Better AI, Not Less AI: The Case for Explainability in Security Operations

The alert fatigue crisis is still real 

Alert fatigue is often described as a state of operational and mental exhaustion caused by an overwhelming number of alerts, particularly when many are low-priority or false positives. This problem is only growing as organizations ingest data from an increasing number of security tools, each generating its own stream of alerts. Analysts are so bogged down by the sheer volume that they are becoming desensitized, allowing potential threats to pass through the cracks. AI was supposed to fix this. By implementing AI, organizations were able to filter alert volumes. However, traditional AI security tools often help address the problem, but they lack the ability to build trust with the user. The tools are unable to explain how they arrived at their conclusions, often relying on the analyst to blindly trust that the AI tool is making the correct decision. They are handed a verdict with no reasoning, no evidence trail and no way to know if the system got it right. What analysts need is insight into the decision-making process. 

What explainable AI looks like in practice 

Explainable AI (XAI) emerges as the answer to this problem. XAI shows the analyst why it made a decision not just what it decided. XAI removes the black box that traditional AI security tools have long operated behind. It can operate across triage, investigation, detection engineering and response with a consistent thread running through all of them: the analyst can always see how the AI tool came to the decision, not just the decision itself. 

At the triage layer, instead of just receiving a risk score with no context, analysts receive a reasoned argument. The AI not only explains why an alert was elevated, but also how confident it is in its assessment. This gives the analyst a clearer picture of how much weight they should give the recommendation. Gone are the days when AI tools simply issue verdicts, and instead, they present findings that analysts can interrogate, creating a tool they can now trust. The result is now a queue that moves faster, a workload that feels more manageable, and an analyst with the confidence that the decisions they made were informed rather than educated guesses made under pressure.  

At the investigation layer, XAI allows for investigations that build a case. Instead of manually investigating alerts or relying on a platform that auto-investigates without explanation, an explainable system lays the groundwork for investigation. It cites the sources that it drew from, maps alerts to attack chains, labels its reasoning and then presents its findings in plain language. This even applies when an XAI system closes a case autonomously. It doesn't just make the alert disappear it provides a report of why it reached that conclusion. When the XAI tool handles a case without direct analyst involvement, the reasoning is available for audit, learning and challenge, creating an autonomous investigation that builds trust. Because the AI tool shows its reasoning at every step, analysts can trace a false positive back to its source and identify when the issue isn't a genuine threat but a detection rule that needs to be tuned, stopping the same noisy alert from firing again and again. This allows for every investigation to become an opportunity to make the system smarter. What used to take analysts the better part of an hour now takes minutes. Especially across hundreds of alerts a day, that time compounds quickly into the kind of breathing room that makes alert fatigue feel like an operational challenge rather than an inevitable reality.

At the response layer, XAI extends beyond detection and investigation to include the actions the platform actually takes. When an AI tool recommends a response action such as isolating a host, revoking an identity, or blocking traffic, XAI doesn't just execute. Instead, it tells the analyst what it wants to do and why. Rather than assigning every recommendation the same weight and urgency, XAI provides the reasoning behind each suggestion, allowing analysts to evaluate the argument rather than blindly trust a directive. The result is a response layer where analysts are not just approving or rejecting AI recommendations, but evaluating the logic behind them and making more informed decisions as a result. When analysts can quickly understand the reasoning behind each recommendation, they can move through the response queue with greater ease and confidence in what they are acting on. 

Explainable AI as a tool 

When traditional AI security tools operate as black boxes, analysts develop a passive relationship with their tooling. The analysts aren't necessarily developing deeper threat-reasoning skills because the reasoning is inaccessible to them. XAI flips this dynamic. When an AI system explains why an alert was elevated, the explanation becomes a lesson. Instead of just closing the ticket, analysts can walk away with what that attack pattern looks like in real behavioral data, and they bring that with them into every investigation that follows. The AI almost becomes a mentor as much as a tool, allowing junior analysts to build threat intuition from day one rather than spending months developing it through trial and error. Senior analysts can then focus on more complex cases that genuinely require human judgment. An analyst who understands why threats behave the way they do can move through their queue with more confidence and less second-guessing. That shift in experience, multiplied across a team, is what alert fatigue looks like when it starts to get solved. The SOC gets smarter not in spite of AI, but because of how transparently it operates. 

Platforms leading the way in explainability 

The security industry is beginning to move away from black box AI, with several platforms now building explainability directly into how their tools operate. CrowdStrike's Falcon platform is one of the most explicit about this commitment, with every agent action governed by transparent, auditable logic and role-based controls that ensure AI always operates under analyst command. Palo Alto Networks' Cortex XSIAM takes a similar approach with its AgentiX functionality, which provides full auditability across autonomous SOC workflows. It is also trained on over a billion real-world playbook executions, grounding its reasoning in actual investigations. Fortinet's FortiAI and FortiAI-Assist are embedded across FortiSIEM, FortiSOAR, FortiNDR Cloud and FortiAnalyzer, providing analysts with plain language threat summaries and clear reasoning behind every recommendation, regardless of where they are working in the platform. 

The bottom line

The goal of implementing AI in the SOC was never to remove the analyst from the equation. It was to give them better tools, more time and better context to do the work that actually requires human judgment. Traditional AI tools in the SOC fell short of that promise because they were unable to build trust with their users. Explainable AI, in turn, closes that gap by promoting that trust through detailed reasoning. It provides the analysts with the confidence to act, the ability to push back and the context to keep getting better. In the new era of security operations, the analysts who will thrive are the ones who learn to work alongside AI by supervising it, challenging it and using its transparency as a foundation for sharper human judgment. 

If this got you thinking about your own SOC and where AI fits into it, WWT's Security Operations team is here to help.