Every security vendor claims AI capabilities. The term has become so overloaded that it is difficult for security teams to separate genuine advances from marketing. Here is our honest assessment of where AI helps, where it falls short, and how we apply it at SenseOn.
Where AI delivers real value
Anomaly detection at scale. Humans cannot review millions of events per day. Machine learning models can establish baselines of normal behaviour for users, endpoints, and network flows, then flag deviations worth investigating. This is not new, but modern models are significantly better at reducing false positives.
Correlation across telemetry sources. When you combine signals from endpoints, network traffic, and cloud workloads, the volume of potential correlations is enormous. AI can identify patterns across these sources that would take analysts hours to piece together manually.
Triage and prioritisation. Not all alerts are equal. Models trained on historical incident data can score alerts by likely severity, helping analysts focus on what matters first.
Where AI falls short
Novel attack techniques. AI models learn from data. Truly novel techniques, the kind used in sophisticated targeted attacks, may not match any pattern the model has seen. Human judgement remains essential for detecting the unexpected.
Context and intent. A model can tell you that behaviour is unusual. It struggles to tell you why it is happening. An analyst who knows that the finance team is running year-end processes will interpret anomalous database queries differently than one who does not have that context.
Explainability. When a model flags something, analysts need to understand the reasoning to decide how to respond. Black-box detections that cannot be explained create trust problems.
How we approach AI at SenseOn
We use AI as an analyst augmentation layer, not a replacement. Our models handle the high-volume correlation and triage work, surfacing a smaller number of high-confidence findings for human review. Every detection includes the underlying evidence, so analysts can verify the reasoning and make informed decisions.
The goal is not to automate security analysts out of existence. It is to give them better signal and more time to do the investigative work that requires human expertise.
