Vulnerability Discovery

Automate discovery and prioritization of software vulnerabilities using advanced AI models.

Business impact

• Vulnerability detection rate — Increases as AI uncovers more vulnerabilities faster than manual methods
• Time to remediation — Decreases due to prioritized and automated vulnerability triage and patching
• Security incident reduction — Fewer incidents occur as vulnerabilities are identified and fixed proactively
• Operational efficiency — Improves by automating repetitive discovery and triage tasks, freeing human experts
• Patch prioritization efficiency — Enhances focus on high-risk vulnerabilities through AI-driven risk contextualization

Data requirements

• Source code repositories (Text) — Analyze code to identify potential vulnerabilities and insecure patterns
• Security logs and telemetry (Structured) — Provide behavioral data to detect anomalous activities indicating vulnerabilities
• Bug and vulnerability databases (Structured) — Supply historical vulnerability patterns and severity ratings for training AI models
• Code review comments (Text) — Extract developer insights to guide fuzzing and vulnerability discovery

AI methods and techniques

• Predictive AI — Predicts potential vulnerabilities based on code patterns and historical data
• Generative AI — Generates exploit proofs and patch recommendations for discovered vulnerabilities
• Agentic AI — Autonomously explores code and simulates attacks to discover complex vulnerabilities

AI models and model families

Claude Mythos, GPT-5.4-Cyber, Claude Opus 4.6, Big Sleep, DepthFirst

Industries

Real-world evidence

7 documented case studies on record.

Companies using this: Google, Mozilla, Ripple, Team Atlanta Georgia Tech Samsung Research, The University Melbourne, Zscaler.