Why this matters
AI assistants are increasingly used to draft governance documents, generate RACI matrices, and analyze deployment scenarios against regulatory requirements. Without a grounding system instruction, the outputs can contradict the SRF — misassigning accountability, skipping layer analysis, or treating "shared" responsibility as a valid final answer when the framework requires a single accountable party.
A canonical system instruction solves this by making the framework's core rules non-negotiable in any AI-assisted governance workflow.
What's planned
Core system instruction (v2.0) — A production-ready prompt that enforces the SRF's fundamental rules: one accountable party per activity, L1-to-L5 responsibility cascade, mandatory autonomy level classification (L0–L5) for agentic systems, and explicit citation of framework sections.
Prompt variants — Shorter, role-specific versions optimized for executive summary, auditor (evidence-focused), developer (technical controls), and legal/procurement (contract language) contexts.
Scenario packs — Few-shot examples for the 10–15 most common governance scenarios: third-party model evaluation, incident post-mortem, autonomy classification, contract clause generation, and sector-specific deployments in healthcare, finance, and public sector.
Version registry — All prompts are versioned and pinned to a specific SRF release. The registry includes a testing harness that validates outputs against framework rules.
Timeline
Phase 1 (0–3 months from v2.0 launch): core system instruction and initial variant set published to the GitHub repository.