The widespread adoption of artificial intelligence within security operations centers has paradoxically created a landscape where advanced tools often exist without a clear purpose or measurable impact. While the promise of AI-driven threat detection and automated response has captivated the cybersecurity industry, many organizations find themselves in possession of powerful technology that operates more like a high-maintenance curiosity than a strategic asset. The critical disconnect lies not in the technology itself, but in the absence of a deliberate, disciplined, and operationally integrated approach. Unlocking the true potential of AI requires a fundamental shift in mindset: from simply acquiring tools to meticulously engineering solutions that augment human expertise and strengthen established workflows.
Beyond the Hype Is Your SOCs AI a Powerful Asset or an Unpredictable Liability
A startling reality check emerges from recent industry analysis, revealing a significant gap between the presence of AI and its purposeful application. According to the 2025 SANS SOC Survey, a staggering 40 percent of Security Operations Centers use AI or machine learning tools without incorporating them into a defined operational plan. This means that for nearly half of these teams, AI exists in a state of informal experimentation rather than as a core, reliable component of their defense strategy. This ad-hoc usage creates an environment of unpredictability, where results are inconsistent, successes are not repeatable, and the technology can easily become a source of noise and distraction rather than a clear signal.
The common pitfall for many organizations is the failure to transition from this phase of casual exploration to one that delivers consistent, operational value. Individual analysts might use an AI tool to investigate a specific alert or generate a query, but without a formalized process, these efforts remain isolated. The insights gained are not scaled across the team, the methodologies are not standardized, and the overall security posture sees no lasting improvement. This informal approach turns a potentially powerful asset into a liability, consuming resources and providing a false sense of security while failing to contribute to the SOC’s core mission in a sustainable or measurable way.
The AI Paradox Confronting the Gap Between Adoption and Effective Integration
At the heart of the issue is a prevailing paradox: many SOCs possess sophisticated AI capabilities but lack the strategic framework to harness them effectively. This leads to a state of arrested development where the technology’s potential remains largely dormant. The tools are installed, the dashboards are active, but their outputs are not systematically integrated into decision-making processes, incident response playbooks, or threat hunting campaigns. The result is a collection of powerful but disconnected systems that fail to deliver a cohesive, cumulative benefit, leaving analysts to bridge the gap manually and leadership without a clear return on their significant investment.
This disconnect is further illuminated by another key finding from the SANS survey, which reports that 42 percent of SOCs rely on their AI tools straight “out of the box” with no customization. This plug-and-play mentality is fundamentally at odds with the nature of effective cybersecurity. Every organization has a unique digital environment, a distinct risk profile, and specific operational workflows. A generic AI model, untrained on the nuances of a particular network’s traffic patterns or business processes, cannot be expected to perform with high fidelity. Without careful tuning and validation against local data, these off-the-shelf solutions often generate a high volume of false positives or, more dangerously, fail to recognize novel threats that deviate from their generalized training data.
To overcome this challenge, a paradigm shift is necessary. AI should not be viewed as a panacea for flawed procedures or a substitute for a disciplined engineering culture. Its true power is realized when it serves as a force multiplier for mature, well-understood workflows. Rather than expecting AI to magically fix a broken alerting pipeline, the focus should be on applying it to specific, well-defined problems where its analytical capabilities can provide a clear and measurable advantage. This demands that AI implementation be treated with the same rigor as any other critical engineering project, complete with meticulous planning, continuous validation, and a clear understanding of both its capabilities and its limitations.
Five Strategic Arenas for High Impact AI Application in the SOC
In the realm of detection engineering, the objective is to build high-fidelity alerting for operational systems. A common misstep is applying AI broadly in the hope of resolving underlying deficiencies in an alerting pipeline. A far more effective strategy involves applying AI to a narrow, testable problem where its output can be continuously validated. A prime example is the use of a machine learning autoencoder to analyze DNS traffic. By training the model exclusively on the initial bytes of DNS packets, it learns the intricate patterns of “normal” traffic. When it encounters an anomaly—data that it cannot properly reconstruct—it generates a high-fidelity alert. This targeted application, focused on a specific protocol and a measurable outcome, transforms AI from a vague concept into a precise and valuable detection tool.
Threat hunting, at its core, is a research and development function within the SOC, designed to explore hypotheses and investigate weak signals that are not yet ready for production-level detection. Here, AI acts as a powerful accelerator for human-led discovery, not an autonomous hunter. Analysts can leverage AI to rapidly test a new analytical approach, compare complex data patterns across vast datasets, or validate whether a fledgling hypothesis warrants a deeper, more resource-intensive investigation. However, the human analyst remains indispensable for interpreting the operational context, determining the significance of the findings, and making the final judgment. It is also a critical Operational Security (OpSec) imperative to ensure that only authorized and relevant information is provided to AI models to prevent data leakage and maintain the integrity of the investigation.
