Threat Intelligence Analysis

Automate threat data processing to prioritize and accelerate detection and response

Business impact

• Operational Efficiency — Automating data processing reduces manual workload and speeds up workflows
• Incident Response Time — Prioritized intelligence accelerates investigation and containment of threats
• Threat Detection Rate — Enhanced analysis improves identification of relevant and emerging threats

Data requirements

• Industry advisories and threat feeds (Text) — Provide raw threat intelligence data for automated ingestion and analysis
• Security logs and alerts (Structured) — Supply contextual data to correlate and validate threat indicators
• Historical incident reports and SOPs (Text) — Used to train AI agents for triage and playbook automation

AI methods and techniques

• Generative AI — Distills noisy data into concise summaries and generates detection rules
• Agentic AI — Automates IOC extraction and rule engineering within security platforms
• Predictive AI — Forecasts emerging threats based on historical and contextual data patterns

AI models and model families

Google Gemini for Government, GPT-4o, Claude, Llama

Industries

Real-world evidence

1 documented case study on record.

Companies using this: Google.