Robust Intelligence

Automatically generates and executes adversarial test cases against deployed LLMs to identify vulnerabilities, failure modes, and edge cases before they reach production. Tests cover prompt injection, jailbreaks, hallucinations, and other attack vectors.

Tracks deployed LLM behavior in real-time across production environments, detecting anomalies, drift, and emerging threats. Provides continuous visibility into model performance and safety metrics.

Unified threat detection engine that works across major LLM platforms (OpenAI, Anthropic, open-source models) with consistent security policies and detection rules. Eliminates need for platform-specific security tools.

Systematically scans deployed LLMs for known vulnerability patterns, misconfigurations, and security gaps without requiring manual penetration testing or red-teaming expertise.

Identifies and catalogs specific ways a deployed LLM can fail, including hallucinations, refusals, inconsistencies, and unsafe outputs. Creates a comprehensive failure mode inventory for risk assessment.

Enforces consistent security policies across deployed LLMs, ensuring models comply with organizational security standards, regulatory requirements, and safety guidelines.

Detects security incidents and anomalies in real-time, generating alerts and notifications when suspicious behavior or policy violations occur in deployed LLMs.

Provides a centralized dashboard for viewing security status, threats, and metrics across all deployed LLMs and platforms. Aggregates data from multiple sources into actionable insights.