7 Real-World Applications of AI Agents in Cybersecurity

Despite the recency of the hype, artificial intelligence (AI) in cybersecurity is nothing new. It has been used in this field for years, whether for heuristic malware detection and phishing prevention or calculating vulnerability scores. Even endpoint antivirus solutions have relied on machine learning components since the 1990s.

But, fast-forward to today, and AI has become much more widespread. Perhaps the key difference is that AI is now available to end users rather than only developers, so more people are using it (or exploring its use) beyond traditional fields. On top of that, AI has become much more advanced, which has resulted in many new use cases emerging in recent years. 

Take AI agents, for example. Nowadays, most routine tasks can be outsourced to one agent or another, and the role of humans is to keep up with the rapid development of AI, connect the dots, and think critically.

In this post, we will explore some of the use cases of AI agents in cybersecurity.

Let’s start with a definition that will help us narrow down the scope, because nowadays everything has some sort of AI, and we can’t cover everything in one article.

What Is an AI Agent?

An AI agent is an autonomous or semi-autonomous AI system that is capable of:

• understanding the environment,
• making decisions, 
• taking actions,
• pursuing specific goals. 

Not every AI is like that. There are plenty of models that only perform their predefined functions based on patterns fed to the model, such as facial recognition, classifying an email as spam, or matching a file’s code to a known malware signature. They don’t reason and and cannot act beyond their narrow scope.

Agentic AI, however, is built to act independently and continuously improve. Because of this, it can be used to automate entire workflows, not just one task.

Real-World Use Cases of AI Agents in Cybersecurity

1. AI Agents for Automated Vulnerability Remediation

AI agents are good at executing vulnerability remediation workflows for non-critical systems. For example, an AI agent integrated into a security platform can automatically close certain ports on systems where a vulnerability was discovered that could be exploited using a certain port. The value here is that it can do so almost instantaneously on hundreds of systems at once, without making support engineers go through hundreds of tickets. As a result, security teams in large organizations with hundreds or thousands of similar hosts can reduce metrics like Mean Time to Remediate (MTTR).

For cloud environments, platforms can deploy AI agents that enforce security policies. An agent tied to an email security platform, for instance, can automatically force a password reset and end all active sessions for a user whose Microsoft 365 account shows signs of compromise.

Even test suites can be optimized by enabling AI agents to identify and delete redundant or irrelevant tests, keeping testing processes lean and more focused on critical areas. 

Examples:

• Google AI Remediation Agent: Automates routine remediation activities, such as system restarts and file system housekeeping, helping improve system uptime by ensuring that repetitive maintenance tasks are error-free.  
• Darktrace NDR: Darktrace uses AI to rapidly contain and disarm network threats based on a granular understanding of normal device or user behavior. Its autonomous response is context-aware and customizable.
• Vulnerability remediation agent by Port.io: This platform allows users to create AI agents that analyze vulnerabilities and use Model Context Protocol (MCP) capabilities to enrich vulnerability data. Automations can then be set up to trigger the agent when severity changes, so it generates an AI summary, which would then trigger Claude Code to fix the vulnerability.   

2. Using AI Agents for Open-Source Intelligence (OSINT)

AI agents have become one of the most widely used OSINT tools, enabling security teams to collect data from multiple sources simultaneously and within seconds. 

Example: OSINT Research with Jake AI

You can task an AI assistant like our Jake AI to do an OSINT report on a target domain or IP address. The agent quickly gathers domain ownership history, related IP addresses, DNS changes, and threat intelligence. 

It then connects the dots across billions of data points to generate actionable intelligence.

There are also other AI agents that can monitor underground forums, dark web markets, and social media sites, and alert the security team when they find a threat actor advertising the sale of employee credentials or proprietary source code related to the company or any accidental information leaks.

3. AI Agents for External Attack Surface Management (EASM)

MCP servers and AI assistants can be used to map exposed infrastructure, including associated subdomains, IP addresses, and digital certificates. 

Example: Generating an EASM Report with Jake AI

For example, asking WhoisXML API’s Jake AI to create an EASM report on example1[.]com would lead to this asset list:

Aside from asset discovery, another EASM process that AI agents can help with is vulnerability scoring. The agent autonomously assigns a risk score to the asset based on severity and exploitability (both in the wild and within the organization’s unique environment), helping security teams prioritize remediation.

