By Lefteris Karavaras, SOC Manager of ADACOM
Summary: AI is reshaping how security operations teams detect, investigate, and respond to threats. But the same enthusiasm that drives adoption is generating dangerous blind spots. This article offers a sober look at where AI genuinely adds force — and where it introduces risk.
The Force Multiplier Narrative — and Its Limits
IBM’s 2025 Cost of a Data Breach Report found that average global costs dropped to USD 4.44 million, down from USD 4.88 million, or 9%, in the year prior. The catalyst? Faster breach containment driven by AI-powered defenses. According to the report, organizations were able to identify and contain a breach in a median time of 241 days, the lowest it’s been in 9 years.
It has become common to describe AI as a force multiplier for security operations. That framing is not wrong — but it is incomplete, and in the wrong hands it becomes a liability.
When applied to specific, well-scoped tasks such as correlating telemetry from thousands of endpoints, surfacing anomalous authentication patterns, or triaging a flood of low-fidelity alerts, AI delivers measurable, repeatable value. These are domains where human analysts are structurally disadvantaged: the data volumes are too high, the signal-to-noise ratio too low, and the time windows too narrow. Machine learning models operating at scale can detect what no human team could, simply because no human team has the bandwidth to look.
But the force multiplier narrative has a shadow side. Organizations that treat AI adoption as a security strategy in itself, rather than as a capability enabler within a coherent strategy, are not multiplying their security posture. They are multiplying their assumptions and their exposure.
The risk is not that AI will fail to detect threats. The risk is that it will detect something, flag it with high confidence, trigger an automated response, and that no human will have reviewed any part of that chain until after the damage is done or, equally dangerous, until after a legitimate business process has been disrupted.
Where AI Earns Its Place in the SOC
Honest practitioners draw a clear line between AI as a decision-support tool and AI as a decision-making system. The former is where the technology consistently performs. The three areas that stand out are triage, pattern recognition, and cross-source correlation.
Alert triage is perhaps the most immediate win. Modern SOC environments routinely generate volumes of alerts that no analyst team can process with full rigor. AI-driven triage, when trained on representative data and regularly validated against outcomes, can reliably separate signal from noise, surface the cases that demand human attention, and contextualize them with relevant threat intelligence before an analyst ever opens a ticket. That is not replacing analyst judgment. It is protecting analyst time.
Pattern recognition at scale is similarly well-suited to machine learning. Behavioral baselines, user and entity analytics, and anomaly detection across network flows are computationally intensive tasks, and AI can surface deviations that would be invisible to manual review. The nuance is that the model's output is a signal, not a verdict. It tells an analyst where to look, not what to conclude.
Cross-source correlation — linking events across endpoint, network, identity, and application layers — is where AI can genuinely compress investigation timelines. Connecting a suspicious process execution on a workstation to an anomalous outbound connection to a recent phishing lure in the email logs is the kind of multi-hop reasoning that takes an experienced analyst significant time to reconstruct manually. AI tools that can surface these chains accelerate the investigation phase in meaningful ways.
Analyst firm Gartner expects that by 2028, 50% of threat detection, investigation, and response platforms will incorporate agentic AI capabilities, up from less than 10% in 2024.
"AI tells the analyst where to look — not what to conclude. The moment that distinction collapses, so does your accountability model."
The Explainability Problem Is Not a Technicality
There is a governance dimension to AI in security that many organizations are underestimating. It surfaces most acutely around explainability.
- NIST's AI Risk Management Framework defines several characteristics of "trustworthy AI," explicitly listing explainability and interpretability alongside security, resilience, and accountability — positioning these not as aspirational goals but as foundational design requirements.
- Under Article 13 of the EU AI Act, high-risk AI systems must be accompanied by instructions that include clear information on the system's capabilities, limitations, and level of accuracy — specifically to enable deployers to understand and appropriately oversee AI-driven outputs.
When an AI model recommends isolating a device, blocking an account, or escalating an incident to a response playbook, someone in your organization becomes accountable for that decision, regardless of whether a human reviewed it. Your board, your regulators, and potentially your legal counsel will want to know: who authorized this action, on the basis of what evidence, and what oversight existed at the time of execution.
Black-box models that produce high-confidence scores with no interpretable reasoning chain are a governance problem masquerading as a technology advantage. If your security team cannot explain in plain language to a non-technical stakeholder why an AI system took a specific action, then your organization is making material security decisions without adequate oversight. That is a risk posture, not a capability.