Modern security analysts are increasingly becoming software developers, writing Python scripts, PowerShell tools, and complex SIEM queries to automate tasks and enhance their capabilities. AI can significantly augment this process by generating draft code and accelerating the construction of complex logic, providing a functional starting point much faster than manual coding. This utility, however, carries an inherent risk. Because AI models lack a true understanding of the operational environment or the security implications of the code they produce, the analyst must retain full responsibility for testing and comprehending every line. Deploying AI-generated code without rigorous human validation creates the potential for introducing subtle but critical flaws into the security infrastructure.
Within automation and orchestration, AI is reshaping how workflows are designed. Analysts can use natural language to describe a process, and AI can translate that description into the structured scaffolding of an automation runbook for a SOAR platform. It can propose the necessary steps, outline conditional logic, and format the output for seamless integration. Yet, AI cannot and should not answer the most critical question: when should an automated action be executed? The decision to act autonomously versus pausing for human review is a risk-based judgment that depends entirely on the organization’s risk tolerance. The authority to initiate a potentially impactful action must always reside with a person, ensuring that automation remains predictable, explainable, and aligned with business objectives.
Finally, one of the most persistent challenges for SOCs is translating technical data into clear business communication, a problem highlighted by the fact that 69 percent of teams still rely on manual reporting. AI offers an immediate and low-risk solution to this inefficiency. It can be used to standardize the structure of reports, transform raw technical notes into polished executive summaries, and ensure that all communications are consistent and comparable over time. This not only improves clarity and provides leadership with better visibility into security operations but also frees up significant analyst time, allowing them to focus on core defense tasks rather than administrative overhead.
The Foundational Mindset Treat AI Implementation as a Rigorous Engineering Effort
The true potential of AI is unlocked only when it is applied with precision to narrow, well-defined problems within the context of mature operational workflows. This expert perspective, championed by cybersecurity instructors like SANS’s Christopher Crowley, reframes AI adoption away from a simple procurement exercise and toward a disciplined engineering endeavor. Success is not found by chasing the most advanced algorithm but by identifying a specific, persistent operational bottleneck and methodically applying AI as a targeted solution. This approach ensures that the technology serves a clear purpose, its performance can be measured, and its value can be clearly articulated.
This engineering mindset necessitates a culture of continuous oversight and unwavering human accountability. AI systems are not static; models can drift as data patterns evolve, and their outputs must be constantly validated to ensure they remain accurate and relevant. Implementing clear review processes and feedback loops is critical to maintaining the integrity of AI-driven systems. There is no “set it and forget it” scenario in operational AI; it requires ongoing care and maintenance, with human experts always remaining in control, ready to intervene, retrain, or decommission a model that is no longer performing as expected.
Ultimately, the goal of integrating AI into the SOC must be redefined. The objective is not to replace human analysts but to augment their capabilities, improve the repeatability of their processes, and expand their overall capacity. By focusing AI on discrete tasks within established functions—such as refining detection logic, accelerating hypothesis testing, or standardizing reports—organizations can achieve tangible improvements in operational efficiency and effectiveness. This focus on augmenting capability ensures that AI becomes a supportive pillar of the SOC, empowering analysts to perform higher-value work and make faster, more informed decisions.
A Practical Framework for Intentional AI Adoption Are You a Taker Shaper or Maker
To move from haphazard experimentation to intentional implementation, SOCs can adopt a practical framework that defines their role in relation to AI technology. This involves categorizing AI utilization into three distinct archetypes: the Taker, the Shaper, and the Maker. A Taker implements a vendor’s out-of-the-box AI functionality without modification, relying on the provider for updates and maintenance. A Shaper takes existing AI tools and customizes or tunes them to fit their specific environmental needs and workflows. Finally, a Maker builds a bespoke AI or machine learning solution from the ground up to solve a unique, well-defined operational problem that off-the-shelf products cannot address.
A mature and strategically adept SOC will often embody all three roles simultaneously across different functions, applying the appropriate model based on the specific use case, available resources, and required level of customization. For instance, a team might act as a Taker for its SIEM vendor’s built-in, AI-driven correlation rules, a Shaper for its SOAR platform’s pre-built automation runbooks, and a Maker of a custom script for a highly specialized threat hunting task. By consciously deciding which role to play for each workflow, the organization establishes clear expectations for use, validation, and maintenance, ensuring that accountability remains firmly in place regardless of whether the solution was bought, adapted, or built.
This intentional approach provided a clear path for Security Operations Centers to move beyond the initial hype cycle. Teams that defined their roles and treated AI integration as a formal engineering discipline were the ones that successfully translated technological potential into operational reality. They understood that whether they were taking, shaping, or making a solution, the principles of rigorous validation, human oversight, and clear accountability were non-negotiable. The journey revealed that the greatest value was found not in the algorithm itself, but in the thoughtful and deliberate process of weaving it into the fabric of human-led security operations. The next frontier for these leading organizations shifted toward creating a culture of continuous AI governance, managing their models with the same lifecycle and diligence as any other piece of critical infrastructure, ensuring their long-term efficacy and resilience.
