Security and governance

AI red teaming

Structured adversarial testing intended to discover ways an AI system can fail, be misused, or violate constraints.

AI safety

The field and practice of keeping AI-system risks within acceptable bounds across design, deployment, operation, and retirement.

AI supply chain

The people, artifacts, services, and infrastructure whose integrity can change an AI system's behavior or authority.

AI-ready data

Data demonstrably fit for a named AI use, with the quality, structure, rights, provenance, coverage, and separation that use requires.

Adversarial example

An input constructed to make a model produce an incorrect or attacker-chosen result, often through a change that appears insignificant to a person.

Adversarial training

Training on adversarially generated examples to improve robustness against attacks inside a defined threat model.

Anthropomorphism

Attributing human mental states, motives, understanding, or emotion to an AI system because its behavior resembles a person's.

Assurance case

A structured argument, supported by evidence, that a system is acceptably safe or dependable within a stated scope.

Blast radius

The plausible scope of damage, exposure, or irreversible change if a component, credential, or action fails or is compromised.

Context poisoning

Corrupting information placed into an agent's active or persistent context so later decisions are based on false facts, malicious instructions, or distorted state.

Data poisoning

Introducing malicious, misleading, or strategically biased data into training, tuning, retrieval, memory, or evaluation pipelines to alter later behavior.

Differential privacy

A mathematical privacy framework that bounds how much a computation's output can change because one person's data is included or removed.

Direct prompt injection

Prompt injection delivered directly through the current user's message or another explicit input channel.

Excessive agency

Granting an AI system more functionality, permissions, autonomy, or action scope than required for its intended task.

Explainability

The ability to provide a human-usable account of why a system produced a particular output or action.

Guardrail

A rule, model, filter, policy check, or enforcement mechanism intended to constrain inputs, outputs, or actions of an AI system.

Indirect prompt injection

Malicious instructions embedded in external content such as webpages, documents, email, code, tool results, or retrieved memory that an AI system later processes.

Interpretability

The degree to which a human can understand how a model represents information or produces behavior.

Jailbreak

An input strategy intended to make a model bypass or disregard its trained or instructed safety restrictions.

Least privilege

Granting an identity or component only the minimum permissions needed for a bounded task, for no longer than needed.

Personally identifiable information (PII)

Information that can identify, distinguish, or be linked to a specific person, alone or in combination with other data.

Policy as code

Expressing policy in machine-readable rules that can be versioned, tested, reviewed, and enforced by software.

Post-hoc explanation

An explanation produced after a model has generated a prediction or action, often by analyzing inputs, outputs, or a separate approximation.

Prompt injection

Manipulating an AI system by placing instructions in input or data that the model treats as authoritative enough to alter intended behavior.

Provenance

Evidence describing the origin, ownership, custody, transformation, and version history of data, code, models, skills, tools, or claims.

Risk-scoped autonomy

Granting different levels of agent discretion and authority according to task verifiability, reversibility, sensitivity, and blast radius.

Rollback

Restoring a previously known-good software, configuration, model, policy, or data state after a failed or harmful change.

Tool poisoning

Manipulating a tool's description, schema, implementation, or output so an agent selects unsafe actions or incorporates attacker-controlled instructions.

AI alignment

The effort to keep an AI system's behavior compatible with intended human goals, constraints, and values across relevant conditions.

AI bias

A systematic tendency in data, modeling, or decision processes that skews estimates, errors, treatment, or impacts.

AI fairness

The normative and technical treatment of how an AI system distributes errors, benefits, burdens, and opportunities across people or groups.