AI Security
Aurascape
Discovers and controls employee, embedded, and agentic AI use with real-time data protection.
Aurascape Overview
What it does
Aurascape is an AI Security Posture Management (AISPM) platform that gives enterprises real-time visibility, classification, and control over every AI interaction, spanning employee use of third-party tools, AI embedded inside trusted SaaS applications, and homegrown agents. Rather than relying on static allow-and-block lists, its engine decodes the prompt, response, user identity, and intent behind each interaction, so policy follows the conversation context instead of just the destination domain.
How it works
The platform continuously discovers AI applications across the long tail of tools released daily, scoring each by risk and cataloging shadow AI that traditional inventories miss. A multimodal data protection engine inspects text, code, voice, video, and generated files in line, fingerprinting sensitive data and enforcing intent-based controls in allow or block mode. For agents, a Zero Bypass MCP Gateway verifies every tool call, API invocation, and data retrieval, while pre-deployment adversarial testing simulates prompt injection and jailbreak attempts and runtime guardrails inspect agent activity against identity and data sensitivity.
Credentials and traction
Aurascape was named a Top 10 Finalist in the RSAC 2025 Innovation Sandbox contest, and it is included as a vendor in the AI Usage Control category of the 2025 Gartner Emerging Tech Impact Radar: AI Cybersecurity Ecosystem. Its customer base spans enterprises and regulated organizations, with named adopters including the University of Southern California, AC Transit, SiTime, and the Insurance Institute for Highway Safety, along with financial institutions such as SF Federal Credit Union and The Police Credit Union.
Key Capabilities
mapped to solution categoriesAutomatically 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.
Maps data lineage and provenance across AI training and inference pipelines, tracing how PII, PHI, and IP move into models and external services.
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.
Monitors AI-agent behavior at runtime to detect anomalous or malicious actions and policy violations.
Discovers and enforces least-privilege access for non-human and AI-agent identities across systems and data.
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.
Implementation & support
Info last updated on July 1, 2026
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