AI Security Glossary

European Union regulation that mandates strict data privacy and security measures for protecting personal data, giving individuals greater control over their personal information.

AI systems designed to create original content—such as text, images, code, or audio—by learning patterns from large datasets. Used in automation, creativity, and user interaction across industries.

U.S. law that sets national standards for the protection of sensitive patient health information, ensuring that such information is kept confidential and secure, particularly in electronic form.

An open-source platform and model hub widely used for sharing, deploying, and fine-tuning machine learning and NLP models, especially transformers and large language models.

A failure to properly inspect, filter, or validate AI-generated outputs—leading to the release of unsafe, biased, or non-compliant content into user-facing applications.

The process used to manage the lifecycle of incidents to ensure that normal service operation is restored as quickly as possible.

The process of identifying, managing, and mitigating security incidents to minimize their impact on the organization.

The ability to provision and support computing infrastructure using code instead of manual processes and settings.

Verification that software has not been altered or tampered with.

An independent, objective assurance and consulting activity designed to add value and improve an organization's operations.

Metrics used to provide an early signal of increasing risk exposures in various areas of an enterprise.

LLM Firewall is a security layer that monitors and filters interactions with large language models, blocking malicious prompts, preventing data leaks, and enforcing compliance. It safeguards AI systems from threats like prompt injections while ensuring safe, authorized, and regulation-compliant usage.

A set of practices and tools for managing the lifecycle of large language models (LLMs), including deployment, monitoring, scaling, and governance in production environments.

A type of AI model trained on massive text datasets to understand and generate human-like language. LLMs power chatbots, assistants, and other natural language applications.

Ensuring that software components comply with licensing agreements and open-source licenses.

A research initiative from MIT that catalogs emerging risks, incidents, and best practices related to AI systems—supporting safe deployment and policy development through shared, evidence-based insights.

A knowledge base that catalogs the tactics, techniques, and case studies of adversarial attacks on machine learning (ML) and artificial intelligence (AI) systems to help organizations understand and mitigate these threats.

Knowledge base of adversary tactics and techniques based on real-world observations, used as a foundation for developing threat models and methodologies in the cybersecurity community.

The practice of deploying, managing, and monitoring machine learning models in production to ensure they operate efficiently and effectively.

A method that enables software systems to learn from data and improve performance without explicit programming. Commonly used for predictions, classifications, and decision-making across dynamic or data-rich environments.