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Challenge

Purdue Model Multi-Agent Enterprise OT/IT Architecture Team with CrewAI

Legacy OT networks violate Purdue Model segmentation, exposing critical SCADA zones to IT network breaches. Orchestrate a multi-agent team using CrewAI with defined roles (Network Auditor, Security Engineer, and Data Architect) to evaluate network traffic logs and enforce strict level-based data flow boundaries. Achieve zero policy violation false negatives across multi-zone telemetry logs.

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Challenge brief

What you are building

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

Deploy a CrewAI collaborative agent team to audit OT/IT network boundaries and enforce Purdue Model data segmentation guidelines.

Delivery guide

How work is evaluated

Evaluation

Evaluates CrewAI team output for accuracy in detecting illegal cross-level communications.

Datasets

Shared data for this challenge

Review public datasets and any private uploads tied to your build.

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

zero_false_negatives

Ensures all direct Level 1 to Level 4 illegal connections are detected.

Binary check

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

Dimension 2

audit_recall

Recall metric for detecting true boundary violations. • target: 1 • 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

  • Configure CrewAI Agents with distinct personas: Purdue Auditor, Firewall Architect, and Data Pipeline Lead

  • Analyze cross-level traffic (Purdue Level 0/1 to Level 4) for policy infractions

  • Formulate DMZ (Level 3.5) data relay recommendations for cloud telemetry push

Resources and assets

Reference links and supporting material

Dataset notes

Synthesized industrial netflow logs annotated with source/destination Purdue Model zones.

How this agent runs

Evaluates CrewAI team output for accuracy in detecting illegal cross-level communications.

Challenge input

JSON array of network flow tuples (src_ip, src_level, dst_ip, dst_level, protocol)

CrewAI

Role-playing multi-agent task collaboration framework

Evaluated output

JSON report detailing severe_violations, recommended_dmz_rules, and risk_score

Checks for
  • Ensures all direct Level 1 to Level 4 illegal connections are detected.
  • Recall metric for detecting true boundary violations. • target: 1 • range: 0-1
Proof of success
  • Benchmark: OT Network Security Audit
  • Audit Recall target: 1
  • 1 public reference case
Runtime evidence
  • Python execution harness
  • Python sandbox (unavailable on Versalist)
View technical recipe

Configured tools

Action Space
  • CrewAI · Required
  • crewAI · Optional

Evaluation contract

  • zero_false_negatives · Weight 1
  • audit_recall · Weight 1

Recipe state

This is a preview. The configuration can change before the evaluation recipe is locked.

Frequently Asked Questions about Purdue Model Multi-Agent Enterprise OT/IT Architecture Team with CrewAI