AI security frameworks compared
A practitioner's comparison of the five frameworks most AI programs lean on, what each one is for, which are binding or certifiable, and how to combine them without duplicating work.
AI governance frameworks serve different purposes, and no single framework is right for every organization. The right approach depends on your regulatory obligations, the countries where you operate, your industry, customer commitments, and the AI systems you use.
Although these frameworks differ in purpose and application, they share common themes: governance, accountability, risk management, security, transparency, documentation, monitoring, and continual improvement.
AISGRC's proprietary #Governance18 model brings these core components together into a practical AI governance operating model. It can be mapped to relevant legal, regulatory, customer, and industry requirements as your organization's needs evolve.
The guidance below explains where these frameworks overlap, where they differ, and when each may be relevant.
The five frameworks compared
| Framework | What it is | Legally binding | Certifiable | Use it for |
|---|---|---|---|---|
| EU AI Act | Law | Yes — fines to €35m or 7% of turnover | No | Establishing what you are legally required to do |
| NIST AI RMF | Risk method | No | No | Structuring the risk program that produces evidence |
| ISO/IEC 42001 | Management system standard | No | Yes — third-party audit | Proving governance to customers and procurement |
| MITRE ATLAS | Threat knowledge base | No | No | Threat modelling against observed attacker behaviour |
| OWASP (LLM & Agentic Top 10) | Engineering guidance | No | No | Building and testing the system itself |
Two distinctions carry most of the weight. Of these five, only the EU AI Act is enforceable — the rest carry no penalties on their own. Only ISO/IEC 42001 can be certified — no accredited body issues certificates for the others, whatever a vendor may imply.
EU AI Act: the binding one
The EU AI Act regulates AI by risk tier and has applied in general since 2 August 2026. Prohibited practices are already banned, and Article 50 transparency duties now cover chatbots and synthetic content. The Digital Omnibus, in force since 27 July 2026, moved Annex III high-risk obligations to 2 December 2027 and obligations for AI embedded in regulated products under Annex I (such as medical devices and machinery) to 2 August 2028. It also added new prohibited practices. It is the only framework here that carries fines. Read the full EU AI Act guide.
NIST AI RMF: a process for managing AI risk
The NIST AI Risk Management Framework organizes AI risk work into four functions: Govern, Map, Measure and Manage. It is voluntary and not certifiable, and it provides a structured method for managing AI risks. It may be useful where organizations need a recognized risk-management approach, particularly when it aligns with customer, sector, or internal governance requirements. Read the full NIST AI RMF guide.
ISO/IEC 42001: a certifiable AI management system
ISO/IEC 42001:2023 defines requirements for an Artificial Intelligence Management System. Unlike NIST AI RMF it can be independently audited and certified, which matters when customers or regulators want proof rather than assertion. It pairs naturally with NIST AI RMF, which supplies the risk method while ISO supplies the auditable structure. Read the full ISO/IEC 42001 guide.
MITRE ATLAS: how AI systems are actually attacked
MITRE ATLAS is a living knowledge base of adversary tactics and techniques against AI systems, modelled on MITRE ATT&CK. It catalogs incidents and researcher demonstrations as case studies, including AML.CS0035, PromptArmor's demonstration of data exfiltration from Slack AI. Use it for threat modelling rather than governance. Read the full MITRE ATLAS guide.
OWASP: guidance for the people building the system
The OWASP AI Security and Privacy Guide translates AI risk into engineering practice, and the OWASP Top 10 for LLM Applications (2025) names the specific failure modes teams hit in production, from prompt injection to unbounded consumption. The newer OWASP Top 10 for Agentic Applications covers risks specific to autonomous agents. This is the most practical of the five for developers. Read the full OWASP Top 10 for LLMs guide.
How they fit together
These frameworks are not mutually exclusive, but organizations do not need to implement each one as a separate program. The appropriate framework or combination depends on the legal, regulatory, geographic, industry, contractual, and operational requirements that apply to your organization.
Start by establishing practical AI governance foundations: an inventory of AI use cases, clear ownership, risk assessment, policies, security controls, documentation, monitoring, and oversight.
AISGRC's proprietary #Governance18 model organizes these common foundations into eighteen governance components. It provides a practical operating model that can be mapped to the EU AI Act, NIST AI RMF, ISO/IEC 42001, MITRE ATLAS, OWASP, and other relevant requirements as your organization matures.
