Enterprise Technology Glossary

6,173 results · page 11 of 309

• AI Ethics

  AI ethics is the framework of principles, governance practices, and technical controls that directs how organizations design, deploy, and oversee artificial intelligence systems to meet legal, societal, and organizational requirements in areas such as fairness, accountability, transparency, privacy, safety, and security.

• AI Ethics Board

  AI ethics board is a formal governance body that reviews and oversees an organization’s artificial intelligence systems and practices to ensure they align with defined ethical, legal, and governance requirements, providing structured oversight for AI-related risk, compliance, and accountability.

• AI Ethics Committee

  AI ethics committee is a formal governance body that oversees how an organization designs, deploys, and operates artificial intelligence systems, ensuring alignment with legal, risk, and policy requirements so automated decision-making remains documented, accountable, and subject to structured oversight in enterprise contexts.

• AI Ethics Framework

  AI ethics framework is a structured set of principles, governance mechanisms, and operational processes that organizations apply to AI systems to align them with defined ethical, legal, and societal requirements in enterprise environments and to support compliance and risk management.

• AI Ethics Review Board

  AI ethics review board is a formal governance body within an organization that evaluates AI systems and projects against legal, ethical, and risk criteria, supporting documented oversight, accountability, and compliance across AI development, deployment, and monitoring in enterprise environments.

• AI Fabric

  AI fabric is an architectural layer that connects and manages distributed AI services, models, and data pipelines across enterprise environments, enabling consistent governance, reuse, and operations of AI workloads within existing data, application, and infrastructure ecosystems.

• AI Fabric Controller

  AI fabric controller is a software control plane that applies artificial intelligence and automation to operate data center or cloud network fabrics, enabling centralized policy management, telemetry-driven optimization, and integration of fabric control with broader enterprise networking, security, and operations workflows.

• AI Factories

  AI factories are organized, repeatable pipelines that turn enterprise data into deployable AI models through standardized processes for data preparation, model training, evaluation, deployment, and monitoring, enabling reuse, governance, and lifecycle management of AI across multiple business applications.

• AI factory

  AI factory is an enterprise architecture pattern that organizes data, models, and feedback into standardized pipelines so organizations can build, deploy, and operate AI workloads in a repeatable, governed way across multiple business domains and applications.

• AI Fail-Safe System

  AI fail-safe system is the combination of policies, controls, and technical mechanisms that ensures an artificial intelligence system shifts to a predefined safe state when faults, anomalies, or out-of-bounds conditions occur, supporting operational safety, compliance, and controlled risk in enterprise environments.

• AI for cybersecurity

  AI for cybersecurity is the use of artificial intelligence techniques to analyze security data, detect threats, and support automated or assisted cyber defense, enabling enterprises to monitor complex environments, prioritize alerts, and support risk and compliance objectives.

• AI for Networking

  AI for networking applies artificial intelligence and machine learning to analyze, optimize, and automate computer networks. It matters in enterprise environments because it supports performance assurance, fault detection, security monitoring, and operational efficiency across complex, software-defined, and hybrid network infrastructures.

• Ai-Generated Content

  Ai-generated content is any digital output such as text, images, code, audio, or video produced by AI models from inputs or prompts, which enterprises must manage with defined controls for quality, governance, security, compliance, and content lifecycle oversight.

• AI Governance

  AI governance is the organizational framework of policies, processes, and controls that oversee how enterprises design, deploy, and manage AI systems, ensuring alignment with legal, risk, and business requirements across the AI lifecycle in complex technology and data environments.

• AI Governance Framework

  AI governance framework is a structured set of policies, processes, roles, and controls that organizations use to direct and monitor AI systems so they comply with legal and regulatory requirements, manage technical and operational risk, and support accountable decision-making.

• AI Hallucinations

  AI hallucinations are incorrect or fabricated outputs generated by AI models and presented as factual, which raises reliability, compliance, and operational risk for enterprises that use generative systems in customer support, knowledge management, software development, and decision support workflows.

• AI Hardware Abstraction Layer

  AI hardware abstraction layer is a software layer that standardizes how AI workloads access heterogeneous accelerators and compute hardware, allowing enterprises to run models across different devices with one codebase and support portability, procurement flexibility, and operational consistency.

• AI Incident Response Plan

  AI incident response plan is a documented set of procedures that defines how an enterprise prepares for, detects, analyzes, contains, and recovers from incidents involving AI systems and data, aligning AI operations with existing incident response, security, and compliance processes.

• AI infrastructure

  AI infrastructure is the integrated hardware, software, networking, and data stack that runs enterprise artificial intelligence workloads, enabling scalable training and inference while aligning with existing data platforms, security controls, governance requirements, and operational objectives in on-premises, cloud, or hybrid environments.

• AI Infrastructure

  AI infrastructure is the integrated hardware, software, data, and networking stack that supports development and operation of enterprise AI and machine learning workloads, enabling organizations to run training and inference at scale under their performance, governance, and cost constraints.