February 8, 2025
Artificial Intelligence (AI) is sweeping through cybersecurity. AI-driven SOCs, security copilots, and autonomous threat-hunting tools are being pitched as the next frontier in defense - faster, smarter, and more efficient than ever. The promise? AI that detects threats in real-time, automates responses, and even predicts attacks before they happen, and the industry is embracing AI in all its flavors.
But when the dust settles, one must ask a critical question - is AI alone enough? In our opinion, in security, AI should be a force multiplier, not a replacement for human expertise. The best cybersecurity isn’t AI-driven - it’s AI-enabled but human-expert led.
AI in cybersecurity isn’t a singular technology. It spans multiple approaches, each with its own strengths and trade-offs.
Traditional machine learning (ML)-based AI has been around for a while, powering SIEMs, Endpoint Detection and Response (EDR), and behavioural analytics. These models excel at spotting known attack patterns but struggle with novel threats. More recently, Generative AI (Gen-AI) - using large language models (LLMs) - has surged in popularity, providing automated threat intelligence analysis, alert summarization, and decision-making assistance. And now, Agentic AI - the concept of fully autonomous, self-executing security, is being hyped as the next revolution in defense. Some claim it’s the future of cybersecurity, replacing human intervention entirely. But how much control should we really be handing over?
Each of these technologies contribute to security operations, but they also introduce major challenges. AI-driven SOCs claim to reduce human workload, but do they truly eliminate the need for expert judgment? Security copilots promise to assist analysts, but can they be trusted with critical decisions? And as agentic AI pushes the boundaries of automation, can we afford to let machines take full control?
Let’s break them down:
The idea of an AI-powered SOC sounds like a dream: automated threat detection, rapid investigation, and seamless response - all without human intervention. But reality is more complex. AI models are only as good as the data they are trained on, and most SOC AI solutions rely on historical attack patterns. That means they struggle with zero-days, novel attack techniques, and adversaries who deliberately manipulate AI models.
AI-SOCs generate overwhelming alert volumes, often full of false positives. Without human oversight, analysts either get buried in noise or start tuning out alerts altogether.There’s also the problem of context - AI doesn’t inherently understand business-critical assets. It treats all alerts with the same weight, even when a minor anomaly in a core financial system is far more critical than a flagged event on a non-essential server.
But here’s the bigger problem - attackers aren’t just dodging AI security; they’re actively manipulating it.Through adversarial machine learning techniques, threat actors can poison AI models, feeding them misleading data that causes them to misclassify threats as benign. The end result? AI-SOCs, while useful for efficiency, are unreliable if they are left to operate without human validation.
Microsoft Security Copilot, Google’s AI-driven threat intelligence, and other similar solutions provide real-time insights, summarization, and response recommendations. These are incredibly useful, but still fall into major traps:
Over-Reliance on training data – security copilots lack real-time adaptive intelligence. Their models are trained on static datasets, making them great at summarization but weak at novel techniques and emerging threats.
Misinformation Risks - Generative AI hallucinates. Generative AI can produce highly plausible but incorrect responses - a phenomenon often referred to as “hallucination.” In cybersecurity, this could mean misclassifying an active attack as a false positive or fabricating an alert based on misleading patterns. AI itself can present a security risk - many AI copilots share data with external cloud-based models, creating new security concerns (data leakage, compliance risks).
Co-pilots are useful assistants, but they don’t replace the need for human expertise.
Agentic AI is the next evolution - AI that not only assists but executes actions autonomously. Think AI-driven SOAR (Security Orchestration, Automation, and Response) that investigates, patches vulnerabilities, and quarantines assets without human input. Sounds ideal, right? Here’s the problem:
Agentic AI may promise full Automation, but security decisions require accountability - something today only humans can provide.
At ThreatLight, we take a different approach. We embrace our AI - it plays a crucial role in our operations - it augments, accelerates, and assists security teams, but doesn’t replace expert judgment, intuition, and adaptability. That’s why our model blends AI-driven efficiency with human-led security operations:
We believe that security should never be fully automated because cybersecurity isn’t about speed alone, it’s about strategy, adaptability, and understanding the evolving nature of threats.
We harness AI to reduce noise, accelerate response, and provide critical insights, but always keep the human in the loop.
The rush toward AI-driven security solutions is both exciting and dangerous. AI has the potential to revolutionize threat detection, response, and overall security posture, but only if used correctly. The industry must resist the temptation to treat AI as a replacement for human expertise. AI is not an infallible security analyst, and it never will be (well, at least not in the foreseeable future).
At its core, Cybersecurity is a human problem first and a technical challenge second.
The best security isn’t AI-driven - it’s AI-enabled, but human-expert led.
Artificial Intelligence (AI) is sweeping through cybersecurity. AI-driven SOCs, security copilots, and autonomous threat-hunting tools are being pitched as the next frontier in defense - faster, smarter, and more efficient than ever.
A Battle CISO knows that no playbook survives first contact with an attacker. They focus on adaptability and flexibility, knowing that these qualities – not theoretical readiness – are what carry teams through a crisis.
