Steam engines, electricity, airplanes, the Internet – what do these things have in common? Each invention was born out of a necessity to address some of humanity’s most tedious activities and pressing challenges. This cycle of creation has never stopped, and each time society faces an unprecedented problem, innovators rise to the occasion and develop new ways to overcome obstacles, streamline efforts, and defeat enemies.

While machine learning (ML) and AI began as tools to automate basic tasks, they’ve become the foundation of technological advancement; they can easily analyze endless amounts of data, identify patterns, and solve problems with exceptional speed and accuracy. At a time when businesses are under attack, not harnessing their power to the fullest extent leaves them vulnerable to nation-state threat actors and digital criminal enterprises.

Much like how automobiles replaced horse-drawn carriages and revolutionized transportation, AI-based Security Operations Centers (SOCs) are replacing traditional ones, transforming how SecOps teams operate and how organizations defend themselves against modern threats, including those launched by adversaries weaponizing AI.

The Limitations of Traditional SOCs

For years, traditional SOCs were the gold standard for monitoring, detecting, and responding to cyberattacks. However, as IT environments grow more complex and adversaries launch more sophisticated attacks, traditional SOCs fail to provide adequate protection. The factors making it particularly difficult for traditional SOCs are:

Expanding Attack Surfaces: Each month, enterprise attack surfaces add more than 300 new services, contributing to nearly one-third of new severe or critical vulnerabilities in cloud environments. Additionally, over 23% of vulnerabilities involve critical IT and security infrastructures accessible online, raising the chance of attacks.

Alert Fatigue: Every service and tool generates logs, telemetry, and traffic patterns, leading to a high volume of security alerts. A 2023 report found SOC teams spend 32% of the day on incidents that pose no threat because traditional SOCs struggle to filter out many false positives; this leads to alert fatigue and increases the risk of missing urgent threats.

Slow Response Times: Analysts spend nearly 3 hours daily manually triaging alerts, 83% of which are false positives. This waste of time inevitably results in delayed reactions to real threats.

Increased Attacks: Cybercriminals can access automated scanners, RaaS tools, and GhostGPT to craft phishing messages, automate social engineering, and generate undetectable malware. Unsurprisingly, 450,000 new malware variants are detected daily.

Lack of Human Resources: With 4.8 million cybersecurity jobs unfilled in 2024 and 25% of CISOs considering leaving, talent shortages and burnout strain security teams.

Rising Costs: As threats intensify, the need for more personnel and tools drives up expenses. Cybersecurity spending will hit $212 billion in 2025, up 15% from 2024, with SMBs investing $90 billion—a 58% jump from 2020. Yet, 60% of mid-sized businesses cite budget constraints as their biggest barrier.

In light of these, traditional SOCs are quickly becoming outdated; they need to be replaced with solutions that empower SecOps teams to work more efficiently and intelligently.

The Emergence of AI in Cybersecurity

AI and cybersecurity have been intertwined for decades, beginning with early contributors like Alan Turing. As computer use grew in the 1960s and 1970s, so did the discovery of new security threats; the 1971 Creeper virus demonstrated how vulnerable digital systems could be.

In the 1980s, expert systems attempted to mimic human decision-making by combining a knowledge base with logical rules to draw conclusions. They were also the early guardians of cybersecurity, capable of monitoring network traffic and behavior and comparing that data to known threat signatures to identify potential breaches. However, their static knowledge and strictly reactive approach meant they couldn’t learn or adapt to new risks.

In the early 2000s, widespread Internet adoption generated a data explosion. Meanwhile, modern ML, which learns from data, started analyzing patterns, detecting anomalies, and predicting threats, which let organizations anticipate and counteract attacks proactively rather than reactively.

The Rise of AI-based SOCs

The shift towards AI-based SOCs is a noteworthy yet natural turning point in cybersecurity. As more big data has become available for training, AI and ML models have improved substantially, enabling AI SOCs to address the many limitations of traditional SOCs. The rising cost of cyberattacks (expected to cost $12 trillion in 2025) and the skyrocketing frequency of attacks (averaging 1,876 weekly attacks per organization) have made it clear that organizations need to fight fire with fire, or, in this case, fight AI-generated attacks with AI-powered threat detection and response.

Components of an AI-Based SOC

AI-based SOCs provide comprehensive cybersecurity protection by combining several core components into an interconnected system that works harmoniously to defend an organization’s entire IT environment. These key elements include:

Security Information and Event Management (SIEM) or Security Data Lake (SDL)

A SIEM or SDL is the foundation of an AI-based SOC, serving as a central repository for data from all security and non-security log sources. AI enables the SIEM or SDL to ingest and parse diverse data in any language and schema, allowing for seamless integration and analysis of information and providing a holistic view of a business’s security posture.

AI-powered data correlation and anomaly detection analyze patterns to identify subtle deviations that could indicate a security threat. With AI monitoring and analyzing data in real-time, threats can be detected almost instantly, so the SOC can address them quickly and minimize damage. Additionally, AI-enriched threat intelligence integration provides deeper insights into emerging threats to help organizations proactively avoid potential attacks.

Security Orchestration, Automation, and Response (SOAR)

SOAR platforms, which can operate as standalone systems or be integrated with an SDL, use ML-driven incident response (IR) automation to streamline workflows by handling routine tasks automatically; this frees up SOC analysts to focus on more complicated issues. AI prioritizes incidents to ensure the most severe ones are addressed first and recommends remediation strategies so teams can respond effectively.

Analysts and AI Agents

AI-assisted threat hunting and investigation capabilities empower human analysts to identify and investigate threats with more precision. Large Language Models (LLMs) analyze security data, correlate information, and present digestible case summaries of threats and initial investigations so analysts can make informed decisions and take appropriate action quickly. Natural language agents (chatbots) support the investigation process by enabling analysts to query security data using natural language, receive instant insights, interact with complex datasets, and execute tasks like alert triage, containment, and remediation by interpreting natural language in security playbooks.

Cybersecurity is one of the fastest-evolving industries and needs solutions that keep pace with rapid changes. AI SOCs introduce components and technologies that easily overcome traditional SOC challenges and do much more. In the second part of this article, we’ll explore how AI SOCs and Managed Detection and Response (MDR) solutions bring transformative technical advancements and business value to organizations of all sizes and how to prepare for a future in which AI SOCs will become the leading paradigm.