Skip to main content
Back to challenges
Challenge

Cyber Catastrophe Bond & ILS Telemetry Trigger Verification Engine

Cyber cat bonds require automated, tamper-proof telemetry validation to trigger bond payouts upon systemic outage events. Build a high-speed Python verification engine using Pydantic AI that validates global cloud uptime API signals and determines parametric catastrophe bond triggers.

Machine LearningHosted by Vera
Sign in free to start
Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Construct a tamper-proof parametric trigger verification system using Pydantic AI for cyber catastrophe bonds.

Delivery guide

How work is evaluated

Evaluation

Evaluates outage telemetry signal validation and payout trigger calculation precision.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

payout_range_check

Ensures payout_pct is between 0.0 and 1.0

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

verification_accuracy

Accuracy in validating true cloud outages versus transient false alarms • target: 0.98 • range: 0-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Process distributed cloud service uptime and global network outage telemetry logs

  • Build Pydantic AI validation agents ensuring signal authenticity and zero spoofing

  • Compute parametric payout percentages based on hours of continuous major cloud region outage

  • Output auditable catastrophe bond execution state reports

Resources and assets

Reference links and supporting material

Dataset notes

Cloud service status outage logs paired with cyber catastrophe bond prospectus terms.

Challenge setup

Evaluates outage telemetry signal validation and payout trigger calculation precision.

Challenge input

JSON containing service_provider, primary_region, continuous_outage_hours, and bond_attachment_hours.

Pydantic AI

Type-safe signal processing and result schemas

Evaluated output

JSON containing verified_outage boolean, trigger_activated boolean, and payout_pct.

Evaluation checks
  • Ensures payout_pct is between 0.0 and 1.0
  • Accuracy in validating true cloud outages versus transient false alarms • target: 0.98 • range: 0-1
Configured evaluation evidence
  • Benchmark: CyberCatBondBench
  • Verification Accuracy target: 0.98
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

Action Space
  • Pydantic AI · Required
  • Vast.ai · Optional
Policy Serving
  • Gemini 2.5 Flash · Optional
Orchestration
  • Pydantic AI · Required

Evaluation contract

  • payout_range_check · Weight 1
  • verification_accuracy · Weight 1

Metrics

  • verification_accuracy · Target 0.98

Benchmarks

  • CyberCatBondBench
  • security

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON containing service_provider, primary_region, continuous_outage_hours, and bond_attachment_hours.

    Output: JSON containing verified_outage boolean, trigger_activated boolean, and payout_pct.

    Execution harness: Python

Evaluation tools

  • Pydantic AI · Agent Framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Cyber Catastrophe Bond & ILS Telemetry Trigger Verification Engine