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Challenge

Purdue Model OT Cybersecurity Auditor using Pydantic AI

Modern industrial architectures mix cloud analytics directly with operational networks, breaking traditional Purdue Model segmentation. Develop a type-safe cybersecurity compliance auditor using Pydantic AI and Taskade to validate IEC 62443 zone and conduit isolation standards across industrial network topology graphs.

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Challenge brief

What you are building

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

Create a type-safe Pydantic AI agent workflow to audit Purdue Model zone transitions and detect unauthorized Level 0 to Level 4 cross-boundary traffic.

Delivery guide

How work is evaluated

Evaluation

Validates that the Pydantic AI agent accurately identifies forbidden cross-zone traffic channels in complex OT network schemas.

Datasets

Shared data for this challenge

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

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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

violation_detection_accuracy

Verifies all Level 0/1 to Level 4 direct connections are flagged.

Binary check

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

Dimension 2

schema_conformance_rate

Percentage of generated responses matching Pydantic validation schema without errors. • target: 1 • range: 0.9-1

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

  • Define Pydantic schema constraints for Purdue Model Levels 0 through 5

  • Integrate Pydantic AI agents with Taskade structured execution tasks

  • Scan OT network routing tables and firewall configs for unauthorized conduits

  • Generate structured ISA/IEC 62443 compliance audit reports

Resources and assets

Reference links and supporting material

Dataset notes

Synthetic OT network topology definitions, router ACL tables, and Purdue Model zone assignment specifications.

How this agent runs

Validates that the Pydantic AI agent accurately identifies forbidden cross-zone traffic channels in complex OT network schemas.

Challenge input

JSON representing network conduits with source_level and destination_level.

Pydantic AI

Ensures output outputs strictly adhere to Pydantic data schemas.

Taskade

Provides structured execution tracking and visual workflow nodes.

Evaluated output

JSON output conforming to SecurityAuditResult Pydantic schema.

Checks for
  • Verifies all Level 0/1 to Level 4 direct connections are flagged.
  • Percentage of generated responses matching Pydantic validation schema without errors. • target: 1 • range:...
Proof of success
  • Benchmark: OT Security Zone Compliance Benchmark
  • Schema Conformance Rate target: 100%
  • 1 public reference case
Runtime evidence
  • Python execution harness
  • Python sandbox
View technical recipe

Configured tools

Action Space
  • Pydantic AI · Required
  • Taskade · Optional
Orchestration
  • Pydantic AI · Required

Evaluation contract

  • violation_detection_accuracy · Weight 1
  • schema_conformance_rate · Weight 1

Recipe state

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

Frequently Asked Questions about Purdue Model OT Cybersecurity Auditor using Pydantic AI