Security operations centres are drowning in false positives. Industry data suggests that SOC analysts spend up to 25% of their time investigating alerts that turn out to be benign, a staggering waste of skilled human attention.
At SenseOn, we built the cross-domain correlation engine specifically to solve this problem. Rather than relying on a single detection methodology, the cross-domain correlation engine cross-validates every potential threat using three independent AI approaches.
The Three Pillars
1. Supervised Learning
Our supervised models are trained on labelled datasets of known attack patterns. They excel at detecting threats that match established signatures and behaviours, including variants of known malware families, common lateral movement patterns, and documented exploit techniques.
2. Unsupervised Learning
Unsupervised models identify anomalies without prior labelling. They build a baseline of normal behaviour for each environment and flag deviations. This catches novel threats that supervised models would miss: zero-day exploits, insider threats, and entirely new attack techniques.
3. Deep Learning
Our deep learning layer processes raw network telemetry and endpoint data to identify complex, multi-stage attack patterns. It operates on sequences of events rather than individual indicators, enabling detection of sophisticated adversaries who deliberately avoid triggering individual alert thresholds.
How Cross-Validation Works
When any single methodology flags a potential threat, the other two independently assess the same data. A genuine threat typically triggers signals across multiple methodologies:
- High confidence: All three methodologies agree: immediate escalation
- Medium confidence: Two of three agree: prioritised for analyst review
- Low confidence: Only one methodology flags: logged for pattern analysis
This cross-validation dramatically reduces false positives. In customer deployments, the cross-domain correlation engine typically reduces alert volume by 95% while maintaining or improving true positive detection rates.
Real-World Impact
One financial services customer reported that their SOC team went from investigating 500+ daily alerts to fewer than 25 high-fidelity cases, each backed by multi-methodology evidence. Investigation time per incident dropped from 45 minutes to under 10 minutes because analysts received pre-correlated evidence from all three AI approaches.
The cross-domain correlation engine is not just a technical architecture; it is an entirely different approach to threat detection that respects analyst time and delivers actionable intelligence instead of noise.
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