An adversarial attack is a deliberate attempt to manipulate an AI model’s behavior by crafting inputs designed to cause a specific wrong output.

An AI cybersecurity incident is any event where an AI system is compromised, manipulated, or causes unintended harm with security consequences.

AI governance is the set of policies, processes, roles, and controls that an organization implements to ensure its AI systems are developed, deployed, and operated responsibly.

AI observability is the capability to understand what an AI system is doing, why it is doing it, and whether its behavior has changed.

AI red teaming is the practice of adversarially probing AI systems to find failures before attackers do.

AI risk management is the structured practice of identifying, assessing, and treating risks arising from the development, deployment, and operation of AI systems.

An AI system is the complete stack of components that delivers an artificial intelligence capability in practice.

AI threat modeling is the structured process of identifying what can go wrong in an AI system, who might cause it, and what the consequences would be.

Artificial intelligence (AI) is the capability of a computer system to perform tasks that normally require human judgment. That includes recognizing patterns, understanding language, making decisions, and generating content.

A method for unlocking reasoning capabilities in large language models. By encouraging step-by-step thinking.