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.
What you are building
Construct a tamper-proof parametric trigger verification system using Pydantic AI for cyber catastrophe bonds.
How work is evaluated
Evaluates outage telemetry signal validation and payout trigger calculation precision.
Datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
payout_range_check
Ensures payout_pct is between 0.0 and 1.0
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
verification_accuracy
Accuracy in validating true cloud outages versus transient false alarms • target: 0.98 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Learning goals
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
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.
- 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
- Benchmark: CyberCatBondBench
- Verification Accuracy target: 0.98
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
Shows the first task, up to three tools, and up to three evidence items.
View technical recipe
Configured tools
- Pydantic AI · Required
- Vast.ai · Optional
- Gemini 2.5 Flash · Optional
- 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
- 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.