Aurascape — agentic threat model

Not certain from the listing — likely uses proprietary or third-party LLMs to classify threats and analyze AI activity logs, exposing it to potential adversarial evasion or prompt injection designed to bypass detection.

Not certain from the listing — ingests telemetry, logs, and potentially sensitive IP from monitored AI applications. Risks include data exfiltration of monitored logs or poisoning of its threat detection database.

Not certain from the listing — orchestrates automated security workflows for AI activity. Vulnerabilities in its orchestration framework could lead to unauthorized tool execution or workflow bypass.

Not certain from the listing — likely deployed as a secure SaaS or enterprise virtual appliance. Infrastructure risks include unauthorized access to its centralized monitoring dashboard or API endpoints.

As an AI security and observability tool, its primary function is monitoring and threat prevention. Risks include blind spots in detecting novel AI-driven threats or evasion techniques that bypass its automated workflows.

Designed to enforce compliance and protect IP across enterprise AI use. However, if compromised, its broad access to security policies and compliance reports presents a high-value target for compliance evasion.

Monitors a vast ecosystem of external AI applications. It faces threats from compromised third-party agents sending malicious telemetry to disrupt its monitoring or trigger cascading automated workflow failures.