AI Agents in the SOC: From Hype to Reality

Cybercriminals constantly change tools, update infrastructure, and modify scripts to bypass defenses. At the same time, their basic behavior consistently reflects typical patterns described in the MITRE ATT&CK matrix: they still need to deploy C2, move within the network, and escalate privileges. Traditional generative models, lacking access to such detailed context, address these issues inefficiently. They operate like regular chatbots atop massive arrays of unstructured data, generating informational noise. Security team surveys show that specialists do not seek full automation; instead, they look for partial autonomy, where artificial intelligence undertakes the tedious routine of data gathering.

True efficiency of analytical agents is formed long before an expert formulates a query in natural language. The platform from Vectra AI was developed with a focus on specialized machine learning to detect violative behavior at the early stages of an attack. Collected telemetry passes through AI Triage algorithms and automatic prioritization based on real risks. Thus, an operator or external model, when accessing the system, receives an enriched, prioritized, and false-positive-free processed signal. This predictive stage ensures that artificial intelligence analyzes objective facts, not disparate log files.

For organizations ready to configure analytical processes on their own, developers have released a special open-source toolkit—Vectra AI SOC Agent Starter. This repository provides basic elements for the rapid deployment of an agent that integrates a chosen large language model (LLM), such as Claude, GPT, or Gemini, into the company infrastructure. The system’s connecting link is the Model Context Protocol (MCP) server, which provides the agent with secure access to platform data: risk assessments, network metadata, or traffic dumps (PCAP). Automation capabilities are expanded with specialized skills for alert sorting, reporting, and threat hunting. A special configuration file, AGENTS.md, coordinates the work of all tools, acting as a fundamental guide on what capabilities exist and when they should be applied.

The systematic use of agents significantly speeds up the response to new challenges to corporate security. For example, an operator can issue a simple command: “Here is the new CISA bulletin, search for all provided indicators in our environment.” Or, using the short phrase “vectra priorities,” a specialist initiates an instant analysis of the most risky object. Such queries allow quick hypothesis checks without the need to switch between dozens of systems or painstaking manual tab scanning exclusively through an information dashboard. This shifts the team’s focus to verifying investigation results, rather than mechanical evidence gathering from scratch.

For the implementation of artificial intelligence, two parallel approaches are available, which enterprises often use simultaneously depending on process maturity. The first path suits immediate results implementation and involves using the built-in capabilities of the Vectra AI platform without the need to create local infrastructure. Teams receive a ready ecosystem for query formation and conducting objective investigations. The second path is designed for mature security operations centers that require maximum adaptability. The Vectra AI SOC Agent Starter practice allows for the open-source code to be copied, trained to the company’s internal policy, and enhanced with a specific terminology dictionary and corporate architectural schemes, tailoring the agent exclusively to the team’s internal standards.

The implementation of artificial intelligence in security operation centers stops being a conceptual experiment and becomes a basic requirement for defense against modern information attacks. The use of a model of partial autonomy reduces the incident response time from several hours to a matter of minutes, maintaining transparency of actions and leaving the final strategic decision-making to security system experts. Organizations already using such platforms in their protection contour create a qualitatively new paradigm of operational trust and gain a multi-year advantage over modern cybercriminals.

iIT Distribution as a Value Added Distributor of Vectra AI’s intelligent solutions provides full expert support for the systematic integration of advanced analytical tools. The iIT Distribution team assists enterprises with architecture design, resource selection, and technical consultations at all stages of infrastructure development. This partnership allows clients to confidently scale security resources, ensuring high performance and seamless operation of the entire IT environment.