AI Risk Under Control.
Identify AI-specific threats, detect anomalies, manage incidents, and mitigate risks before they become breaches.
AI systems fail in ways you have not imagined yet
Traditional risk frameworks were not built for AI. They do not account for autonomous systems that learn, adapt, and sometimes do things nobody expected.
Zentinelle brings control theory to AI risk. Treat your AI systems like the dynamic systems they are, with observability, controllability, and feedback loops that catch problems before they cascade.
- Observability — You cannot control what you cannot see. Full telemetry on every interaction, every decision, every data access.
- Controllability — Policy gates that enforce boundaries. Rate limits. Budget caps. Model restrictions. Tool permissions.
- Feedback loops — Anomaly detection that spots drift. Alerts that trigger before thresholds breach. Auto-remediation where safe.
This is not bolted-on security. It is systems engineering applied to AI.
AI-specific risks to manage
Risk Register
Catalog AI-specific risks. Assign owners. Track mitigation status. Map to controls. Know your exposure at all times.
Anomaly Detection
Baseline normal behavior. Detect deviations.
- Usage spikes (token consumption, API calls)
- Cost anomalies (unexpected spend patterns)
- Latency changes (model performance shifts)
- Error rate increases (failure patterns)
- Behavioral drift (output characteristic changes)
Incident Management
When policies are violated, Zentinelle creates incidents with full context: what happened, root cause analysis, remediation actions, SLA tracking, and post-incident review.
When things go wrong: real-time alerts fire on policy violations. Automatic responses block or quarantine. Full forensic records capture everything.