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1. Artefact Model

Purpose

Overview of the management artefacts (Goal Definition, Hard Boundaries, Prompts, Validation Report and Traceability) that provide control over AI system behaviour.

1. Management Artefacts

To make AI systems governable, we manage specific artefacts that give control over behaviour.

Artefact Purpose Owner Format
Goal Definition Business hypothesis: Which outcome is being pursued? (Intent) AI Product Manager Structured statement ("Given X, when Y, then Z")
Hard Boundaries Hard limits: What must NEVER happen? (Constraints) Guardian (Ethicist) IF/THEN rules ("IF PII, THEN block")
Steering Instructions Steering: The configuration that steers the AI (prompts, knowledge coupling). ML Engineer Version-controlled config (e.g. YAML, JSON, Markdown or other structured formats)
Validation Report Evidence: Results of tests and measurements (Evidence). QA Engineer Structured report with metrics
Traceability Connection: Linking Goal → Instruction → Evidence. ML Engineer References (IDs / Git SHAs)

Steering Instructions encompass not only prompts, but all information and configurations that influence the system's behaviour, including linked knowledge sources, permitted actions, technical constraints, retention periods and rules for use and escalation.