your environment?

That last question matters more than it might seem. AI monitoring that lives in a separate console — disconnected from the rest of your security data — loses the cross-domain context needed to make detections actionable. An alert about a suspicious prompt interaction means something very different when you can correlate it with an identity event, a cloud anomaly, or a prior endpoint detection. Without that context, you're managing AI risk in a silo.

The best implementations bring AI visibility into existing security workflows rather than requiring teams to manage a separate toolset. Security teams are already stretched thin. Adding another console and another set of alerts without integration into existing processes tends to create coverage gaps rather than close them.

How CrowdStrike approaches AI monitoring

CrowdStrike addresses AI monitoring across several components of the CrowdStrike Falcon® platform:

• CrowdStrike Falcon® AI Detection and Response (AIDR) secures the AI interaction layer, monitoring prompts, responses, and agent actions in real time and capturing runtime logs for compliance and investigations.
• CrowdStrike Falcon® Cloud Security operates at the infrastructure level, providing visibility across cloud-hosted AI services and scanning for misconfigurations and vulnerabilities.
• CrowdStrike Falcon® Shield handles AI agent governance across SaaS environments, discovering agents, mapping them to their human owners, and detecting anomalous behavior.
• CrowdStrike Signal represents the defensive side of AI monitoring: self-learning detection models that build a behavioral baseline for each user and host, surfacing low-signal deviations that early-stage intrusions tend to leave behind.

All of these feed into the broader Falcon platform, so AI-related activity shares context with endpoint, identity, and cloud data. That's what turns AI monitoring from an isolated visibility exercise into something that can actually change outcomes.

Organizations that build AI monitoring into their security program early, before their AI usage expands further, will be in a fundamentally different position than those that treat it as a future problem. 

To learn more about how to protect your organization confidently in the era of AI, contact our team of security experts today.