Glossary of AI Governance Concepts

This glossary defines the core concepts required to understand, evaluate and govern AI systems operating in enterprise environments. It is designed for leaders responsible for deploying AI in production, including CISOs, CIOs, Chief Risk Officers, Heads of AI and Procurement teams. As AI systems move from generating outputs to executing actions, governance must extend beyond access and model safety to include real-time control, authorization and accountability at the point of execution.

Foundational AI Concepts

Artificial Intelligence (AI): Software systems designed to perform tasks that typically require human intelligence, including reasoning, pattern recognition, decision-making and language understanding. In enterprise environments, AI systems range from analytical tools that generate insights to autonomous systems capable of executing actions.

Generative AI: A class of AI systems that produce outputs such as text, code, images or audio in response to prompts. Governance risks associated with generative AI primarily relate to accuracy, bias, hallucination and appropriate use.

Large Language Model (LLM): A type of AI model trained on large-scale text data that can understand and generate human language. LLMs underpin most generative AI systems.

AI Agent: An AI system capable of perceiving context, making decisions and executing actions to achieve defined objectives. AI agents can call APIs, trigger workflows, access enterprise systems, initiate transactions and interact with other agents.

Agentic AI: AI systems operating in an autonomous or semi-autonomous mode, executing sequences of actions across systems with limited human intervention.

Multi-Agent System: An environment in which multiple AI agents interact, coordinate or delegate tasks.

Autonomous Execution: The capability of an AI system to initiate and complete actions without requiring human approval at each step.

Shadow AI: AI tools, models or agents operating within an organization without formal approval, registration or governance.

Foundation Model: A large, pre-trained AI model that serves as the base for downstream applications and agents.

Prompt Injection: An attack technique in which malicious instructions are embedded in data processed by an AI system, causing it to deviate from its intended behavior.

Governance Concepts

AI Governance: The policies, processes, controls and infrastructure required to ensure AI systems operate safely, reliably and in alignment with regulatory and organizational requirements.

Model Governance: The governance domain responsible for ensuring AI models are safe, reliable and fit for purpose.

System Governance: The governance domain responsible for ensuring AI systems interact securely with enterprise infrastructure.

Action Governance: The governance domain that determines whether a specific AI action is authorized to execute, under what authority and within what constraints.

Runtime Governance: The enforcement layer that operates continuously while AI systems are running in production.

Execution Control: The capability to enforce governance decisions at the exact moment an AI system attempts to execute an action.

Governance Lifecycle: The continuous process of governing AI systems across their lifecycle: Discover, Evaluate, Deploy, Operate and Evolve.

Human-in-the-Loop: A governance mechanism requiring human review or approval before certain AI actions are executed.

AI Risk Classification: A structured approach to categorizing AI systems based on the level of authority they are granted and the potential impact of their actions.

Audit Trail: A tamper-resistant, verifiable record of AI actions, decisions and outcomes.

Identity and Trust

Identity: The verifiable identity of an actor performing an action.

Know Your Agent (KYA): The principle that organizations must be able to identify, verify and account for every AI agent operating within their environment.

Identity and Access Management (IAM): The systems and processes used to authenticate identities and control access to resources.

Delegated Authority: A mechanism by which a human or organizational principal grants an AI agent permission to act on their behalf within defined scope and constraints.

Delegation Chain: The traceable sequence of authority from a human or organizational principal to an AI agent.

Verifiable Credentials: Cryptographically signed digital credentials that allow an entity to prove identity, permissions or attributes without relying on a centralized authority.

Cryptographic Proof: A mathematical method for proving that a claim is valid without revealing the underlying data.

Decentralized Identity: An identity model where individuals or entities control their own credentials rather than relying on centralized identity providers.

Decentralized Identifiers (DIDs): A W3C standard for creating unique, verifiable and decentralized digital identifiers.

Selective Disclosure: A technique that allows specific attributes of a credential to be shared without exposing the full credential.

The Trust Stack

Trust Stack: The ordered set of primitives required to authorize an AI action: Identity, Authority, Intent and Action.

Authority: The right to perform a specific action within defined limits.

Intent: The declared purpose and scope of an action request.

Consent: An explicit, time-bound authorization provided by a principal allowing specific actions to be performed on their behalf.

Policy: The rules and constraints that define how and when actions may be executed.

Enforcement: The runtime application of policy to determine whether an action is allowed, constrained or blocked.

Verification: The process of proving that an action was authorized and executed within defined constraints.

Audit: The structured record of governance decisions and execution outcomes, enabling accountability and compliance.

Enterprise and Security Terms

AI Security: The discipline focused on protecting AI systems from threats.

Agent Identity: A unique, verifiable identity assigned to an AI agent.

Machine Identity: Digital identities assigned to non-human entities.

Non-Human Identity (NHI): A category that includes machine identities and AI agents.

Zero Trust Architecture: A security model based on continuous verification of identity and context.

Policy Decision Point (PDP): A component that evaluates whether an action should be allowed based on policy.

Policy Enforcement Point (PEP): A component that enforces the decision made by the Policy Decision Point.

Least Privilege: The principle of granting only the minimum level of access required.

Access Control: Mechanisms used to restrict access to systems and data.

Authorization: The process of determining whether an action is permitted.

Authentication: The process of verifying the identity of an actor.

Regulatory and Standards Context

EU AI Act: A European regulatory framework that classifies AI systems by risk and imposes requirements on high-risk systems.

NIST AI Risk Management Framework (AI RMF): A framework providing guidance for identifying, assessing and managing risks associated with AI systems.

ISO 42001: An international standard for managing AI systems and governance processes.

SOC 2: An auditing standard assessing controls related to security, availability and data protection.

Regulatory Accountability: The obligation to demonstrate that AI systems operated within authorized parameters and that verifiable evidence can be produced on demand.