Develop a Cyber Data Breach Exposure Estimator Agent with Mastra AI Framework
Multi-million dollar cyber loss settlements like the recent $117.5M breach case require accurate loss modeling based on compromised record types. Build an autonomous breach estimation pipeline using Mastra AI in TypeScript with built-in state memory and RAG workflows. Achieve loss estimation within 15% of historical regulatory benchmark settlements.
What you are building
The core problem, expected build, and operating context for this challenge.
Construct a TypeScript Mastra AI workflow that uses local vector memory and dynamic tools to calculate cyber breach loss exposure.
How work is evaluated
Evaluates estimated cyber breach monetary reserve outputs against known benchmark settlement values.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
valid_confidence_interval
Confidence interval logically bounds the predicted estimate.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
mean_absolute_percentage_error
MAPE of total estimated monetary loss vs ground truth settlement • target: 0.15 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Implement autonomous agent workflows using Mastra AI framework.
Utilize Mastra's built-in memory and RAG capabilities.
Model cyber liability per record based on compromised PII/PHI categories.
Generate structured risk mitigation and loss reserve reports.
Reference links and supporting material
Dataset of 80 historical cyber data breach settlements with record counts and industry verticals.
How this agent runs
Evaluates estimated cyber breach monetary reserve outputs against known benchmark settlement values.
Challenge input
JSON containing record_count, record_types (PII, SSN, Health), and jurisdiction
Mastra AI
TypeScript framework with native memory and workflow orchestration.
Evaluated output
JSON with estimated_total_loss_usd, loss_per_record, and confidence_interval
- Confidence interval logically bounds the predicted estimate.
- MAPE of total estimated monetary loss vs ground truth settlement • target: 0.15 • range: 0-1
- Benchmark: CyberLossBench
- Mean Absolute Percentage Error target: 0.15
- 1 public reference case
- JavaScript execution harness
- javascript sandbox (unavailable on Versalist)
View technical recipe
Configured tools
- Mastra AI · Required
- Langbase · Optional
- IBM watsonx.ai · Optional
- Mastra AI · Required
Evaluation contract
- valid_confidence_interval · Weight 1
- mean_absolute_percentage_error · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.