Vertex Security Intelligence Suite
An AI-powered threat detection system achieving 99.9% accuracy across 2 billion daily security events.
The Security Scale Problem
At 2 billion events per day, no human team can review every alert. Traditional rule-based security systems at this scale generate so many false positives that alert fatigue becomes the primary security risk — analysts start ignoring alerts because 99% of them are noise.
Vertex Finance's security team was receiving 40,000 alerts per day and had the capacity to investigate 200. The math was not working in their favor.
Our Approach: Layered Intelligence
We built a four-layer architecture that progressively filters events from noise to signal:
Layer 1: Real-time stream processing. Apache Flink processes all 2 billion daily events in real time, applying 200+ deterministic rules to eliminate obvious non-threats. This filters to roughly 50 million events requiring further analysis.
Layer 2: Behavioral baseline models. Machine learning models establish normal behavior profiles for every entity in the environment — users, services, and machines. Events that deviate significantly from baseline are flagged.
Layer 3: Graph-based threat detection. Many sophisticated attacks involve coordinated activity across multiple entities. We built a graph model that identifies attack chains — sequences of events that individually look benign but collectively indicate a threat.
Layer 4: LLM-powered triage. High-priority alerts are summarized by a fine-tuned language model that provides human-readable context, severity assessment, and recommended response actions. This reduces analyst investigation time by 70%.
Model Development
Training a threat detection model requires ground truth data — labeled examples of real attacks. Vertex provided access to 3 years of historical security incidents and their outcomes.
We trained on 18 months of data, validated on 6 months, and tested on the remaining 6 months. The model's performance on the test set became our baseline for production deployment.
Continuous learning: the model is retrained weekly with newly confirmed incidents, adapting to Vertex's evolving threat landscape.
Results
- 99.9% threat detection accuracy on known attack patterns
- 94% reduction in false positive alerts (40,000 → 2,400 per day)
- 70% faster mean time to investigate
- Zero critical threats missed in 18 months of production operation
- $8M saved annually in reduced analyst overhead