AI in SOC: A Revolutionary Opportunity or Just Another Technological Dead End?

The enthusiasm surrounding AI today is reminiscent in many ways of the dawn of the Industrial Revolution. It is marked by both excessive expectations and fear.

On one hand, some believe AI will make every job easier. On the other, others argue it will take away jobs, introduce unpredictable risks, and fundamentally reshape professions.

In cybersecurity, however—especially in the world of SOCs—we’re no longer talking about a theoretical question. The impact of AI is already measurable today. The real question is whether we’re using it well.

The new reality for SOCs: it’s not data that’s missing, but time

For a long time, the fundamental fear in security operations was that we weren’t seeing enough. We weren’t collecting enough logs, we couldn’t monitor every endpoint, and not every signal was coming in from the network, applications, or the cloud.

Today, this situation has reversed. Most advanced SOCs no longer struggle with a lack of visibility, but with a lack of time. The systems are signaling, the sensors are working, the events are coming in—but there simply isn’t enough human capacity to turn all of this into actual security decisions.

As a result, the SOC often does not function as an analytics center in the traditional sense, but rather as a continuous decision-making bottleneck: every alert requires someone to determine whether it is important or not. The problem is that the line in front of the bottleneck is getting longer and longer.

In day-to-day operations, this manifests in several ways:

• There are alerts that no one can respond to in time;
• There are rules that are disabled or simplified because they generate too much noise;
• There are events that seem insignificant on their own but could be part of a larger attack chain;
• And there are analysts who, at the end of the day, leave behind not closed cases but a backlog of work.

This is dangerous because time does not pass at the same pace on the attacker’s side. In a modern attack, penetration, reconnaissance, privilege escalation, and data exfiltration occur at an ever-increasing pace. If the defender is slow to interpret a signal, they will not be able to preempt the attack but will instead have to reconstruct what happened after the fact.

In the SOC, human resources are the most expensive resource

We talk a lot about the volume of alerts, but the real question is what effect this has on those who sit in front of their screens day in and day out. The work of a SOC analyst requires intense concentration, quick decision-making, and constant readiness. If this time is filled with repetitive, low-value validation tasks, it will eventually lead to professional burnout, and the organization may ultimately lose a specialist whose expertise cannot be replaced overnight.

This is a particularly sensitive issue because replacing a SOC analyst is a long and costly process:

• You need to find the right candidates;
• You need to go through a multi-stage professional selection process;
• You need to train the new colleague in technologies, processes, and client environments;
• Mentoring is required during the initial period;
• It can take months to achieve full independence.

Automation is important, but it is not enough on its own

For a long time, automation seemed like the obvious solution to the problem of overburdened SOCs. The introduction of SOAR systems has indeed led to progress in many areas.

For example, these:

• Repetitive processes can be organized into standardized playbooks;
• Certain routine tasks can be performed without human intervention;
• A more consistent incident management process can be established;
• The number of ad hoc decisions can be reduced.

However, experience shows that automation has not eliminated the problem; it has merely shifted it elsewhere.

Playbooks must be designed, developed, maintained, and continuously adapted to the changing environment. A complex SOAR project therefore requires significant resources, yet not every organization is capable or mature enough to derive real business value from it.

This does not mean that SOAR is a dead end. Well-designed automation remains valuable. But on its own, it does not solve the problem that the SOC must interpret, prioritize, and contextualize a massive volume of security events.

AI-washing: when everything is labeled “AI”

The emergence of AI in the cybersecurity market has brought not only technological change but also significant market noise. Today, virtually every vendor positions its product as an AI solution.

Several approaches are evident in the market:

• AI modules built into SIEM and XDR systems;
• SOAR platforms with AI enhancements;
• triage-focused solutions;
• copilot-style, chat-based assistants;
• standalone AI SOC platforms;
• agent-based AI security systems.

Therefore, the most important question is not “does it have AI,” but rather what exactly AI means in a given solution.

It matters whether a system uses simple machine learning-based anomaly detection, employs generative AI, offers agentic AI functionality, or is a multi-task SOC support platform built on a large language model.

Similarly, it matters whether the tool merely analyzes incoming alerts or is capable of reaching back to source systems, examining events in real time and context, and then providing a documented conclusion.

The real difference, therefore, lies not in the marketing materials, but in the depth of the functionality.

What should you look for when choosing an AI-based SOC solution?

When evaluating an AI SOC solution, there are several factors worth considering.

1. Operating Model

Are we talking about an on-premises, cloud, or hybrid solution?

The most powerful AI models are currently typically available in cloud environments, but this also raises data protection and compliance issues. It matters what kind of sensitive data leaves the corporate environment, how it is anonymized or hashed, and how the service provider can access it.

