Cranium AI Security Platform
AI security and governance platform for shadow-AI discovery, agentic-AI visibility, AI red teaming, and compliance attestation.
Info last updated on May 23, 2026
Vendor Information
Cranium AI Security Platform Overview
Cranium provides enterprise AI security and governance solutions enabling organizations to secure, monitor, and govern AI systems across their entire AI supply chain. The platform combines AI Bill of Materials (BoM) generation, continuous threat monitoring, compliance automation, and red teaming capabilities to address shadow AI, adversarial threats, and regulatory requirements for EU AI Act, NIST AI RMF, and ISO 42001 compliance.
The platform features Cranium Arena, the industry's first AI red teaming platform that simulates automated and human-led cyberattacks against AI models with integrated MITRE ATLAS and OWASP threat libraries. Recent launches include AgentSensor for agentic AI visibility, CloudSensor for cloud security monitoring, ComplianceAgent for intelligent compliance automation, and Arena Shield that auto-generates remediation scripts. Detect AI and CodeSensor scan codebases and cloud infrastructure to identify shadow AI, reducing undocumented AI systems by up to 65% within six months according to IDC data.
Founded in 2023 as the first spinout from KPMG Studio, Cranium is based in Short Hills, New Jersey and has raised $32 million from Titanium Ventures, KPMG, and SYN Ventures. The company was named a Gartner Cool Vendor for AI Cybersecurity Governance (2025), recognized in Fortune's Top 50 Cybersecurity Companies, and featured in 11 Gartner Hype Cycle reports. Cranium serves financial services, healthcare, life sciences, and government organizations requiring stringent AI security and compliance.
Key Capabilities
Standardized capabilities mapped to this product's security niche
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.
Maps what data (including PII, PHI, and IP) flows into and out of AI models and APIs, identifying unintended exposure of sensitive data to external LLM services.
Detects adversarial inputs designed to override AI system instructions, extract training data, or manipulate model outputs. Detection approaches range from pattern matching to secondary model evaluation.
Produces a structured inventory of AI components, training data provenance, model versions, and dependencies in SPDX AI extension or CycloneDX ML profile format.
Maps the AI system inventory and associated controls to EU AI Act risk classification requirements and ISO 42001 AI management system controls.
Scores deployed AI models by risk level based on data sensitivity processed, deployment scope, capability classification, and applicable regulatory requirements.
Identifies AI systems processing sensitive or regulated data without appropriate controls: unencrypted PII in model inputs, PHI flowing to external APIs, IP in fine-tuning datasets.
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