ISO/IEC 42001
The international standard for an AI management system, providing a certifiable framework for governing the development and use of AI -- covering policy, roles and accountability, AI risk and impact assessment, lifecycle controls, supplier management, and continual improvement. It follows the same management-system structure as ISO/IEC 27001, so an organisation with an established ISMS can extend rather than rebuild. ISO/IEC 42001 appears in AIGP, AICP and AAISM governance material.
Why It Matters
In practice 42001's contribution is that it makes AI governance auditable: where the NIST AI RMF offers a structure for thinking and the EU AI Act imposes obligations, 42001 supplies the management system an assessor can certify against, which is increasingly what enterprise customers ask for in procurement. Its most distinctive requirement is the AI system impact assessment, which considers effects on individuals and society and not only risk to the organisation -- a genuine departure from a conventional security risk assessment and the part teams most often under-scope. The overlap with ISO/IEC 27001 is substantial in policy, competence, supplier and improvement clauses, so the efficient path is to extend existing processes and add the AI-specific controls rather than run a parallel programme. As with any management-system standard, certification evidences that a process exists and is followed, not that a given model is safe. On exams such as AIGP and AAISM, expect questions on how 42001 relates to 27001 and to the AI RMF, and what an impact assessment must consider.
Related AI Security terms
Prompt Injection
An attack against large language model (LLM) applications in which crafted input manipulates the model into ignoring its original instructions or system prompt and performing attacker-controlled actions. Direct prompt injection embeds malicious instructions in user input ("ignore previous instructions and..."), while indirect prompt injection hides instructions in external content the model ingests (web pages, documents, emails) during retrieval or tool use. It ranks as the #1 risk in the OWASP Top 10 for LLM Applications. Prompt injection is a core topic in AI security and governance certifications such as AIGP, AICP, and AAISM.
Jailbreaking (LLM)
Techniques that bypass an AI model's safety guardrails and content policies to elicit prohibited outputs such as instructions for weapons, malware, or disallowed content. Common methods include role-play framing ("act as an unrestricted assistant"), obfuscation and encoding, many-shot priming, and adversarial suffixes discovered through optimization. Jailbreaking differs from prompt injection: jailbreaking targets the model's safety alignment, whereas prompt injection hijacks an application's surrounding instructions. It is central to red-teaming generative AI and appears in AICP, AIGP, and AAISM study domains.
Adversarial Examples
Inputs deliberately perturbed with small, often human-imperceptible changes that cause a machine learning model to misclassify them — for example altering a few pixels so an image classifier reads a stop sign as a speed-limit sign, or crafting audio that a voice assistant transcribes as a hidden command. Adversarial machine learning is the broader field studying such evasion attacks alongside poisoning and extraction across the ML lifecycle. NIST formalizes the taxonomy in NIST AI 100-2. Covered in AAISM, AICP, and AIGP.
Data Poisoning
An attack in which adversaries inject malicious or mislabeled data into a model's training set to degrade performance, cause targeted misclassifications, or implant a backdoor that activates on a specific trigger. Poisoning can target the pre-training corpus, fine-tuning data, or a retrieval (RAG) knowledge base. Because modern models train on large, often web-scraped datasets, even a small fraction of poisoned samples can have outsized effects. It appears in the OWASP Top 10 for LLM Applications and NIST AI 100-2, and is tested in AAISM, AICP, and AIGP.
Model Inversion Attack
A privacy attack that reconstructs sensitive training data, or attributes of it, by repeatedly querying a model and analyzing its outputs — for example recovering recognizable face images from a facial-recognition model or inferring private attributes of individuals in the training set. Model inversion undermines the confidentiality of the data a model was trained on and can breach privacy regulations such as GDPR. It is a key privacy risk in AI governance and is covered in AICP, AIGP, and AAISM.
Membership Inference Attack
A privacy attack that determines whether a specific data record was part of a model's training set, by exploiting differences in the model's confidence or behavior on data it has seen versus unseen data. It can reveal, for instance, that a particular person's medical record was used to train a model — a confidentiality breach in its own right. Membership inference is closely related to model inversion and is relevant to AICP, AIGP, and privacy-focused AI governance.