Each customer benefits from its own trained model per Vertical, enriched by powerful pretrained Transformers, Foundation Models and vetted corpuses specifically designed for cybersecurity intelligence and threat analysis.
Multi-layered deep learning models trained on millions of cybersecurity patterns and threat vectors
Lightning-fast analysis of security reports with sub-second response times for critical alerts
Industry-leading accuracy in threat classification and false positive elimination
Our AI engines leverage state-of-the-art transformer architectures combined with domain-specific cybersecurity knowledge bases. Each model is fine-tuned on your organization's unique security landscape and threat patterns.
Advanced attention mechanisms for contextual understanding
Cybersecurity ontologies and threat intelligence integration
Training data curated from trusted cybersecurity sources, including NIST frameworks, MITRE ATT&CK database, CVE repositories, and real-world incident reports to ensure accuracy and relevance.
The generative AI engines behind the Agentic SOC are built specifically for the volume and nuance of security telemetry, not adapted from a general-purpose chatbot. They exist to close a very specific gap: security teams generate far more reports, tickets, and log summaries than any human team can read carefully, and generic keyword search or rule-based triage cannot reliably tell which of those reports describe a real, escalating threat.
Instead of a single monolithic model, each customer deployment runs its own trained model per vertical, layered on top of shared, pretrained transformer foundations. That combination gives the accuracy of a model trained on an organization's actual data while retaining the broad cybersecurity knowledge captured in our shared foundation layer.
Incoming security reports, whether generated by a SIEM rule, an EDR agent, or a human analyst's notes, are parsed into a structured representation that captures the actor, the technique, the affected asset, and the timeline. The model then classifies the report against the MITRE ATT&CK framework, assigns a confidence score, and links it to any related open investigations.
Reports that describe genuinely novel behavior, patterns the model has not confidently seen before, are flagged for analyst review rather than silently auto-classified, which keeps false confidence out of the pipeline while still automating the bulk of routine triage.
Each vertical-specific model is fine-tuned on a corpus drawn from NIST frameworks, the MITRE ATT&CK knowledge base, CVE records, and the customer's own historical incident data, under strict data isolation between customers. This is described further in our methodology.
Retrieval-augmented generation is used to ground model outputs in the specific documents and playbooks relevant to a given alert, which keeps explanations traceable to source material rather than generated from the model's parametric memory alone.
A phishing campaign generates hundreds of near-duplicate alerts across mail gateways and endpoint tools. The engine collapses them into a single incident narrative, citing the shared infrastructure and payload similarities that tie them together.
An agentic AI workflow calls an internal API in a sequence the model has not seen associated with any known technique. Rather than guessing, the model surfaces it as an unclassified anomaly for human review.
Deployments generally see a sharp drop in the number of reports that require full manual triage, because a large share of routine classification is handled automatically with a documented confidence score. Detailed benchmarking methodology and evaluation criteria are available on our research page.
No. Each customer's vertical-specific model is trained and isolated separately; only the shared foundation layer, built from public frameworks and vetted corpuses, is common across deployments.
This engine is the classification layer underneath the broader Agentic SOC platform, and works alongside AgentGuard for real-time agent behavior enforcement.
Low-confidence classifications are routed to a human analyst with full supporting evidence rather than being auto-resolved, consistent with the approach described in our what is an Agentic SOC overview.
See how our cutting-edge GenAI engines transform your cybersecurity operations with intelligent automation and unprecedented accuracy.