AI Security Glossary

The potential for losses or legal penalties due to violations of laws, regulations, or prescribed practices.

A lightweight, standalone executable package of software that includes everything needed to run it, ensuring consistency across environments.

AppSOC�s proprietary technology for prioritizing security issues based on severity, exploitability, and business context.

A practice that automates the integration and deployment of code changes, enabling frequent and reliable updates to applications.

A proactive and continuous program to monitor, evaluate, and reduce levels of exploitability and validate analysis and remediation processes.

A training-time attack where malicious or manipulated data is introduced into the training set to distort a model’s behavior, reduce accuracy, or embed hidden backdoors.

The aspect of information technology that deals with the ability of an organization or individual to determine what data can be shared with third parties.

The process of determining and fetching the correct versions of dependencies for a software project.

A methodology that combines software development (Dev) and IT operations (Ops) to shorten the development lifecycle, deliver high-quality software continuously, and improve collaboration between development and operations teams.

An approach that integrates security practices into the DevOps process, ensuring that security is incorporated at every stage of the software development lifecycle to enhance the overall security posture of applications.

A testing method that analyzes applications in their running state to identify vulnerabilities by simulating external attacks.

A comprehensive European regulation that classifies AI systems by risk and imposes strict requirements on high-risk applications—covering transparency, accountability, and safety.

A system that monitors and analyzes endpoint activities to detect, investigate, and respond to security incidents in real-time.

A process developed by First.org for estimating the likelihood that a software vulnerability will be exploited in the wild.

A failure to detect an actual vulnerability or threat, leading to a potential security risk remaining unaddressed.

A security alert that incorrectly indicates the presence of a vulnerability or threat when none exists.

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.