AI Security Glossary

Continuously monitoring, assessing, and improving the security measures and defenses of AI systems to ensure they are protected against threats and vulnerabilities.

Safeguarding the integrity and security of AI systems and their components throughout the supply chain, from development to deployment, to prevent vulnerabilities and ensure trustworthiness.

A framework that combines Trust, Risk, and Security Management to govern AI systems—ensuring they operate responsibly, ethically, and within regulatory and organizational boundaries.

The generation of harmful, offensive, or biased content by AI systems—often due to flawed training data, poor filtering, or misaligned model objectives.

The datasets used to teach AI models how to recognize patterns, make decisions, or generate content. Training data quality and composition directly affect model performance, bias, and security.

AI Usage Control (AIUC) governs how users, systems, and applications can access, interact with, and utilize AI models and data. It enforces dynamic policies to prevent misuse, data leakage, and unauthorized prompts—ensuring safe and compliant AI behavior across the enterprise.

Protecting Application Programming Interfaces (APIs) from vulnerabilities, ensuring secure data exchange, and preventing unauthorized access or malicious activities.

A tactic where attackers craft inputs designed to fool AI models into making incorrect predictions. These subtle manipulations pose serious risks to model reliability, accuracy, and trust.

The protection of autonomous AI systems—such as agents or AI-driven workflows—that independently perform tasks, access tools, or make decisions on behalf of users.

Tools and processes for continuously monitoring, assessing, and improving the security posture of software applications throughout their development lifecycle, with a focus on identifying, assessing, and mitigating vulnerabilities and risks associated with applications to ensure they remain secure against potential cyber threats.

Evaluating applications to identify and mitigate security vulnerabilities, ensuring that they are secure against threats and comply with security standards.

A storage location for software artifacts, including binaries and dependencies, used in the development and deployment process.

Technology that enables machines to perform tasks typically requiring human intelligence, such as reasoning, decision-making, and learning. Used in automation, analytics, and interaction across diverse business and consumer applications.

A chronological record of all activities and changes that have occurred in a system or process, ensuring transparency and accountability.

The use of automated tools and processes to continuously test applications for security vulnerabilities throughout the development lifecycle.

A a program that rewards individuals for finding and reporting security vulnerabilities in an organization's software, encouraging proactive identification and resolution of potential security issues.

List compiled by the US Cybersecurity and Infrastructure Security Agency which identifies vulnerabilities confirmed as being exploited in the wild.

A proposed California law that aims to regulate AI systems—focusing on safety, transparency, and civil rights protections—especially in high-risk and public-facing applications.

A storage location for source code, often using version control systems like Git.

A list of publicly know vulnerabilities maintained by NIST.