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AI Governance

Definition

AI governance encompasses the policies, technical controls, organizational processes, and oversight mechanisms that ensure AI systems operate safely, transparently, and within regulatory boundaries. In the context of AI automation platforms, governance includes access control (who can build and run AI), tool approval gates (which external services AI can access), audit logging (what AI did and when), cost controls (budget limits per department), and compliance alignment (mapping AI operations to frameworks like EU AI Act, NIST AI RMF, and ISO 42001).

Why AI Governance Matters

As organizations deploy AI beyond individual chat assistants to automated department workflows, the risk surface expands dramatically. An ungoverned AI agent with access to customer data, financial systems, or external APIs can cause real damage — data leaks, compliance violations, runaway costs, or reputational harm. Governance is the infrastructure that prevents these outcomes before they happen, rather than detecting them after the fact.

JieGou's 10-Layer Governance Stack

JieGou implements governance across 10 layers: identity and authentication, encryption (AES-256-GCM for API keys), data residency controls, environment management, role-based access control (5 roles, 20 permissions), escalation protocols, tool approval gates, audit logging (30 event types), compliance timeline, evidence export, and regulatory compliance mapping. Each layer is independently configurable per account.

GovernanceScore

GovernanceScore is a quantitative metric (0-100) that measures how well an organization's AI deployment is governed across 8 factors. It provides a single number for executives and auditors to track governance posture over time, benchmark against standards, and identify gaps before they become incidents.

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