BasicAgent

AI Governance: Open-Source Hosting + System Prompt Control

Practical AI governance notes on hosting open-source models, enforcing prompt structure, and building evidence for high-risk workflows.

This page is a practical governance hub focused on high-risk workflows. The emphasis is on hosting open-source models safely and locking system prompts into a tight, auditable structure.

What this covers

Open-source model hosting controls (supply chain, isolation, evidence)
System prompt structure and change control
Auditability for high-risk deployments (government, regulated, or sensitive data)

Open-source model hosting (high-risk baseline)

If you host open-source models, you own the full trust chain. A minimal baseline includes:

Model provenance: hashes, version pinning, and reproducible weights.
Isolated inference: sandboxed runtimes, locked egress, tight filesystem boundaries.
Data handling: redact inputs, segment sensitive data, enforce retention.
Change control: signed releases, human approvals for model/prompt updates.
Continuous evaluation: targeted tests for safety, leakage, and regressions.

System prompt control (tight structure)

High-risk workflows require prompts that are versioned, reviewed, and locked:

Prompt registry with strict ownership and version history.
Structured output contracts to minimize ambiguity.
Diff review for any prompt change before it reaches production.
Runtime guardrails that prevent tool abuse or scope drift.

Implementation blueprint (lightweight but strict)

Inventory and ownership: catalog models and prompts (/ai-model-tracking/).
Controls: enforce change gates (/ai-controls/, /ai-governance-policy-llm-systems/).
Testing: evaluate risk-weighted scenarios (/ai-risk-management-framework/, /ai-risk-assessment-framework/).
Evidence: capture audit trails and provenance (/llm-audit-trail-agent-pipelines/, /rag-provenance-citations/).
Monitoring: detect drift and abuse (/ai-observability/, /llm-observability-agent-workflows/).

Observability and evidence

LLM observability: /ai-observability/ and /llm-observability-agent-workflows/
Audit logs: /llm-audit-trail-agent-pipelines/
Provenance: /rag-provenance-citations/
Tracking: /ai-model-tracking/

Prompting and safety

System prompting: /system-prompting/ plus /system-prompting/guidelines/ and /system-prompting/tools/
Prompt quality and safety: /llm-prompting/, /prompt-guidelines/, /ai-prompt-examples/
Security posture: /llm-security/, /owasp-llm/

Link governance subs to this pillar: framework, policy, controls, data governance.
Link observability, audit, and prompting pages back here to centralize authority.
From tools and downloads (audit log schema, governance policy template), link back here for context.