Scenario Analytics
Actuarial-grade risk modeling. For AI.
Monte Carlo simulation, loss triangles, Markov chains, survival analysis, and stress testing: the same quantitative tools insurers and risk managers use, purpose-built for AI risk.
The Suite
Every model a risk team needs.
Monte Carlo Simulation
Run 10,000 probabilistic scenarios across your defined time horizon. Get Expected Annual Loss, VaR at 95% and 99%, CVaR, and breach probability: real actuarial outputs, not estimates.
Monte Carlo Simulation — 10,000 Scenarios
Expected Annual Loss
$0
VaR (95%)
$0
CVaR (Tail Risk)
$0
Breach Probability
0.0%
Loss Distribution by Percentile
$0
P10
$0
P25
$0
P50
$0
P75
$0
P90
$0
P95
$0
P99
Mean
VaR 95%
Tail Risk
Scenario Factory
Describe a risk in plain language. The platform generates dozens of synthetic test scenarios, streams them in for review, and adds approved ones to your governance test suite.
Scenario Factory
Risk Description
AI must never offer refunds greater than $50 without manager approval
Markov Risk States
Model your AI fleet as a Markov chain transitioning between Low, Moderate, High, and Critical risk states. See steady-state probabilities and forward projections.
Markov Risk State Model
30-Day State History
Actuarial Loss Triangle
Standard actuarial loss development modeling adapted for AI incidents. Project ultimate losses from reported loss history with IBNR reserves.
Loss Development Triangle
0mo
3mo
6mo
9mo
12mo
Q1 2025
$45k
$52k
$54k
$55k
$56k
Q2 2025
$38k
$44k
$47k
$49k
—
Q3 2025
$42k
$49k
$52k
—
—
Q4 2025
$35k
$41k
—
—
—
Q1 2026
$28k
—
—
—
—
Survival Analysis
Kaplan-Meier curves and hazard function estimates for governance failures. Compare survival profiles across governors and policy configurations.
Governor Survival Analysis
PII Shield
Financial Guard
Content Safety
Time between governance failures (months) — higher is better
Stress Testing
Define extreme scenarios: major provider outage, mass PII incident, regulatory enforcement. Model the financial impact. Compare across multiple stress cases.
Stress Testing — Scenario Comparison
P=2.1%
Major Provider Outage
Primary AI provider offline for 72+ hours
Revenue Impact
$2.4M
Agents Affected
89%
Recovery Time
72hr
P=0.8%
Mass PII Incident
Simultaneous PII exposure across 5+ agents
Regulatory Fines
$1.2M
Records Exposed
45k
Legal Costs
$600k
P=4.3%
Coordinated Prompt Attack
Adversarial campaign exploiting agent chain
Financial Loss
$890k
Agents Compromised
3
Containment
4hr
Also Included
The full quantitative toolkit.
Poisson Forecast
Expected incident frequency by time period with confidence intervals. Inform capacity planning and insurance reserve calculations.
Behavioral Clusters
Cluster analysis of agent behavioral patterns. Identify risk-similar groups and surface outliers that warrant investigation.
SPC Dashboards
Statistical process control charts for governance metrics. Detect when processes go out of control with Shewhart, CUSUM, and EWMA charts.
ROI Attribution
Break down total liability shielded into contributing components. See which governors, policies, and violation types drive financial return.
FAQ
Risk analytics and scenario modeling, answered.
What is risk analytics and scenario modeling for AI?
Trinitite runs the same actuarial tools insurers use, Monte Carlo simulation, Markov risk states, actuarial loss triangles, survival analysis, and stress testing, against your real agent fleet. You get Expected Annual Loss, Value-at-Risk at the 95th and 99th percentiles, and breach probability instead of a survey rating.
What is a risk scenario tool?
A risk scenario tool lets you describe a risk in plain language and immediately see its financial shape. The Scenario Factory generates dozens of synthetic test scenarios, streams them in for review, and adds approved ones to your governance test suite; stress testing models extreme cases, a major provider outage, a mass PII incident, regulatory enforcement, and compares the dollar impact across them.
Is the modeling automated?
Yes. Every action your agents take feeds the engine, and the actuarial models run continuously rather than once a quarter. The live outputs drive the Agentic Risk Score in the broader Risk & Analytics module, so the numbers your CFO and CRO read are always current.
Ready when you are
Model the risk before it models you.
Thirty minutes against your real agent fleet. Leave with a Monte Carlo VaR model your CFO and CRO can both read.