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Security Operations Centers are under growing pressure. IT environments are becoming more complex, while SOC teams rarely receive matching increases in resources. They must monitor more technologies, process more data, and assess more alerts in less time to determine what is a real threat and what is only noise. <\/p>\n\n\n\n
Over the past few years, the response has evolved from automation to AI assistants and now toward agentic AI. The question is no longer whether AI belongs in the SOC, but how it can deliver real operational value. <\/p>\n\n\n\n
Today\u2019s SOCs face several connected problems. The volume of data and events keeps growing, many organizations run more than 40 security and IT tools in parallel, alert overload and false positives remain common, cloud migration and automation create additional pressure, qualified experts are hard to find and retain, analysts burn out quickly, and key functions such as threat hunting or CTI processing are often missing.<\/p>\n\n\n\n
Attackers are also using AI more effectively, which increases the pressure on defenders to adopt more advanced capabilities. <\/p>\n\n\n\n
A major early step in SOC development was automation through SOAR systems. The goal was to automate parts of incident handling and, in some cases, response actions. <\/p>\n\n\n\n
In practice, this proved difficult to scale. Fully automated intervention is often not acceptable for business, operational, or risk reasons. On top of that, each incident type usually requires its own playbook to be created, maintained, and refined. <\/p>\n\n\n\n
As a result, SOAR has often become more of an analysis and process support tool than a fully autonomous incident management engine.<\/p>\n\n\n\n
The next phase introduced assistant-style AI. These tools let analysts ask questions in natural language, request summaries, gather information, and get support in interpreting a situation. <\/p>\n\n\n\n
This is useful, but the human still drives the process. The analyst must ask the right questions, interpret the answers, and continue the investigation. In complex cases, that still requires strong expertise. <\/p>\n\n\n\n
Agentic AI goes beyond question-and-answer support. It uses software agents built on large language models (LLMs) that incorporate domain expertise, methodology, and operational logic. <\/p>\n\n\n\n
Such a system can gather information, build context, identify connections, create a narrative, and recommend next steps. At the same time, the analyst remains in control and can pause the process, add context, or override conclusions. <\/p>\n\n\n\n
This human-in-the-loop model is essential. The goal is not to replace analysts, but to reduce repetitive and time-consuming work while keeping human validation and decision-making in place. <\/p>\n\n\n\n
A practical example of this approach is an AI Analyst-type solution that reviews SIEM-generated incidents and recommends whether further action is needed.<\/p>\n\n\n\n
Its strength is that it does not rely only on the alert itself. A meaningful investigation also needs to consider what happened on the affected host before and after the event, which user was linked to the activity, how the relevant process or service was launched, whether downloads, privilege escalation, or lateral movement occurred, and whether similar activity appeared elsewhere in the environment. <\/p>\n\n\n\n
AI Analyst follows the same logic. It uses the SIEM as a broader data source and can collect information from logs, network data, endpoint telemetry, and supporting internal or external context such as asset, identity, or CTI data. <\/p>\n\n\n