What is AI Agent
An AI agent is a software system that combines advanced AI models with access to tools, data sources, and workflows to perform tasks on behalf of users under defined permissions and controls.
AI agents combine the intelligence of AI models with the ability to take actions, enabling systems to move beyond generating information and actively assist with real-world tasks.
In cybersecurity, AI agents act as intelligent assistants that can analyze security data, understand context, make recommendations, and execute approved actions across security platforms.
AI Agent vs Traditional Automation
Traditional automation relies on predefined rules and workflows. It performs specific actions when a known condition is met, for example: "if an IP address is identified as malicious, block it."
AI agents differ by evaluating broader context, analyzing relationships between events, adapting to new information, and determining the most appropriate next action based on the available evidence.
In cybersecurity, this enables AI agents to handle more complex tasks such as investigating unknown threats, correlating activity across multiple data sources, and assisting analysts with decision-making.
How Are AI Agents Used in Cybersecurity
Cybersecurity AI agents support Security Operations Center (SOC) teams by collecting and analyzing data from multiple sources, understanding attacker behavior, and assisting with investigation and response activities.
Common use cases include:
- Alert investigation: Analyze security alerts, correlate related events, identify attack patterns, and determine potential attack paths.
- Threat intelligence analysis: Enrich indicators such as IP addresses, domains, and file hashes with additional context and relationships.
- Threat hunting: Search across security data to identify suspicious behavior, hidden attacker activity, and emerging threats.
- Incident response: Recommend or execute response actions, such as blocking malicious infrastructure, isolating systems, or triggering workflows.
- Security operations automation: Automate repetitive SOC activities, investigation steps, and operational workflows.
- Detection improvement: Identify visibility gaps and assist with creating or improving detection rules and analytics.
What Are Key Characteristics of Cybersecurity AI Agents
- Autonomous: Performs tasks independently based on defined objectives, policies, and access permissions.
- Context-aware: Combines information from multiple sources, including logs, alerts, threat intelligence, asset data, identity information, and historical activity.
- Adaptive: Updates analysis and recommendations as new evidence, telemetry, or threat intelligence becomes available.
- Goal-driven: Works toward a specific security objective, such as identifying threats, reducing risk, or improving response efficiency.
- Tool-connected: Integrates with security technologies such as SIEM, SOAR, EDR, vulnerability management, case management, and threat intelligence platforms.
What is Native AI in SIEM and SOAR Platforms
Native AI in SIEM (Security Information and Event Management) and SOAR (Security Orchestration, Automation, and Response) platforms refers to AI capabilities built directly into security platforms to analyze data, support investigations, detect threats, and automate security workflows.
Unlike external AI assistants that require separate integrations, native AI operates within the existing security ecosystem and can directly leverage available telemetry, alerts, threat intelligence, assets, workflows, and investigation data.
Key capabilities:
- Built-in context: Uses data already available in the SIEM/SOAR platform, such as telemetry, alerts, assets, and threat intelligence
- Automation support: Helps execute repetitive investigation and response tasks faster
- Natural language interaction: Allows analysts to ask questions and receive security insights without complex queries
- Continuous improvement: Supports faster adaptation to evolving threats and changing environments
Frequently Asked Questions
What is required for effective cybersecurity AI agents?
The effectiveness of cybersecurity AI agents depends on the quality of their data, integrations, and operational controls. AI agents require accurate security telemetry, threat intelligence, and contextual information to make reliable decisions and understand attacker behavior.
When built on trusted data and secure integrations, AI agents can significantly improve security investigation, detection, and response.
What are the key considerations when using AI in cybersecurity?
AI systems are only as effective as the data they analyze. High-quality security telemetry, accurate threat intelligence, and rich contextual data are essential for reliable AI-driven detection, investigation, and response.
To understand evolving attacker behavior, AI systems need trusted intelligence that reflects real-world activity, including reconnaissance, exploitation trends, and changing attacker infrastructure. Threat intelligence providers such as ELLIO help enrich AI-powered security operations with high-confidence visibility into the external threat landscape, providing context on reconnaissance and mass exploitation activity.
Other key considerations include AI accuracy, explainability, integration with existing security tools, automation controls, and maintaining appropriate human oversight.
Who provides cybersecurity AI agents?
Cybersecurity AI agents are primarily delivered by major security platform providers such as Microsoft, Google, Palo Alto Networks, CrowdStrike, SentinelOne, Fortinet, Cisco, and IBM. These AI capabilities are embedded into SIEM, XDR, SOAR, and security operations platforms, using native telemetry, threat intelligence, and security workflows to support alert triage, investigation, threat hunting, detection engineering, and automated response.
A growing number of independent AI agent providers such as Dropzone AI, Radiant Security, and others are also developing AI-native SOC agents. These solutions are typically vendor-neutral and integrate with existing security tools to act as virtual analysts, helping with alert investigation, threat intelligence enrichment, incident analysis, and security workflow automation.