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.
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.
How work is evaluated
Evaluates breach risk quantification accuracy and API response latency.
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.
latency_check
Verifies that processing time is under 800ms.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
loss_estimation_mape
Mean Absolute Percentage Error on estimated loss USD. • target: 0.12 • 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 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.
Reference links and supporting material
Synthetic dataset of 200 cyber incident reports with actual recorded insurance settlement outputs.
How this agent runs
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
- Verifies that processing time is under 800ms.
- Mean Absolute Percentage Error on estimated loss USD. • target: 0.12 • range: 0-1
- Benchmark: Cyber Loss Quantification Benchmark
- Loss Estimation Mape target: 0.12
- 1 public reference case
- JavaScript execution harness
- javascript sandbox (unavailable on Versalist)
View technical recipe
Configured tools
- Featherless AI · Optional
- Command R+ · Optional
- Fireworks AI · Required
- Fireworks AI · Required
Evaluation contract
- latency_check · Weight 1
- loss_estimation_mape · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.