CISOs who are deploying or evaluating AI tooling should be asking hard questions about explainability by design:
- Can the system produce an audit trail that maps each output to the inputs and logic that generated it?
- Is that trail legible to an analyst, or only to the model's vendor?
- What happens when the model is wrong, and the response has already been executed?
Guardrails Are Not Optional — They Are the Design
Gartner places both AI Assistants and AI SOC Agents at the Peak of Inflated Expectations in its 2025 Hype Cycle for Security Operations, noting explicitly that over-automation introduces risk when agents act on flawed assumptions, and that most use cases remain narrow and task-specific rather than end-to-end.
A separate Gartner evaluation framework for AI SOC agents found that while 70% of large SOCs are expected to pilot AI agents for Tier 1 and Tier 2 operations by 2028, only 15% will achieve measurable improvements without structured evaluation. This sobering finding underscores why deployment without defined guardrails is the rule, not the exception.
The organizations getting AI deployment right in security share a common characteristic: they treat controls and guardrails not as constraints on AI's potential, but as the architecture that makes AI safe to deploy at all.
Practically, this means defining in advance and in writing which decisions AI is permitted to act on autonomously, which require analyst review before execution, and which require senior authorization regardless of AI confidence scores. These thresholds should be informed by impact analysis: how reversible is the action, what the blast radius would be if the model is wrong, and what the regulatory implications of that error would be.
It also means building structured feedback loops into your AI deployment. Models that are not continuously validated against real-world outcomes will drift. Alert thresholds calibrated against last year's threat landscape may be misaligned with this year's adversary techniques. False-positive rates that were acceptable at initial deployment may be eroding analyst trust and, with it, the willingness to act on AI-generated signals. Regular model performance reviews, conducted by people with both security domain expertise and understanding of the underlying algorithms, are a governance obligation, not an engineering nicety.
Finally, and most importantly: human escalation paths must be genuine, not nominal. Too many organizations nominally preserve human-in-the-loop requirements while structurally ensuring that no human has the time, context, or authority to meaningfully intervene. If your escalation process is designed to be bypassed under operational pressure, it is not a control. It is a theater.
Augmentation as the Organizing Principle
The debate about whether AI will replace security analysts is, for most organizations at their current maturity level, a distraction. The more useful question is:
“What would it look like for your analysts to be genuinely better at their jobs because of AI, and what does your architecture need to look like to make that true?”
Augmentation, as opposed to automation, means that AI surfaces, contextualizes, and presents. Analysts decide, authorize, and own outcomes. The cognitive load shifts; the accountability does not. Analysts in an augmented SOC or ROC spend less time filtering noise and more time exercising the adversarial judgment that no current AI system can replicate: understanding attacker intent, assessing organizational context, and making defensible decisions under uncertainty.
This model also has a talent dimension. Security talent is scarce, and experienced analysts do not want to spend their days managing alert queues. Organizations that use AI to eliminate the most mechanical, repetitive elements of security operations are more likely to retain the analysts they have and to attract the ones they need. That is not a secondary benefit. In many environments, it is the primary business case.
A Realistic Posture for AI in Security
The organizations that will get the most out of AI in security over the next three to five years are not the ones moving fastest. They are the ones moving most deliberately, defining clear use cases, establishing governance before deployment, preserving human accountability at decision points, and continuously validating performance against evolving threat conditions.
AI as a force multiplier is a real phenomenon. But force without direction and without control is not a security posture; it is a liability. The goal is not to automate your SOC. The goal is to build a security operation that is smarter, faster, and more resilient than the one you have today. AI, properly governed and properly integrated, is one of the most powerful tools available for that purpose.
Ready for the Next Generation of Security Operations?
As cyber threats become faster, more adaptive, and increasingly AI-driven, organizations need security operations that can keep pace. ADACOM's Agentic SOC brings together AI-powered detection, intelligent automation, and experienced security analysts to reduce response times, cut through alert fatigue, and deliver rapid, informed action without sacrificing governance or human accountability.
If you're looking to build a SOC that is smarter, faster, and ready for the challenges ahead, ADACOM's Agentic SOC is built for exactly that mission.
For more information, contact us at https://www.adacom.com/contact-us