CISO-ready security posture overview for board reporting.
Graph-based visualization mapping all AI agents to data sources, permissions, and external endpoints. Identifies toxic permission combinations and attack paths in real-time.
10,000-iteration probabilistic simulation quantifying financial risk exposure. Outputs Expected Annual Loss, VaR 95%, and VaR 99% for CFO-ready reporting.
Real-time policy engine with 8+ security rules. Automated Terraform remediation generation. One-click PR creation for GitOps workflows.
Expected Loss
$705K
VaR (95%)
$3.83M
VaR (99%)
$5.07M
Max Loss
$7.64M
Risk Assessment: Based on Monte Carlo simulation, your organization faces an expected annual loss of $705K from AI agent vulnerabilities. In a worst-case scenario (1% probability), losses could reach $5.07M.
"Our AI agent infrastructure currently has a security score of 35/100, driven primarily by 1 CRITICAL and 6 HIGH severity vulnerabilities. The Monte Carlo risk analysis indicates an expected annual loss of $705K, with potential exposure up to $5.07M in adverse scenarios (99th percentile). The primary risk vector is PII exfiltration through the billing-pii agent, which has unrestricted access to an external webhook. Remediation of identified vulnerabilities would reduce expected losses by an estimated 60-80%. Recommended immediate actions: (1) block external webhook access, (2) enable VNet integration, (3) implement least-privilege access controls."
Azure
10 agents
AWS
0 agents
GCP
0 agents
Oracle
0 agents