According to the research, most organization are planning to reallocate Security Operations center(SOC) roles as a result of increased use of AI. Today organizations are under constant attack from cyber threats, with major data breaches making headlines almost every day. There are simply too many threats and too much data for security teams to manage manually. The survey says that 96% of security leaders have no plans to reduce the headcount in SOC teams amid growing utilization of AI. AI has recently launched the concept of SOC 3.0, which helps the SOC teams work smartly by reducing their workload.  Many organizations are developing plans to migrate Tier 1 SOC analysts to more senior Tier 2-3 roles.

What do Tier 1 SOC Analysts do?

Tier 1 SOC analyst are the first line of defence and their main job includes:

Tier1_SOC_Analyst

Why and how AI is  replacing  Tier 1 Analysts

  • Faster Threat detection:

In general, SOC, Tier 1 analysts do the threat detection, but with the help of AI, it becomes easier to detect them faster. AI can manage massive and suspicious activity faster compared to manual work. It spots threats the moment they occur and reduces response time.

  • Reduces False Positives:

Every day, traditional SOCs deal with thousands of alerts, many of them can be false positives. AI helps filter out and prioritize real threats, so analysts can focus on genuine threats.

  • Automated Incident Response:

Without requiring human input, AI-powered solutions may react to recognized risks automatically, such as banning malicious IP addresses or isolating infected systems or blocking suspicious activity. AI tools help to reduce response time and prevent attacks from spreading.

  • Threat Prediction:

AI can predict future attacks using machine learning by analyzing past threat patterns and behaviour. Because of this proactive defence strategy, SOCs are able to stay ahead of cybercriminals.

According to the Wipro State of Cybersecurity Report,93% of organizations now focus on AI-driven threat detection and response. AI is set to change how Security Operation Centers (SOCs) work, but experts say humans will still play an important role in handling cybersecurity roles and making sure AI systems are working properly. AI can handle complex tasks, but it still has limitations.

Recently, a generative AI has come to the market named SOC Co-Pilot, powered by Artificial Intelligence, so let’s understand what it is and how it helps SOC analysts to work smartly.

AI-Driven SOC Co-Pilots: The Smart Assistants

AI-driven SOC co-pilots are generative AI tools. This AI-driven SOC co-pilots will make a significant impact in 2025. It uses machine learning to help security analysts run and manage the SOC. The common co-pilot tasks include detecting threats, managing incidents and predicting new trends and patterns for attacks and breaches and automating response to threats.

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Co-pilot can analyze alerts and use AI to highlight the serious threats. This helps reduce false positives, which is a common problem in SOCs. As a result, analysts can focus on the most important alerts instead of wasting time on low-risk or false positives. With the help of Co-Pilot, SOC analysts have more time to respond to real threats and increase their chances of stopping them effectively. Analysts can use the Co-Pilot similarly to ChatGPT. We can assign the incident response, and the co-pilot analyzes data to suggest possible causes as well as how the organizations should respond to the incident.

 

Co-Pilot

Popular SOC Co-pilot tools

Although there are many SOC co-pilot tools available in the market, but Microsoft Security Copilot and IBM QRadar with Watson are now among most popular and widely used. These tools are completely transforming how security operations centers detect, analyze and respond to threats.

Let’s take a closer look at these AI-powered tools in more detail and why they are becoming so popular.

Microsoft Security Copilot is an AI-powered security assistant developed by Microsoft. It helps security analysts by summarizing incidents, logs and alerts quickly. It works with Microsoft Defender, Sentinel, and other Microsoft security tools.

It helps to make threat detection faster and decision-making smarter. You can think of it like a smart chatbot co-pilot for cybersecurity teams. Analysts can ask it questions in plain English, and it provides instant insights.

It is IBM’s AI-based tool that supports security analysts in investing threats. It helps in threat triage by suggesting next steps for the analyst. It helps to reduce manual work like reading logs and connecting indicators, and detects complex threats that humans might miss due to volume. It speeds up investigations with AI-generated attack summaries. To pull more relevant data, it integrates with existing security tools. It acts like a second brain for security teams, reducing response time. It automatically analyze suspicious activity in QRadar.

Benefits of using AI-driven SOC co-pilots

Most of the Companies are using AI-powered co-pilots in managing their security teams. These smart tools provide number of clear benefits.

  • Improved Productivity:

    AI is far more efficient as compared to humans; it can handle more alerts and tasks in less time, helping the security team work more efficiently. AI can handle hard jobs, so as result,fewer people are needed to monitor everything all the time.

  • More Time for Important Work:

    Checking logs and sorting alerts are handled by AI. This allows human analysts more time to focus on important tasks such as planning, analysing, and solving complex issues. Because meaningful work increases job satisfaction, it also helps to reduce burnout.

  • Fewer Mistakes:

    Sometimes people can make mistakes or overlook things, especially while performing repetitive tasks. On the other side, Ai is able to identify patterns and warning signs that people might overlook. It helps prevent attacks and security problems before they get worse.

  • Faster Reaction to Threats:

An AI co-pilot can react to a cyber threat instantly, while a human might take time to recognize and respond to cyber threats. AI doesn’t require a rest period or sleep; it is always on alert and ready to act. That means threats are handled much faster.

Difference between Traditional SOC vs. AI-Driven SOC

Features Traditional SOC AI-Driven SOC
Alert Monitoring Manual by Tier 1 Analysts Automated filtering by AI
False Positive High rate, requires manual filtering Greatly reduced using machine learning
Threat Detection Speed Slower, depends on analyst’s workload Fast real-time detection using AI algorithms
Incident Response Manual and time-consuming Quick and automatic responses to threats
Scalability Limited by team size Highly scalable, handles large volumes of data
Resource Allocation Analysts focus on low and high-priority alerts AI handles low-priority; analysts focus on critical
Operational Cost High due to manpower requirements Lower in the long run due to automation
Human role Critical at all levels Focus shifts to decision-making & strategic response

Conclusion:

Yes, adopting AI does have its own set of difficulties, such as integration, training, and expense, but the payoff is huge.

Companies that successfully apply AI will have a significant competitive advantage in the cybersecurity space, with faster responses. Integration of AI into a Security Operation Center is not just about using the latest tech; it goes beyond simply utilizing the newest technology.

Time-consuming processes like sorting through alerts, analyzing massive amounts of data, and spotting real threats more quickly may be automated with AI. This allows analysts to focus more on preventing actual attacks rather than wasting valuable time on false positives.