A governance hub for organizing AI systems, risks, roles, evidence, policies, audits, and accountability across the AI lifecycle.
30
Terms
9
Comparisons
8
Questions
AI governance turns abstract responsibility into repeatable practices: inventories, policies, risk assessments, evidence records, audits, and monitoring. It connects legal duties with product, data, security, and procurement work.
Without governance, teams cannot prove who approved an AI system, what data it used, how it was tested, or what controls existed when something went wrong.
Useful for ISO/IEC 42001 programs, AI Act readiness, vendor governance, internal policy design, audit preparation, and board-level AI risk reporting.
Concepts for organizing responsibility, accountability, inventory, and policy controls.
Terms for reviewing, evidencing, and improving AI systems and related governance processes.
Recurring governance problems that appear in real systems and procurement workflows.
A side-by-side comparison of AI System and AI Model. Understand why a model is usually a component, while an AI system includes the broader deployed arrangement that turns inputs and objectives into outputs.
A side-by-side comparison of AI Risk Assessment and AI Audit. Understand how a risk process supports decisions before and during deployment, while an audit evaluates systems or governance against defined criteria.
A side-by-side comparison of AI Assurance and AI Audit. Understand how assurance is a broader evidence-generating confidence practice, while audit is a structured evaluation against defined criteria.
A side-by-side comparison of AI Governance and AI Management System. Understand how the broader framework of oversight differs from the structured processes or platform used to manage AI across the lifecycle.
A side-by-side comparison of an AI Management System and ISO/IEC 42001. Understand the difference between an organization’s AI governance system and the international standard for establishing and improving it.
A side-by-side comparison of an AI Inventory or System Register and an AI Use Policy. Understand how a catalogue of AI systems differs from rules for acceptable organizational use.
A side-by-side comparison of Shadow AI and AI Inventory System Register. Understand how the concepts differ, when each term applies, and why the distinction matters for AI governance, evaluation, or system design.
A side-by-side comparison of Trustworthy AI and Responsible AI. Understand how one term describes qualities that support reliance on AI, while the other describes the practices used to govern AI responsibly.
A side-by-side comparison of Vendor Risk and Vendor Due Diligence. Understand how the concepts differ, when each term applies, and why the distinction matters for AI governance, evaluation, or system design.
Use AI Risk Assessment to identify and manage risks; use AI Audit to evaluate evidence, controls, or claims against defined criteria.
AI Assurance is the set of practices that provide justified confidence that an AI system works as intended in its operational context.
Shadow AI is the use of AI tools or systems without formal approval, oversight, or governance by an organization. It creates risks around data leakage, compliance, security, and inconsistent decision-making, and is typically managed through policy, approved alternatives, training, and monitoring.
An AI Use Policy is an internal governance document that defines how AI tools and systems may be used within an organization. It typically covers acceptable and prohibited uses, roles and responsibilities, data handling, approval requirements, monitoring, and incident escalation.
AI vendor due diligence is the assessment of third-party AI products or providers for security, privacy, reliability, governance, and compliance risks. It helps organizations decide whether and how a vendor can be safely approved, procured, monitored, or restricted.
Algorithmic Accountability is responsibility for the design, operation, decisions, and impacts of algorithmic systems. It involves transparency, traceability, oversight, and mechanisms for explaining, contesting, or remedying harmful outcomes.
An AI Model Card is a concise documentation artifact that accompanies a trained model or AI service. It typically describes intended use, limitations, evaluation results, relevant risks, data or training context, and other information that supports transparency and responsible deployment.
Transparency is the degree to which an AI system’s purpose, data use, operation, limitations, and outputs can be understood or examined by relevant stakeholders.