See every prompt, every model, every dollar
Your developers send thousands of prompts per day to LLM APIs. You can't see any of them. Roval captures every request, flags PII and policy violations within 30 seconds, and shows you exactly what your agents cost.
Invisible traffic, uncontrolled cost, silent data leaks
Shadow AI breaches cost an average of $4.63 million per incident, $670K more than a standard data breach.
IBM Cost of a Data Breach 2025
Our first full-month invoice came in near $15k, the second $35k, and by month three it was touching $60k. Our annual API bill was projected to clear $700k.
What we input into LLMs can be just as sensitive as what they output. Prompts are not just casual instructions. They often carry personal, financial, or operational data.
Most teams discover their LLM is leaking sensitive context during a rushed proof of concept, not from a formal red team exercise.
From invisible traffic to full observability
Know what every agent sends to every model
Full prompt capture for every LLM API call. The proxy adds under 1ms of overhead and fails open, so it never breaks a developer's workflow. One environment variable to install. Running multi-agent pipelines? See AI agent orchestration governance.
See the LLM MonitorCatch PII before it leaves your network
Scan every prompt for PII patterns: emails, phone numbers, SSNs, credit card numbers. Flag violations before the request reaches the model API. Alert the security team within 30 seconds.
See policy enforcementSummarize the support history for customer sarah.chen@acmecorp.com. Their account number is 8842 and callback number is +1 (415) 555-0192. Escalate if unresolved.
Attribute every dollar to the agent that spent it
See which agents call which models, how many tokens they consume, and what they cost. Attribute LLM spend to teams, projects, and business units. Set budget alerts before costs spiral.
See the cost dashboardDetect threats in real time
Detect data exfiltration patterns, anomalous request volumes, prompt injection attempts, and model-switching attacks. Configurable threat rules with automatic alerts to Slack, email, or webhook.
See threat detectionFrameworks that require runtime monitoring
These frameworks require ongoing monitoring of AI system behavior, not just point-in-time assessments.
Article 9 requires continuous monitoring of high-risk AI systems including accuracy, robustness, and cybersecurity.
CC7.2 requires monitoring system components for anomalies and security events, including LLM traffic.
MEASURE function requires ongoing monitoring of AI system performance, fairness, and safety.
Clause 9.1 mandates monitoring, measurement, analysis, and evaluation of the AI management system.
Powered by Observer and LLM Monitor
Observer & LLM Monitor
The proxy that captures every LLM request, enforces policies in real time, and feeds every alert and cost metric shown above.
Explore ObserverDashboard
Total agents, compliance posture, active violations, and LLM spend in one view. Filter by team, framework, or risk tier.
Explore DashboardAgent Registry
Register every agent with its model, owner, and risk tier. Tie LLM traffic back to the agent that generated it.
Explore Agent RegistryStart monitoring your LLM traffic
One environment variable. Under five minutes to first insight.