62 Terms

AI Glossary

Every enterprise AI term you need to know — defined clearly with practical context.

A B C D E F I J K L M O P R S T V Z

A

Adversarial Attack (AI)

Deliberate attempts to manipulate AI system behavior through crafted inputs.

AI Agent

An AI system that can autonomously plan, reason, and take actions to accomplish multi-step tasks.

AI Alignment

Ensuring AI systems behave according to human values, intentions, and organizational goals.

AI Audit

A systematic examination of AI system operations, decisions, and impacts for compliance and quality assurance.

AI Bias

Systematic errors in AI outputs that result from biased training data or flawed model design.

AI Budget

A defined spending limit for AI usage, typically allocated per department, team, or individual user.

AI Deployment

The process of making AI models available for use in production environments with appropriate controls.

AI Ethics

The principles and guidelines governing the responsible development and use of AI systems.

AI FinOps

The practice of managing and optimizing AI and LLM costs through financial governance, budgeting, and usage analytics.

AI Gateway

A centralized access point that manages, monitors, and controls traffic between applications and AI model providers.

AI Governance

The framework of policies, processes, and controls that guide responsible AI development and usage within organizations.

AI Guardrails

Safety mechanisms that constrain AI system behavior to prevent harmful, biased, or off-policy outputs.

AI Hallucination

When an AI model generates factually incorrect information presented as truth.

AI Incident

An event where an AI system causes harm, produces incorrect outputs, or violates organizational policies.

AI Orchestration

The coordination of multiple AI services, tools, and workflows into cohesive automated processes.

AI Policy

Organizational rules defining acceptable AI usage, data handling, and governance requirements.

AI Risk

Potential negative outcomes from AI system deployment, including data leaks, bias, hallucinations, and security vulnerabilities.

AI Safety Layer

A middleware component that sits between users and AI models to enforce safety policies and controls.

AI Transparency

The practice of being open about how AI systems work, what data they use, and how decisions are made.

API Management (for AI)

Tools and practices for managing, securing, and monitoring AI model API access and usage.

B

Brand Safety (AI)

Controls ensuring AI outputs align with organizational brand voice, values, and communication guidelines.

C

Compliance Framework

A structured set of guidelines and controls that ensure AI systems meet regulatory and organizational requirements.

Content Safety

Mechanisms ensuring AI-generated content is appropriate, accurate, and aligned with organizational standards.

Context Window

The maximum amount of text (measured in tokens) that an AI model can process in a single conversation.

Credit System

A universal currency that normalizes AI costs across multiple providers into a single internal unit.

D

Data Leak Prevention (for AI)

Specific controls preventing sensitive organizational data from being exposed through AI interactions.

Data Loss Prevention (DLP)

Technologies and practices that detect and prevent unauthorized transmission of sensitive data.

Data Sovereignty

The principle that data is subject to the laws and governance of the jurisdiction where it is collected or processed.

Department Management

Organizational hierarchies within AI platforms that mirror company structure for access and budget control.

E

Embedding

A numerical vector representation of text that captures semantic meaning for AI processing.

Enterprise AI

The strategic deployment of AI systems within large organizations with appropriate governance and security.

Explainability (XAI)

The ability to understand and explain how an AI model arrives at its outputs or decisions.

F

Federated Learning

A machine learning approach where models are trained across multiple devices without sharing raw data.

Fine-Tuning

The process of further training a pre-trained AI model on specific data to customize its behavior for particular tasks.

Foundation Model

A large AI model trained on broad data that can be adapted to many downstream tasks.

I

Inference Cost

The computational cost of running a query through an AI model, typically measured per token.

J

Jailbreaking (AI)

Techniques used to bypass AI safety controls and make models produce restricted or harmful outputs.

K

Knowledge Graph

A structured representation of relationships between entities used to enhance AI reasoning.

L

Large Language Model (LLM)

A deep learning model trained on vast text datasets that can understand and generate human-like text.

M

Model Card

A documentation framework providing transparency about an AI model's capabilities, limitations, and intended use.

Model Endpoint

An API URL where AI model inference requests are sent and responses received.

Model Orchestration

The coordination of multiple AI models to work together on complex tasks or provide redundancy.

Model Routing

The automated process of directing AI queries to the optimal model based on cost, latency, capability, or policy requirements.

Multi-Tenancy

An architecture where a single platform instance serves multiple isolated organizational units or customers.

Multimodal AI

AI systems that can process and generate multiple types of data including text, images, audio, and video.

O

On-Premises AI

AI deployment where all infrastructure, models, and data processing occur within an organization's own facilities.

P

PII Redaction

The automatic detection and removal of personally identifiable information from text before it reaches AI models.

Prompt Injection

An attack technique where malicious instructions are embedded in user prompts to manipulate AI model behavior.

R

Red Teaming (AI)

The practice of adversarially testing AI systems to discover vulnerabilities and failure modes.

Reinforcement Learning

A training technique where AI learns optimal behavior through trial, error, and reward signals.

Responsible AI

An approach to AI development and deployment that prioritizes safety, fairness, transparency, and accountability.

Retrieval-Augmented Generation (RAG)

A technique that grounds AI responses in retrieved documents to improve accuracy and reduce hallucinations.

Role-Based Access Control (RBAC)

A security model that restricts system access based on organizational roles assigned to users.

S

Semantic Filtering

AI-powered content analysis that understands meaning and intent rather than relying on keyword matching.

Shadow AI

Unauthorized use of AI tools by employees outside of IT-approved channels.

Single Sign-On (SSO)

An authentication method that allows users to access multiple applications with one set of credentials.

Synthetic Data

Artificially generated data that mimics real-world data characteristics without containing actual sensitive information.

System Prompt

Hidden instructions given to an AI model that define its behavior, personality, and constraints for a conversation.

T

Token

The basic unit of text processing in LLMs — typically a word, subword, or character that models use for input and output.

Training Data

The dataset used to train an AI model, which significantly influences its capabilities and biases.

V

Vector Database

A database optimized for storing and querying high-dimensional vector embeddings for AI applications.

Z

Zero-Retention Policy

An architecture where no user conversation data is stored on servers, only existing in the user's browser.