AI Overload: Security Teams Investigate Just 37% of Daily Alerts

ALBUQUERQUE, N.M. – March 18, 2026 – Despite a significant push to integrate artificial intelligence into cybersecurity defenses, enterprise security teams are falling further behind in the face of a relentless barrage of digital threats. A new study reveals a stark paradox: even as AI adoption accelerates, organizations are only able to investigate a mere 37% of the thousands of security alerts they receive each day, leaving a vast and dangerous blind spot.

The report, "The State of SecOps AI in the SOC," commissioned by security platform Crogl and conducted by the independent Ponemon Institute, paints a troubling picture of modern Security Operations Centers (SOCs). Based on a survey of 649 IT and security practitioners, the research found that enterprises are inundated with an average of 4,330 security alerts daily. The inability to address nearly two-thirds of these potential threats comes as companies reported suffering an average of 16 cyberattacks in the past year.

The Limits of Automation and the Human Cost

For years, AI has been touted as the definitive solution to "alert fatigue"—the burnout experienced by security analysts forced to sift through a torrent of mostly benign notifications to find genuine threats. However, the data suggests current AI implementations are not the panacea many had hoped for.

While 62% of organizations have deployed AI in their security operations, confidence in its effectiveness is lukewarm. Only 44% of respondents believe their AI tools are "highly effective" in reducing threats. This disconnect highlights the persistent challenges of integrating complex AI systems into existing workflows, a barrier cited by 50% of practitioners. Another 49% pointed to the difficulty of normalizing dispersed data from various security tools as a key obstacle, preventing AI from having a complete and accurate picture of the environment it is supposed to protect.

The result is a high-pressure environment where technology has increased the volume of data without proportionally increasing the capacity to analyze it. This overwhelming flow contributes to analyst burnout and increases the likelihood that a critical alert—the precursor to a major data breach—will be lost in the noise. The report’s findings align with broader industry data, with a recent SANS Institute survey noting that 66% of SOC teams cannot keep pace with their alert volume.

"Security teams are under relentless operational pressure," Monzy Merza, CEO of Crogl, stated in the press release accompanying the report. "AI is emerging as a critical force multiplier inside the SOC, but the research makes clear that automation alone is not enough."

The study underscores that the most effective security postures combine technology with human expertise. In fact, 52% of respondents said human analysts remain "highly effective" as the final line of defense, even in AI-powered environments. This suggests the future is not about replacing humans with machines, but about finding a more effective way for them to collaborate.

Beyond the Hype: AI's Hidden Data Risks

As organizations rush to adopt AI, the report uncovers a growing and often overlooked area of concern: AI governance and data privacy. When security teams feed sensitive logs, network traffic data, and incident reports into third-party AI platforms, they may be exposing their organizations to new and insidious forms of risk.

A significant 61% of security leaders are "highly concerned" that their AI vendors may use their proprietary security data to enrich the vendors' own AI models, effectively learning from one customer's incidents to improve a service sold to others, including competitors. A similar number (59%) worry about the "derivative use" of their data, where insights gleaned from their information could be used in ways they never intended or approved.

Perhaps most alarming is the lack of visibility into this new threat vector. Only 36% of organizations believe they have a strong ability to detect whether their AI tools are introducing new, less visible forms of data leakage. This creates a scenario where the very tools meant to enhance security could become conduits for data exfiltration, a risk that is difficult to monitor and even harder to mitigate. This challenge is forcing industry leaders and regulatory bodies to develop new standards, such as the NIST AI Risk Management Framework, to help organizations govern AI systems and manage their unique risks.

The Rise of the Agents: A New Blueprint for the SOC?

In response to the shortcomings of traditional automation, the cybersecurity industry is beginning to pivot toward a new paradigm: agentic AI. Unlike earlier AI models that might classify an alert or suggest a response, agentic systems are designed to be autonomous actors. They can independently plan, make decisions, and execute multi-step tasks to achieve a goal, such as fully investigating a potential threat from detection to resolution with minimal human supervision.

This approach, championed by companies like Crogl with its "secure agentic platform" and integrated into major platforms from Google, CrowdStrike, and SentinelOne, aims to tackle the alert volume problem head-on. By automating the entire Tier-1 investigation workflow, these agents promise to act as a digital SOC analyst, handling the repetitive, high-volume tasks that currently consume human analysts' time.

The goal is to move beyond simple automation (like Security Orchestration, Automation, and Response, or SOAR) to true augmentation. Instead of an analyst manually reviewing an AI's findings and then deciding on the next action, an AI agent could, for example, detect a suspicious login, query threat intelligence databases, analyze the user's recent activity across multiple systems, and decide to quarantine the affected device, all while documenting its steps for human review.

Redefining the Analyst: Human Oversight in the AI Era

The emergence of more powerful and autonomous AI does not signal the end of the human security analyst. Instead, it is fundamentally reshaping the role and raising the bar for the skills required. The report’s findings show that AI is most beneficial when it frees up analyst bandwidth for higher-priority work (57% of respondents) and helps resolve alerts faster (67%).

This shift is transforming analysts from "alert janitors" into strategic investigators, proactive threat hunters, and, crucially, managers and auditors of AI systems. The day-to-day work is becoming less about following rigid playbooks and more about applying critical thinking, contextualizing AI-generated insights, and making high-level judgments that machines cannot.

To thrive in this new environment, analysts need a blend of classic cybersecurity fundamentals and new AI-centric skills. They must possess deep technical knowledge but also develop AI literacy—understanding how models work, where they might fail, and how to spot bias or drift. The most valuable professionals will be those who can not only use AI tools but also question, validate, and fine-tune them, ensuring that the speed of automation is always guided by the wisdom of human oversight. This human-machine partnership represents the next frontier in the ongoing battle to secure the digital landscape.