Other frameworks worth knowing
Beyond the main five, these are worth knowing when building an AI security plan, either as national guidance or as sector-specific reference material.
- NIST AI 100-2 E2025: Adversarial Machine Learning — A Taxonomy and Terminology of Attacks and Mitigations
- Guidelines for Secure AI System Development — UK NCSC and international partners
- AI Playbook for the UK Government (2025)
- Engaging with Artificial Intelligence — Australian Signals Directorate
- Singapore Model AI Governance Framework for Generative AI — plus IMDA's guidance for agentic AI
- OECD AI Principles
- Microsoft Responsible AI Standard and resources
- Google DeepMind Frontier Safety Framework (latest version)
Choosing a framework is not the starting point for most organizations. The priority is to establish an AI governance program your organization can operate: clear ownership, an AI inventory, risk and control processes, documented decisions, and evidence of oversight. AISGRC's proprietary #Governance18 model provides a practical foundation that can be mapped to applicable framework, legal, and industry requirements as they arise. Start with the free AI governance scorecard.
Explore by topic
AI Governance
Operating models, ownership, policies, audit, and oversight for running AI responsibly.
AI Risks
The main AI risks, bias, and AI-enabled fraud, and how to assess them.
Attacks & Defences
Adversarial threats to AI systems and the controls that counter them.
Reference guides
What is AI security?
AI security protects models, training data and AI pipelines from attack. It covers adversarial inputs, data poisoning, model theft and prompt injection.
Choosing an AI security assessment framework
Compare AI security assessment frameworks: NIST AI RMF, ISO/IEC 42001, MITRE ATLAS and OWASP each answer a different question. Pick by what you need.
EU AI Act
The EU AI Act has applied in general since 2 August 2026, and the Digital Omnibus pushed high-risk duties to December 2027. Here is what binds now.
NIST AI RMF
The NIST AI Risk Management Framework organizes AI risk into four functions: Govern, Map, Measure and Manage. Voluntary, not certifiable, widely adopted.
ISO/IEC 42001
ISO/IEC 42001:2023 sets requirements for an AI management system. Unlike the NIST AI RMF it can be independently audited and certified by a third party.
MITRE ATLAS
MITRE ATLAS catalogs how AI systems are attacked in the real world, mapping tactics, techniques and documented case studies in the style of ATT&CK.
OWASP Top 10 for LLM Applications
The OWASP Top 10 for LLM Applications names the failure modes that break AI in production, from prompt injection to excessive agency and unbounded consumption.
How to use the NIST AI Risk Management Framework, step by step
A practical walkthrough of the NIST AI Risk Management Framework: running Govern, Map, Measure and Manage on a real AI system, and using its profiles.
Frequently asked questions
Which AI framework should we start with?
There is no universal starting framework. The right choice depends on your organization's regulatory obligations, operating jurisdictions, industry expectations, customer commitments, and AI use cases.
Rather than selecting a framework in isolation, begin by putting an AI governance program in place. Focus on the common foundations: an AI inventory, accountable owners, risk assessment, policies, security controls, documentation, monitoring, and oversight.
AISGRC's proprietary #Governance18 model brings these components together and can be mapped to the frameworks and requirements that apply to your organization over time.
Do we need more than one framework?
Not necessarily. Most organizations do not need to implement several frameworks as separate programs.
Start with a practical AI governance foundation, then map it to specific frameworks only where legal, regulatory, customer, or industry requirements make that necessary.
AISGRC's proprietary #Governance18 model is designed around common governance components that can be mapped to existing frameworks as your organization's maturity and obligations evolve.
Which AI frameworks are legally binding?
Of these five, only the EU AI Act. NIST AI RMF, ISO/IEC 42001, MITRE ATLAS and OWASP are voluntary and carry no penalties on their own. Binding AI laws also exist outside the EU: in the US, Texas TRAIGA is in force and Colorado's replacement law, SB 26-189, takes effect on 1 January 2027. The voluntary frameworks still produce the evidence those regimes ask for.
Which AI framework can you be certified against?
ISO/IEC 42001 only. Certification is issued by independent certification bodies, which may be accredited by a national accreditation body such as ANAB or UKAS. There is no certification scheme for the NIST AI RMF, MITRE ATLAS or OWASP, and no certificate confers EU AI Act conformity.