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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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What you are building

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

How work is evaluated

Evaluation

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

Datasets

Public datasets, plus the private datasets you have access to.

How submissions are scored

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

  • 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

Dataset notes

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

Challenge setup

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

Evaluation checks
  • Ensures all direct Level 1 to Level 4 illegal connections are detected.
  • Recall metric for detecting true boundary violations. • target: 1 • range: 0-1
Configured evaluation evidence
  • Benchmark: OT Network Security Audit
  • Audit Recall target: 1
  • 1 public reference case
Configured runtime
  • 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

Action Space
  • CrewAI · Required
  • crewAI · Optional

Evaluation contract

  • zero_false_negatives · Weight 1
  • audit_recall · Weight 1

Metrics

  • audit_recall · Target 1

Benchmarks

  • OT Network Security Audit
  • security

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

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

    Output: JSON report detailing severe_violations, recommended_dmz_rules, and risk_score

    Execution harness: Python

Evaluation tools

  • CrewAI · framework

Recipe freeze marker

No recipe freeze time is recorded.

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