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.
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.
How work is evaluated
Validates that the Pydantic AI agent accurately identifies forbidden cross-zone traffic channels in complex OT network schemas.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
violation_detection_accuracy
Verifies all Level 0/1 to Level 4 direct connections are flagged.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
schema_conformance_rate
Percentage of generated responses matching Pydantic validation schema without errors. • target: 1 • range: 0.9-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
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
Reference links and supporting material
Synthetic OT network topology definitions, router ACL tables, and Purdue Model zone assignment specifications.
Challenge setup
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.
- 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:...
- Benchmark: OT Security Zone Compliance Benchmark
- Schema Conformance Rate target: 100%
- 1 public reference case
- Python execution harness
- Python 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.
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Configured tools
- Pydantic AI · Required
- Taskade · Optional
- Pydantic AI · Required
Evaluation contract
- violation_detection_accuracy · Weight 1
- schema_conformance_rate · Weight 1
Metrics
- schema_conformance_rate · Target 1
Benchmarks
- OT Security Zone Compliance Benchmark
- security
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON representing network conduits with source_level and destination_level.
Output: JSON output conforming to SecurityAuditResult Pydantic schema.
Execution harness: Python
Evaluation tools
- Pydantic AI · Type-Safe Agent Framework
- Taskade · AI Workflow Automation
Recipe freeze marker
No recipe freeze time is recorded.