Aurascape

Discovers and categorizes the organization's use of third-party AI, whether consumed as a service, installed locally, or embedded inside other applications, building a continuously updated inventory of AI usage including shadow AI.

Defines organizational AI usage policies and enforces them at the point of use - allowing, blocking, redirecting, or constraining specific AI services, models, and features per user, group, or data context.

Inspects prompts, uploads, and AI-generated responses for sensitive data across modalities, preventing exposure of regulated or proprietary information to third-party AI services.

Discovers the AI models, agents, MCP servers and AI-enabled applications in use across the organization, including those deployed without IT authorization, and keeps them in an inventory that records type, ownership and criticality. Discovery runs through traffic inspection and code repository scanning at minimum; some products add cloud provider, application server or AI engineering tool integrations. The inventory anchors exposure management and risk scoring.

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

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

Records prompts, completions, and metadata for all AI interactions with tamper-resistant storage, supporting compliance, forensics, and policy investigation.

Detects and blocks adversarial inputs designed to override system prompts, extract training data, or redirect model behavior. Detection approaches include pattern matching, input semantic analysis, and secondary model classification.