AI-driven evolution accelerates security operations amid rapid threat landscape shifts

Security Operations Centres are undergoing a revolutionary shift as artificial intelligence enhances detection, automation, and strategic planning in response to an increasingly complex and swift cyber threat landscape, with autonomous AI agents poised to redefine incident management.

Over recent years, Security Operations Centers (SOC) have evolved significantly from manual operations and isolated event analysis to systematic threat monitoring accompanied by active automation. Central to this transformation are artificial intelligence (AI)-based tools, which not only augment the capabilities of SOCs but also fundamentally alter their operational dynamics. This article explores the key trends shaping SOC development in the coming years, highlighting the shift in tools, processes, and strategic outlook.

The cybersecurity threat landscape has grown increasingly perilous for defenders. Attackers have developed tools that drastically reduce the time needed to execute attacks. According to the 2025 Unit 42 Global Incident Response Report, 19% of data breaches in 2024 involved data exfiltration within the first hour of system compromise. Reports from CrowdStrike also note a record-low average time of 48 minutes between initial access and lateral movement within networks, with the shortest recorded duration at just 51 seconds. This rapid pace underscores the urgent need for faster detection and response capabilities in cybersecurity operations.

Several factors contribute to this acceleration. First, AI is leveraged extensively to conduct phishing, create deepfakes, and carry out vishing attacks. Dark web forums now even offer “Deepfakes as a Service.” Large language models (LLMs) play a role in developing and enhancing malware, exemplified by the ransomware PromtLock created with AI support. Secondly, AI’s potential in automating vulnerability discovery is notable. Tools like the AI assistant Xbow, ranked first on the HackerOne platform, and sophisticated multi-agent systems like Hexstrike-AI, capable of autonomously scanning and exploiting vulnerabilities in under ten minutes, signify a new level of automated offensive capabilities.

Simultaneously, cybercriminals are employing more complex, multi-stage attacks and increasingly utilizing “Living-off-the-Land” (LOTL) tactics, which were observed in 49% of ransomware attacks. These attacks often shift to early morning hours, evading real-time detection. The trend towards destructive attacks targeting critical infrastructure is escalating, reflected in a 176% growth in incident investigation demand in 2023 and an additional 24% increase in the first three quarters of 2024 compared to the previous year.

Modern SOCs face daunting challenges, primarily balancing the minimisation of threat detection and response times against a backdrop of overwhelming data volumes and a persistent skills shortage. Data reveals that 40% of companies take over a month to initially detect threats, while only 21% achieve detection within the first day. The report by SANS highlights key difficulties including false positives (63.8%), data overload (62.5%), and lack of skilled personnel (58.8%). To address alert noise, 57% of SOCs suppress noisy rules, yet this leads to an average of 40% of alerts being uninvestigated. The cybersecurity workforce crisis exacerbates these issues; more than half of analysts leave within three years due to burnout, and recruitment can take two to six months, exacerbating coverage gaps and fatigue in SOC teams.

In response, the AI-driven SOC model is gaining prominence, integrating artificial intelligence throughout security operations, from data collection to incident response. Contemporary SOCs must monitor not only traditional assets but also cloud environments, containers, and SaaS services. Red Canary’s report notes a near 500% increase in cloud-account-related security alerts over the past year. Sysdig research adds that 60% of cloud containers last less than one minute, requiring near-real-time monitoring. Machine identities vastly outnumber human ones, by a factor of 80 on average, creating a vast new attack surface.

Vendors are incorporating AI to automate log source integration and data normalization. Platforms like Gurucul, Elastic Security, and MaxPatrol SIEM employ LLMs to speed and simplify configuration by auto-generating parsers and normalization rules. The continued explosion in data volumes, now reaching tens of terabytes daily, demands reconsideration of storage and analysis strategies. Long-term log retention, critical due to attackers sometimes maintaining undetected access for years, faces a solution in Data Lakehouse architectures. These combine Data Lakes’ flexibility with the speed of traditional data warehouses, enabling scalable, long-term, and AI-ready data environments adopted by companies such as Devo, Hunters, and Positive Technologies.

AI also revolutionises threat detection logic, the core of SOC impact. Behavioural analytics modules, including MaxPatrol BAD, Gurucul, and Devo, use machine learning algorithms to detect anomalies in user and system behaviour beyond the reach of rule-based detection. To combat false positives, specialised AI agents evaluate detection rule effectiveness, platforms like CardinalOps automatically identify coverage gaps in the MITRE ATT&CK framework and generate rule improvements. AI-driven detection engineering environments, such as Gurucul’s Detection Engineering Agents and CrowdStrike’s AI-powered Indicators of Attack, increasingly automate and refine detection logic creation.

AI copilots have become central to analyst workflows, reducing cognitive load and enabling experts to focus on complex investigations. Examples include Elastic AI Assistant, which explains alerts; Google’s Gemini AI in Chronicle, translating natural language requests into SIEM queries; and Exaforce Exabots, generating dashboards and reports on demand. This supports a shift from reactive analysis to proactive threat hunts and investigative efficiency.

The next phase in AI evolution is moving from assistants to autonomous agents capable of independently managing aspects of security operations. Multi-agent systems assign specialised tasks, data collection, analysis, triage, and response, to separate AI entities. Platforms like Intezer Autonomous SOC, MaxPatrol O2, and Radiant Security demonstrate how agents can automatically process alerts, gather context, and execute containment actions such as account lockdowns. The ultimate goal is virtual AI SOC analysts, such as those developed by Legion, which learn from human experts and autonomously handle complex incident investigations.

Proactive defence is essential given that 72% of attack paths to critical assets involve fewer than five steps. Attack Path Management tools, including PT Threat Modeling Engine in MaxPatrol Carbon, Microsoft Sentinel Graph, and Datadog Security Graph, use graph theory and Monte Carlo simulations to model infrastructure and identify the most probable attack routes. This targeted approach enables organisations to “fix less and secure more,” optimising defence efforts based on attack likelihood.

While AI affords SOCs unprecedented opportunity, it also introduces new challenges, such as managing AI model “hallucinations,” ensuring explainability, and securing AI systems themselves, which are increasingly targeted by attackers. Initially, generative AI mainly enhanced cyberattacks, but with agent AI capable of autonomous planning and execution, the threat landscape is shifting dramatically. This compels a reassessment of defensive strategies and proactive AI integration. The SOC Maturity Report 2025 indicates that 41% of companies plan to deploy AI for SOC automation within the next year, marking a transition from experimental to deliberate AI-driven SOC transformation, where artificial intelligence becomes a central architectural pillar rather than an optional add-on.

📌 Reference Map:

• ^[1] (Habr) – Paragraphs 1-11, 13-15
• ^[2] (Palo Alto Networks) – Paragraph 2
• ^[3] (CrowdStrike) – Paragraph 2
• ^[4] (SANS Institute) – Paragraphs 2, 4
• ^[5] (CrowdStrike) – Paragraph 2
• ^[6] (CrowdStrike) – Paragraph 3
• ^[7] (Unit 42 Report via CIO and Leader) – Paragraph 3

Source: Fuse Wire Services