AI Security & Governance, defined

An AI Bill of Materials (AI BOM or AIBOM) is a structured inventory of every component used to build, train, and deploy an AI system: training data sources, foundation models, fine-tuning datasets, prompts, libraries, dependencies, and downstream integrations.

AI governance is the set of policies, processes, roles, and controls an organization uses to develop, deploy, and operate AI systems responsibly and in compliance with applicable laws, standards, and stakeholder expectations.

The EU AI Act (Regulation 2024/1689) is the European Union's binding legal framework for artificial intelligence systems. It classifies AI systems by risk tier — unacceptable, high, limited, minimal — and imposes obligations on providers, deployers, importers, and distributors based on that classification.

ISO/IEC 42001:2023 is the first international certifiable management-system standard for artificial intelligence. It defines requirements for establishing, implementing, maintaining, and continually improving an AI Management System (AIMS) within an organization.

Model risk is the potential for adverse outcomes — financial loss, regulatory action, reputational damage, customer harm — arising from errors in the development, implementation, or use of an AI/ML model. The discipline that manages this risk is called Model Risk Management (MRM).

The NIST AI Risk Management Framework (AI RMF 1.0) is a voluntary US framework for managing AI system risk across the lifecycle. It's organized around four core functions — Govern, Map, Measure, Manage — and a dedicated Generative AI Profile published in 2024.

Prompt injection is an attack where adversarial text — placed in user input, retrieved documents, tool outputs, or other model context — overrides the model's intended instructions and causes it to perform actions or disclose information the developer did not authorize.

Shadow AI is the use of AI tools, models, or services inside an organization without IT, security, or governance team approval. It mirrors the broader concept of shadow IT but specifically covers generative AI assistants, agents, and ML services.

The Trust Gap is the difference between an organization's self-reported AI security posture and the posture verifiable from independent evidence. A wide gap signals process drift, optimistic self-assessment, or gaps between what the security team believes is happening and what is actually happening.