Holistic AI Governance Platform

Automatically discovers AI models, LLM API connections, ML pipelines, and AI-enabled SaaS applications in use across the organization, including those deployed without IT authorization.

Scores deployed AI models by risk level based on data sensitivity processed, deployment scope, capability classification, and applicable regulatory requirements.

Detects sensitive or regulated data in AI training, fine-tuning, or third-party LLM flows without appropriate controls, such as unencrypted PII in inputs or PHI sent to external APIs.

Tests LLMs and AI applications against a library of direct and indirect prompt-injection and jailbreak techniques, reporting which payloads bypass system instructions and safety controls.

Reports validated AI vulnerabilities with reproduction evidence, attacker context, and remediation guidance, mapped to the OWASP LLM Top 10, MITRE ATLAS, EU AI Act, and NIST AI RMF for auditable AI risk reporting.

Autonomously plans and executes multi-step adversarial campaigns against AI systems, emulating real attacker workflows across reconnaissance, exploitation, and escalation rather than running a fixed checklist of tests.

Re-runs red-team campaigns continuously and at release gates in the CI/CD pipeline as models, prompts, and configurations change, catching new exploit paths before and after deployment.

Classifies, assesses and mitigates AI-specific risks such as bias and robustness, with content libraries for regulations and frameworks including the EU AI Act, NIST AI RMF and ISO 42001.

Documents trust, risk and security assessments, testing and validation results, and remediation evidence for AI systems.

Maintains a centralized, discoverable registry of all AI use cases, applications, agents and models with metadata, ownership and deployment status.

Monitors and diagnoses the performance and behavior of AI models, applications and agents in production, including explainability.

Automates AI use-case intake, risk and security assessment, sign-off, attestation and approval workflows.

Enforces AI policies at runtime through guardrails, access controls and use-case validation, with remediation recommendations and compliance reporting.