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Challenge

AI SDK Cyber Shield: Rapid Cyber Breach Insurance Loss Triage

Managing cyber breach claims requires fast log analysis and financial exposure estimation under strict SLA constraints. Build a Vercel AI SDK pipeline backed by Fireworks AI to parse security incident reports and output calibrated loss predictions with sub-second response latency.

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Challenge brief

What you are building

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

Develop a streaming TypeScript loss estimation pipeline using Vercel AI SDK and Fireworks AI fast inference runtime to quantify cyber breach exposure.

Delivery guide

How work is evaluated

Evaluation

Evaluates breach risk quantification accuracy and API response latency.

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

latency_check

Verifies that processing time is under 800ms.

Binary check

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

Dimension 2

loss_estimation_mape

Mean Absolute Percentage Error on estimated loss USD. • target: 0.12 • 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

  • Implement structured data streaming using Vercel AI SDK (ai/react or ai).

  • Integrate Fireworks AI ultra-fast inference API for log processing.

  • Extract breach scale factors (records compromised, exfiltration duration, ransomware strain).

  • Predict settlement loss bounds within a latency threshold under 800ms.

Resources and assets

Reference links and supporting material

Dataset notes

Synthetic dataset of 200 cyber incident reports with actual recorded insurance settlement outputs.

Challenge setup

Evaluates breach risk quantification accuracy and API response latency.

Challenge input

JSON incident summary containing records_compromised, system_downtime_hours, breach_type

AI SDK

Standard TypeScript framework for AI applications.

Fireworks AI

Provides low-latency LLM inference.

Evaluated output

JSON containing estimated_loss_usd, risk_category, confidence_score

Evaluation checks
  • Verifies that processing time is under 800ms.
  • Mean Absolute Percentage Error on estimated loss USD. • target: 0.12 • range: 0-1
Configured evaluation evidence
  • Benchmark: Cyber Loss Quantification Benchmark
  • Loss Estimation Mape target: 0.12
  • 1 public reference case
Configured runtime
  • JavaScript execution harness
  • javascript 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
  • Featherless AI · Optional
  • Command R+ · Optional
Policy Serving
  • Fireworks AI · Required
Training Infra
  • Fireworks AI · Required

Evaluation contract

  • latency_check · Weight 1
  • loss_estimation_mape · Weight 1

Metrics

  • loss_estimation_mape · Target 0.12

Benchmarks

  • Cyber Loss Quantification Benchmark
  • security

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON incident summary containing records_compromised, system_downtime_hours, breach_type

    Output: JSON containing estimated_loss_usd, risk_category, confidence_score

    Execution harness: JavaScript

Evaluation tools

  • AI SDK · Agent & Streaming Framework
  • Fireworks AI · Inference Acceleration

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about AI SDK Cyber Shield: Rapid Cyber Breach Insurance Loss Triage