Paige AI — agentic threat model

Built on proprietary foundation models trained on millions of histopathology slides. Primary threats include adversarial attacks (subtle slide modifications causing misclassification of cancer), model stealing of highly valuable IP, and training data poisoning.

Relies on massive datasets of digital pathology slides and clinical metadata. Key threats include data provenance gaps, unauthorized access to Protected Health Information (PHI), and poisoning of the training pipeline.

Not certain from the listing — the exact orchestration framework and tool-calling mechanisms for the 'co-pilot workflows' are not detailed, but insecure integration with digital pathology viewers or laboratory information systems (LIS) represents a major threat vector.

Not certain from the listing — deployment details (cloud SaaS vs. on-premise hospital network integration) are unspecified, though clinical-grade hosting requires strict sandboxing and network isolation to prevent lateral movement into hospital networks.

Not certain from the listing — while clinical-grade status implies rigorous validation, specific real-time observability, drift detection (e.g., adapting to different slide scanners), and guardrail frameworks are not detailed.

As a clinical-grade diagnostic tool, it operates under strict healthcare regulatory frameworks (such as FDA clearances, HIPAA, and GDPR). Compliance violations and unauthorized access to patient records are critical risks.

Not certain from the listing — multi-agent coordination or marketplace interactions are not explicitly mentioned, though integration into broader clinical ecosystems and diagnostic pipelines introduces cascading dependency risks.