AI agents in cybersecurity are software systems, usually built on large language models, that take a security goal and work toward it by calling tools, reading what comes back, and deciding what to do next. A defender's agent might search a SIEM, look up a file hash, run an attack simulation, or draft a detection rule. The key trait is that the agent chooses its own next step instead of following a fixed script.
How AI Agents Work
An agent runs a loop. It receives a task, such as "check whether we were exposed to this campaign." It plans a first step, calls a tool (for example, a query against EDR process data), reads the result, and decides whether it has enough to answer or needs another step. The loop continues until the agent can give an answer or reaches a limit set by its operators.
Three things shape what an agent can do:
- Tools: the APIs it can call. An agent with read-only SIEM access can investigate but cannot change anything.
- Context: the instructions, data and prior findings it can see, such as asset lists, retention limits or past hunts.
- Guardrails: limits on scope, cost and actions, plus points where a person must approve.
Where Defenders Use AI Agents
- Alert triage: collecting context for an alert and proposing a verdict.
- Threat hunting: turning a threat report into searches across several tools and summarizing the results.
- Detection engineering: drafting rules, testing them against past data, and tuning noisy ones.
- Security validation: running safe attack simulations and explaining which steps were blocked, detected or missed.
- Threat intelligence: extracting behaviors and indicators from reports and mapping them to MITRE ATT&CK.
AI Agents vs Chatbots and Copilots
A chatbot or copilot answers when asked. It might explain a log line or write a query, but a person runs the query and decides what to do with the output. An agent runs the query itself, reads the output, and keeps going. That makes agents useful for work with many steps, and it also means their mistakes can compound if nobody checks them. Good agent designs show every step and every query so a person can follow the reasoning.
Risks to Plan For
Agents can misread data, draw conclusions from incomplete logs, or act outside their intended scope. Common safeguards include read-only access by default, approval for any change, logging every tool call, and requiring the agent to state what it could not check. An agent that says "no matches" when the relevant logs were not collected gives false comfort. Attackers also use AI agents, mainly to speed up reconnaissance and phishing, which is one more reason defenders test their controls often.
Example
A team asks an agent whether a newly published ransomware report affects them. The agent extracts six techniques from the report, runs hunts for each in the SIEM and EDR, and finds no matches for five. For the sixth, it notes that command-line logging is off on a group of older servers, so it reports "could not check" for those hosts instead of "not seen."
FourCore Analyst uses a team of specialist agents in this way across the FourCore platform, with teams setting scopes and approving every change.
Frequently Asked Questions
What is an AI agent in cybersecurity?
It is an AI system that takes a security task, calls tools such as SIEM searches or simulations, reads the results, and decides on further steps until it has an answer. It differs from a chatbot because it acts rather than only responding.
Are AI agents used by attackers too?
Yes. Attackers use AI to speed up reconnaissance, write phishing content and adapt tooling. Defenders respond by testing controls against current techniques and hunting for the behaviors those tools produce.
How do you keep AI agents under control?
Limit their tools and scope, require approval for changes, log every action, and review their findings. Agents should also report gaps in data rather than presenting partial results as complete.