AI Security Glossary

Systematic and repeatable errors in a computer system that create unfair outcomes, such as privileging one arbitrary group of users over others.

AI data governance involves the management of data quality, legality, and ethical standards to ensure responsible AI deployment.

AI Governance involves the systematic framework that oversees the ethical design, development, and implementation of AI technologies to ensure accountability, transparency, and fairness.

AI Impact Assessment is a critical process that evaluates the potential effects and implications of AI technologies on society, the environment, and the economy.

Comprehensive cataloging and management of all artificial intelligence systems, models, datasets, and related resources within an organization.

Security practice where experts simulate attacks on AI systems to identify vulnerabilities, assess their robustness, and improve their resilience against real-world threats.

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.

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

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.

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

A list of publicly know vulnerabilities maintained by NIST.

A standardized, repeatable, and vendor agnostics method to compare application vulnerabilities.

A community-developed list of common software and hardware weaknesses, which could introduce vulnerabilities.

Adhering to industry standards, regulations, and best practices to ensure that applications meet legal and security requirements

A comprehensive review of an organization's adherence to regulatory guidelines.