Security Copilot
AI-powered security assistant automating threat triage and accelerating incident response workflows
Business impact
- decision accuracy — AI copilots improve triage accuracy by providing explainable recommendations
- incident response time — Automated workflows reduce time to resolve security incidents by up to 30%
- false positive rate — AI reduces false alerts by up to 70%, lowering noise and cognitive load
- analyst productivity — Automation frees analysts to focus on complex threats, increasing output
- security operations efficiency — Integrated AI agents streamline SOC workflows and improve operational metrics
- user trust — Explainable AI interfaces increase analyst confidence in AI-driven decisions
- risk exposure assessment time — AI accelerates risk evaluation, enabling faster prioritization of threats
Data requirements
- endpoint telemetry (Numeric) — Provides real-time device activity and threat signals for AI analysis
- network logs (Structured) — Captures traffic patterns and anomalies to detect lateral movement
- security alerts (Structured) — Aggregates SIEM and SOAR alerts for triage and prioritization
- threat intelligence feeds (Text) — Enriches AI models with global threat context and indicators
- user behavior analytics (Numeric) — Monitors identity and access patterns to identify suspicious activity
- security incident reports (Text) — Historical data used to train AI models for improved detection
AI methods and techniques
- Agentic AI — Autonomously performs triage and remediation tasks to reduce manual workload
- Predictive AI — Forecasts potential threats and attack paths for proactive defense
- Generative AI — Generates natural language rationales and explanations to improve analyst trust
AI models and model families
GPT-4, Microsoft Security Copilot LLM, Custom security-specific models
Industries
Real-world evidence
7 documented case studies on record.
Companies using this: City Johannesburg, EY, Hidden Layer, Ivanti, Palo Alto Networks, Security Bridge, Tanium.
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