Artificial intelligence is certainly not new to the world of cybersecurity. For more than a decade, machine learning techniques have been used to detect anomalies, identify suspicious behavior, and support threat detection.
However, the evolution we have witnessed over the past two years is unprecedented: the arrival of generative AI and agentic systems is radically expanding the scope of activities that can be automated.
Sophisticated attacks and a lack of skills are driving automation
The growing sophistication of attacks, the exponential increase in the volume of alerts, and the chronic shortage of specialized skills are prompting companies and industry operators to rethink how Security Operations Centers (SOCs) operate, to automate an ever-greater share of incident detection, analysis, and response processes.
This is where the concept of the Autonomous SOC comes in: an operating model that promises to reduce manual workload, accelerate investigations, and improve the overall effectiveness of security operations. But how much substance is there behind this vision? Which activities can actually be automated, and which require human oversight? And, above all, does a fully autonomous SOC already exist?
In this article, we will answer all these questions.
Key Points
- AI is changing the balance between attack and defense. Attackers are leveraging automation and AI to increase the speed and volume of campaigns. Defense must evolve accordingly.
- The Autonomous SOC is emerging as a response to operational challenges: alert fatigue, skills shortages, the need to scale, and the need to reduce response times.
- Innovative approaches focus on ecosystems of specialized agents that collaborate with analysts to gain an advantage over malicious actors.
- A fully autonomous SOC does not yet exist. AI can automate many operational activities, but critical decisions require human expertise and accountability.
Why automation is becoming an integral part of the SOC
The idea of automating SOC activities is driven by several factors that are making traditional tools less effective than they once were and defense strategies less sustainable.
Asymmetric speed
One of the main factors is the asymmetric speed of attack and defense. Criminal organizations have industrialized many offensive activities, such as information gathering, content generation, and analysis of potential targets. Faced with threats that evolve and adapt at ever-increasing speed, defense must be able to respond at the same speed.
Alert fatigue
This is compounded by a growing problem of operational overload. Every day, Security Operations Centers have to manage thousands of alerts generated by the range of tools that make up the defense infrastructure. Separating real threats from background noise becomes a major challenge, and AI can be extremely useful in this respect.
Organizational scalability
For a Security Provider, as well as for large enterprises, increasing the volume of monitored activities cannot translate into linear staff growth. This is also because the market is short of specialized skills. Automation therefore becomes a necessary condition for managing more customers, more assets, and more data without proportionally increasing costs.
Consistent and repeatable processes
Automated processes also offer the advantage of applying consistent and repeatable analysis criteria. Unlike humans, who can be affected by fatigue and pressure, an automated system always performs the same checks with the same level of quality.
What is an Autonomous SOC: from alert management to full autonomy
A SOC brings together different processes, tools, and areas of expertise. Depending on the organization’s level of maturity, a SOC may include dedicated alert-monitoring staff, first- and second-level analysts responsible for investigating incidents, threat intelligence specialists, digital forensics experts, incident-response leads, and professionals responsible for coordinating activities with the business and management.
Each of these roles performs specific activities; however, not all of these activities require the same level of experience, expertise, or understanding of the business context.
From traditional automation to the agentic SOC
To achieve intelligent automation, the path being pursued leverages agentic AI. Instead of considering the SOC as a single block, the idea is to break its activities down into a series of more granular and specialized functions.
This approach offers two major advantages.
- Each component can be developed, trained, and improved independently, introducing new capabilities without having to redesign entire SOC workflows;
- It is possible to measure the performance of individual agents accurately, progressively increase their level of autonomy, or replace them when they fail to deliver the expected results.
Triage and alert management
Of all the activities performed by a SOC, alert triage is the one that lends itself most readily to automation. The reason is simple: operators have to manage thousands of alerts every day from different tools, many of which turn out to be false positives or events with very low systemic impact.
Rule-based automation has been used here for many years, but agentic AI takes this a step further. In addition to filtering events and applying predefined criteria, it can gather information from different sources, automatically enrich alerts with the context needed to interpret them, assign a risk level, and propose a preliminary classification or scoring for the analyst to review.
Many of these activities have ideal characteristics for automation: they are highly repetitive, require the analysis of large volumes of data, and are based on standardized procedures. At the same time, they leverage some of the key strengths of modern AI, such as the ability to synthesize information from heterogeneous sources, identify correlations, and quickly produce an initial assessment of the context.
Investigation and correlation
Once a suspicious event has been identified, the next step is to determine whether the alerts are part of a broader incident. This is a central phase that requires the analysis of numerous sources of information: logs, endpoints, cloud platforms, threat intelligence tools, and much more.
AI agents are proving effective precisely in their ability to navigate these sources, gathering data, correlating events, and building investigative hypotheses. They do not make decisions, at least for now, but they can certainly reduce the time needed to understand what is happening.
Incident response
The response phase is where the issue of trust in automated systems becomes most apparent. Here too, responses are not uniform: some actions can be carried out autonomously with relatively limited risk, such as isolating a potentially compromised endpoint or automatically opening a ticket.
However, when the consequences can have a significant impact on business or operational continuity, human involvement remains essential. The model is hybrid by definition, and will remain so for a long time: AI provides the information and can propose the action to be taken, but the final decision rests with the professional.
Reporting and documentation
Producing reports, event timelines, and technical documentation consumes a significant amount of professionals’ time. Generative artificial intelligence can intervene effectively and autonomously in this area, transforming complex technical data into readable summaries, reconstructing the sequence of events, and producing consistent and standardized documentation.
How to build a path toward intelligent automation
Organizations do not start out to build an autonomous SOC. The question is much more pragmatic: how can artificial intelligence be used to make existing security processes more effective and sustainable, often supported by established tools such as SIEM, EDR, threat intelligence platforms, and orchestration systems?
- The process always starts by identifying the bottlenecks. Which activities consume the most time? Which require specialized skills but generate little added value? Which skills is the organization lacking?
- The first automation initiatives always focus on standardized activities: alert classification, evidence gathering, preliminary event correlation, contextual information searches, and reporting. These areas can deliver tangible results in a short time, with a very high ROI.
- Only at this point does it make sense to assess more advanced levels of automation, to extend the level of autonomy of systems and enable them to formulate recommendations, propose corrective actions, and support investigative activities with an increasing degree of initiative.
- Throughout this process, governance must remain a key focus. As automation takes on a more significant role, it becomes essential to ensure decision transparency, action traceability, human oversight, and the ability to intervene in the event of unexpected behavior. For this reason, validation mechanisms, scoring associated with AI-generated recommendations, and procedures that allow operators to approve, modify, or cancel the actions suggested by the system are critical.
Rather than an abrupt transformation, the path toward the Autonomous SOC therefore appears to be a gradual process of technological and organizational evolution, not unlike what we are seeing in many other areas of business, from individual productivity to software development, from document management to customer service.
Kirey: preparing companies for today’s and tomorrow’s challenges
The evolution of threats leaves no room for inaction. For organizations, it is essential to adopt a defense strategy capable of evolving over time.
Thanks to our experience in cybersecurity and risk management, we support organizations in defining evolutionary paths aligned with the threat landscape, helping them identify the opportunities offered by AI and turn them into tangible advantages.
Contact us to find out how we can support you on this journey.
