Real-time behavioral monitoring for AI systems in production. Structured AI incident detection aligned to EO 14110, OMB M-24-10, and NIST AI RMF — with human-in-the-loop authorization before any report leaves your organization.
Post-deployment is where AI risk becomes real. Evidion monitors behavior, detects drift and incidents, and keeps a human in the decision loop before anything is escalated.
Continuous inference-level monitoring across all registered models. Detect anomalous outputs, boundary violations, and behavioral drift before they become incidents.
Automatic classification of detected events against NIST AI RMF and EO 14110 severity thresholds. Serious incidents, near-misses, and behavioral anomalies are classified and queued for review.
Zero automated submissions to regulatory bodies. Every queued incident report requires explicit human review and authorization before it is transmitted to any external authority.
Structured incident report generation aligned to EO 14110 Section 4 and OMB M-24-10 requirements. Reports are pre-formatted for federal oversight authority and internal compliance delivery.
Statistical monitoring for model output distribution shifts over time. Early warning before drift becomes a compliance event — gives teams time to retrain or restrict the model.
Every inference event, detection, review action, and submission decision is written to a tamper-evident log. The complete record is available for regulatory inspection or internal audit.
Evidion detects and classifies. It never decides whether an incident meets the threshold for external reporting. That judgment belongs to a qualified human reviewer.
Queued incidents sit in a pending state until an authorized reviewer approves, defers, or dismisses them. No timeout auto-submits. No system bypass.
Every review action — approve, defer, dismiss, modify — is logged with reviewer identity, timestamp, and rationale. The regulator can see not just what was reported, but who decided to report it and why.
Incidents that remain unreviewed beyond configurable thresholds escalate to secondary reviewers automatically. The system ensures nothing sits ignored — without ever automating the decision itself.
Requires developers of dual-use foundation models to report safety incidents and test results. Evidion detects, classifies, and structures these reports — with human authorization before any submission.
Requires federal agencies to conduct ongoing post-deployment monitoring of AI use cases and maintain documented behavioral logs. Evidion's immutable audit trail satisfies M-24-10 record-keeping requirements.
The MANAGE and MONITOR functions require ongoing risk tracking and structured incident response. Evidion operationalizes both functions with tooling aligned to the RMF playbook.
The first US state AI law imposes incident documentation and notification requirements on deployers of high-risk AI systems. Evidion's HITL review and audit trail satisfies Colorado SB 205 obligations.
Schedule a demo and see real-time monitoring and US-framework incident reporting in action.