4. AI Agent as a Security Operations Center (SOC) Assistant

AI agents can act as decision support tools, handling preliminary stages like ingesting thousands of events and adding critical context such as Indicators of Compromise (IOCs), affected machine data, and account details. They can draft initial security incident reports and build event timelines so that human analysts have something concrete to start with.

Example:

Cisco’s Extended Detection and Response (XDR) solution, for example, relies on AI to create incident reports that SOC analysts can use as templates for the final report.

5. AI as a SOC Tier 1 Analyst

AI agents also can be good at triage, something that SOC Tier 1 analysts spend a lot of time on. They can help remove the noise and reduce manual work that overwhelms human analysts. One study found that AI can lower the alert volume by 30%, reducing the number of tickets requiring human review. That means only real cyber threats are escalated, allowing analysts to focus on higher-value tasks.

For example, an agent detects an anomaly, but its automated checks verify that the activity is actually valid (e.g., a system admin running a legitimate scan). The agent automatically closes the alert as a confirmed false positive and prevents it from being escalated.

That same agent can also analyze hundreds of similar login failures from geographically dispersed users and identify them as part of a coordinated credential stuffing campaign. It then consolidates them into one high-priority ticket.

Examples: 

• SentinelOne’s Purple AI: Its agentic AI capabilities — Auto-Triage and Auto-Investigations — are supposed to automate the repetitive, high-volume work of Tier 1 SOC analysts, adding auto-triage and auto-investigations to the SOC capabilities. 
• Dropzone AI’s AI SOC Analyst: The platform is supposed to receive an alert, automatically formulate hypotheses, gather evidence from various security systems, and conduct an end-to-end investigation without human intervention, effectively covering the spectrum of tasks of a Tier 1 SOC analyst.

6. AI Agents for Predictive Threat Intelligence

AI can not only play whack-a-mole with current threats, but also predict them. It enables the use of predictive modeling based on known vulnerability or threat characteristics and massive data sets to find signals that point to emerging threats or new vulnerabilities. 

Example:

An AI-powered solution like First Watch Malicious Domains Data Feed analyzes billions of data points on internet infrastructure to identify threat actor patterns before a newly registered domain (NRD) is used in an attack. It allows security teams to receive warnings about domains that pose a risk to their organization almost at the time of their registration.

Our research team recently investigated ValleyRAT IoCs published on ThreatFox in October 2025 and found that most of them were detected and predicted to be malicious by First Watch much earlier than they were disclosed on ThreatFox — from 2 to 277 days earlier.

AI-based solutions like First Watch allow security teams not only to react to an existing threat but also to predict potential threats and block them before they can cause damage. 

7. Using AI as Security Awareness Training Generators and Managers

The rise of generative AI allows attackers to create realistic and grammatically perfect phishing emails. AI agents can be the defense against this. They automate and personalize security awareness training, which helps reduce human risk.

Examples:

• Companies like Right-Hand AI use agentic security awareness platforms to automate deepfake vishing and social engineering simulations. With agentic AI, these platforms can custom-build phishing email templates and training modules based on specific company data or real-time threat intelligence.

• On the other hand, Abnormal's AI Phishing Coach creates behavior-based phishing simulations that mirror the real phishing emails employees actually receive. When an employee interacts with a suspicious message, the agent provides instant, personalized coaching. Over time, this continuous reinforcement builds better security instincts.

Conclusion

There are fears that AI is taking the jobs of cybersecurity professionals, but in reality, it is simply changing them. Human skills are still needed, especially for complex decision-making, critical thinking, ethical oversight, and strategic planning.

However, for repetitive, Tier 1 tasks, such as data gathering, enriching, or filtering, artificial intelligence helps immensely. Different AI agents can automate tasks that once consumed hundreds of human hours, whether it’s routine OSINT, remediation workflows, drafting reports, or something else. AI models provide the speed and volume of analysis, and then it is up to humans to understand what to do with that information, making them supervisors, strategists, and quality controllers.

Try out WhoisXML API’s AI products: Jake AI Internet Intelligence Assistant, WhoisXML API’s MCP Server, and First Watch Malicious Domains Data Feed.