Cranium AI Security 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.

Maps data lineage and provenance across AI training and inference pipelines, tracing how PII, PHI, and IP move into models and external services.

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.

Assesses the identities and service accounts that AI models, pipelines, and agents use, flagging over-permissioned non-human identities and access paths that violate least privilege. Reports identity risk as a posture finding, distinct from enforcing access policies at the model API at runtime.

Discovers AI model and inference endpoints and flags public exposure, weak authentication, default credentials, or excessive permissions as posture misconfigurations.

Discovers and catalogs AI models, LLM API connections, ML pipelines, training datasets, and AI-enabled SaaS applications in use across the organization, including unauthorized deployments.

Exports AI system inventories in SPDX AI extension or CycloneDX ML profile format for downstream consumption by compliance tools, procurement workflows, and regulators.

Records the origin, training data sources, and modification history of AI models, supporting integrity verification and accountability for AI system behavior.

Generates documentation artifacts for EU AI Act conformity assessment and NIST AI RMF profile, mapping AI system inventory and controls to applicable requirements.

Discovers AI assets, including shadow models, agents, and inference endpoints, and maps the reachable attack surface to scope and target red-team campaigns. Offensive reconnaissance, distinct from posture inventory.

Routes high-value automated findings to specialist AI red teamers for manual exploitation, chaining, and depth beyond automated coverage, blending platform testing with human expertise.

Attacks deployed guardrails, system prompts, and content filters to measure how reliably they block adversarial inputs, quantifying bypass rates rather than assuming the controls work.

Attacks AI agents through their tools, memory, and connected services using multi-step techniques such as tool misuse, goal hijacking, and indirect injection, surfacing exploit paths unique to autonomous agents.

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.

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.

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.

Generates standardized documentation such as model cards and datasheets for auditors and regulators.

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.

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