Attack Surface Management

AI-powered continuous discovery and prioritization of cyber attack surface vulnerabilities.

Business impact

• Incident response time — Reduces time to detect and respond to security incidents through automation
• Security posture improvement — Enhances overall defense by continuously identifying and mitigating attack vectors
• Asset inventory accuracy — Maintains up-to-date and comprehensive asset visibility to reduce unknown exposures
• Operational efficiency — Automates manual security tasks, increasing team productivity and reducing errors
• Threat detection rate — Increases detection of external and internal threats using AI-powered analytics

Data requirements

• Network telemetry (Numeric) — Used to monitor traffic and detect anomalous activities indicating vulnerabilities
• Asset inventories (Structured) — Provide structured data on hardware and software assets for comprehensive visibility
• Vulnerability databases (Text) — Supply known threat intelligence to prioritize remediation efforts
• Cloud environment logs (Text) — Capture cloud asset configurations and changes for continuous monitoring
• External threat intelligence feeds (Text) — Inform on emerging external risks and attack vectors

AI methods and techniques

• Predictive AI — Forecasts potential attack paths and prioritizes vulnerabilities based on risk
• Generative AI — Generates insights on unknown exposures and simulates attack scenarios
• Agentic AI — Automates response actions and continuous scanning without human intervention

AI models and model families

GPT-4, Claude, Llama 2, Custom ML models for vulnerability detection

Industries

Real-world evidence

6 documented case studies on record.

Companies using this: Check Point Software, Circle K, Hadrian, Leading, Rapid7, Tenable.