2. Coverage

A good AI SOC platform does more than just explain alerts. It creates real value by supporting multiple SOC processes, such as:

• triage;
• L2/L3 analysis;
• threat hunting;
• CTI processes;
• rule development;
• SOAR process support;
• incident documentation;
• reporting.

The broader the coverage, the greater the chance that AI will truly reduce the human workload.

3. Handling the full context

Many solutions rely solely on alerts. That alone is not enough.

A mature AI SOC solution must be capable of:

• referencing source systems;
• investigate events retroactively;
• correlate signals from different systems;
• recognize when a seemingly insignificant alert is part of a larger attack chain;
• provide documented, verifiable conclusions.

4. Data Protection and Compliance

Data privacy is a key consideration. When evaluating an AI SOC platform, the following must be clarified:

• what data is stored in the cloud;
• how sensitive data is protected;
• whether hashing, masking, or anonymization options are available;
• who has access to the data;
• how the solution aligns with corporate and regulatory requirements.

A response along the lines of “the cloud provider’s policy is fine” is not sufficient on its own. The details must be understood.

5. Transparency

In a SOC, it is not enough for AI to simply “say something.” We need to know:

• what data it used;
• what logical steps it followed;
• what evidence it based its conclusion on;
• where human validation is required;
• how the decision or recommendation can be audited.

AI is useful when it speeds up decision-making without making the process opaque.

AI will not completely replace humans

The biggest misconception is that AI means complete autonomy. Currently, there is no solution capable of fully replacing the entire operation of a SOC without human oversight and taking full responsibility.

The role of AI is much more to:

• reduce the manual workload;
• accelerate analysis;
• improve prioritization;
• support human decision-making;
• document and standardize analysis processes;
• help smaller teams achieve greater coverage.

This is particularly important from a management perspective. AI does not necessarily mean “fewer people are needed,” but rather that the same team can achieve more mature, faster, and more extensive security operations.

For a smaller organization, it can even enable the development of SOC capabilities that were previously only accessible with a large corporate budget.

SIEM isn’t going away—it’s becoming even more important

With the rise of AI-powered SOC solutions, many have written off traditional SIEM systems. However, market trends suggest the opposite.

Ultimately, some form of SIEM functionality emerges behind many new-generation AI-based security platforms, because the following capabilities remain essential:

• event collection;
• log normalization;
• searchability;
• correlation;
• historical analysis;
• rule-based detection;
• auditability.

AI, therefore, does not necessarily replace SIEM, but rather gives it new meaning.

The question is not whether to choose SIEM or AI, but whether we can use AI to draw conclusions from the data assets in SIEM more quickly, more accurately, and in a way that is more useful for the business.

What should I do now?

Organizations shouldn’t wait it out, but they shouldn’t jump in blindly either. Evaluating AI-based SOC solutions requires a well-defined set of testing criteria.

It’s advisable to launch pilot projects, measure results using real incidents and within your own environment, and not make decisions based solely on demos.

The most important questions:

• To what extent does the system reduce the analyst’s workload?
• Is it capable of examining events in their full context?
• How does it handle sensitive data?
• How transparent is its operation?
• What human validation points does it incorporate?
• What hidden costs should be expected?
• Does it integrate with existing SIEM, XDR, and SOAR environments?
• Is there a clear product vision and roadmap?
• Does it support real SOC tasks, or does it just provide a flashy interface?

Those who don’t get clear answers to these questions can easily fall victim to AI-washing. However, those who test thoroughly, build in appropriate controls, and align AI usage with business goals can gain a significant competitive advantage.

AI Analyst: analytical capabilities, immediately

As we saw in the article, the biggest challenge facing SOCs today is not a lack of data, but a lack of time and capacity. Alerts keep coming in, attackers are accelerating, and experienced analysts are becoming an increasingly valuable and hard-to-replace resource.

We built the AI Analyst approach to address this problem.

It doesn’t just give the SOC another dashboard; it speeds up the analyst’s work: it investigates the alert, pieces together the context, delivers an initial verdict, and then generates a full report for the human analyst.

This way, the analyst doesn’t have to start from scratch. They review a ready-made, structured investigation report and can focus on what truly requires an expert decision:

• whether the verdict provided by the AI holds up;
• whether further investigation is necessary;
• Is an incident response warranted;
• Or can the event be safely closed.

In practice, this means faster response times, less backlog, and more valuable human attention directed toward actual incidents.

The AI Analyst does not replace the SOC Analyst, but rather prepares the decision for them. Those who integrate this capability into their SOC operations in a timely manner will not only be faster but will also be able to provide a more scalable, stable, and competitive security service.