AI is increasing its prominence in cybersecurity, there’s no two ways about it. Dashboards flash alerts, confidence scores pop up in red. And somewhere in the background, a model is deciding which activity is “critical” and which is safe. It looks impressive, but most of the time, no one really knows why.
For experienced security teams, it’s an inconvenience and a dilemma masquerading as efficiency. When an alert lands in your inbox, you have two choices: trust the machine blindly and risk disrupting production, or ignore it and hope it’s a false alarm. Neither option is good. Complexity compounds in multi-cloud environments, and with regulators breathing down your neck, “trust me” is not a strategy. If you can’t explain it, you can’t defend it.
Black-box compared to glass-box AI
Most AI security tools still operate like black boxes. They produce risk scores without context, leaving analysts to make critical decisions based on incomplete information. A “critical” alert might mean anything from a harmless misconfiguration to a real breach.
When no one can see how the model arrived at that conclusion, the system becomes less of a tool and more of a guessing game. In high-stakes environments, that’s risky, and quite frankly indefensible.
Glass-box AI shows both the conclusion and the reasoning behind it. If a device is behaving unusually, the system highlights the exact signals, the queries it ran, and the specific data points that triggered the alert beyond the “high risk” label. Every decision leaves a traceable receipt that anyone on the team, or a regulator, can follow. Transparency turns AI from a black box into a tool you can trust.
For example, imagine a SOC analyst sees a “94% critical” alert for a user logging in from an unusual location. Without more context, they have no idea if it’s a legitimate employee on a business trip, a compromised account, or a misconfigured VPN. Acting on the alert could lock the user out of critical systems, while ignoring it could allow a breach to escalate. This uncertainty illustrates the dangers of relying on probabilistic metrics.
The challenge of fragmented data
Building verifiable AI isn’t easy. Most security systems are a patchwork of cloud logs, endpoints, identity services, and SaaS applications. They weren’t designed to speak a single language. When AI tries to analyze this fragmented data, it’s forced to guess across disconnected datasets and guessing doesn’t inspire confidence.
Verifiable AI also changes the role of the analyst. The aim is to not remove humans, it’s about giving them their time back. When an alert comes with evidence, investigations that once took hours can now be completed in minutes. Junior analysts gain context that would have taken years to build, while senior analysts can focus on strategy instead of chasing logs. Transparency makes humans smarter and not redundant.
One real-world example is multi-cloud telemetry. A security platform might receive endpoint logs from corporate laptops, activity logs from AWS, and identity logs from Azure AD. A user logged in from a new region and modifies a critical database. Without a unified model, the AI has to infer patterns across three disconnected datasets. This often leads to false positives or missed threats. The hard work is unifying that data so AI can reason consistently. Only then do alerts become actionable instead of just noise.
Keeping up with adversaries
The stakes are higher than ever. Attackers are moving fast, using automation and generative AI to test and exploit weaknesses at scale. Defenses need to move just as quickly, but speed without clarity is dangerous. Without explainable AI, organizations risk breaches and the inability to justify decisions when things go wrong.
Consider automated phishing attacks. “AI-powered attackers can send hundreds of phishing emails per minute, each exploiting weak credentials or misconfigured SaaS apps. Without explainable AI, defenders receive a flood of alerts with no insight into which ones are genuine threats, increasing alert fatigue and slowing response times.”
There’s an overall truth at the centre: AI isn’t valuable because it’s smart, or because it’s fast. It’s valuable when its decisions are understandable, traceable, and defensible. Security teams shouldn’t be asked to trust the machine blindly, they need evidence, context, and confidence. That’s how AI stops being a guessing game and starts being a tool you can rely on.
