adversarial model testing
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.
continuous model behavior monitoring
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.
multi-platform llm threat detection
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.
automated vulnerability scanning
Systematically scans deployed LLMs for known vulnerability patterns, misconfigurations, and security gaps without requiring manual penetration testing or red-teaming expertise.
model failure mode identification
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.
security policy enforcement
Enforces consistent security policies across deployed LLMs, ensuring models comply with organizational security standards, regulatory requirements, and safety guidelines.
incident detection and alerting
Detects security incidents and anomalies in real-time, generating alerts and notifications when suspicious behavior or policy violations occur in deployed LLMs.
unified security dashboard
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.