The Six AI Security Frameworks Every Mid-Market Leader Should Know, And What They Will Not Do For You
If you are responsible for AI security in your business in 2026, you are working in a space with more public frameworks than ever, and less clarity than ever about how to use them.
There are six frameworks every senior leader should know. They are all free. They are all public. None of them will give you a defensible AI security posture on their own.
That contradiction is the heart of the problem. The frameworks have done a remarkable job of cataloguing what AI risk looks like. They have done almost nothing to tell you how to implement that knowledge inside a specific business with specific people, specific data, and specific regulatory exposure.
This article fixes the gap. Six frameworks. Plain language. Honest about what each will and will not do. By the end of it, you will know more about the AI security landscape than 95% of your peers, and you will know exactly where the implementation work begins.
The six frameworks, and what they actually are
Framework | What it is | Who publishes it |
|---|---|---|
A four-function governance structure (Govern, Map, Measure, Manage) for managing AI risk across an organisation | US National Institute of Standards and Technology | |
A developer-focused list of the ten most critical security vulnerabilities in LLM-powered applications | Open Worldwide Application Security Project | |
A six-element framework for embedding security into the AI development lifecycle (Prepare, Build, Deploy) | ||
A seven-layer security model for deploying Microsoft Copilot in enterprise environments | Microsoft | |
A security guidance framework for AI workloads on AWS | Amazon Web Services | |
A defence-in-depth security framework for AI workloads on Google Cloud | Google Cloud |
Two things to notice before going deeper.
Three of these (NIST, OWASP, Google SAIF) are vendor-neutral frameworks. They apply to any AI deployment regardless of where it runs.
Three of them (Microsoft Copilot Architecture, AWS CAF, Google Cloud Well-Architected) are vendor-specific. They tell you how to secure AI on a particular cloud or product. They are useful but they are not the same kind of artifact as NIST or OWASP. If you mix them without understanding the distinction, you can end up with policy gaps that nobody owns.
What each framework is actually good for
Framework | Best for | Where it stops |
|---|---|---|
NIST AI RMF | Building the governance structure. Setting up who owns what across your organisation. Aligning with regulators. | It does not tell you how to test for prompt injection in your specific application. |
OWASP LLM Top 10 | Threat-modelling a specific LLM application. Giving your engineers a concrete checklist. Aligning with developer practices. | It does not tell you how to organise governance, set policy, or assign accountability across teams. |
Google SAIF | Embedding security into model development and training pipelines. Useful if you are building custom models. | It does not address governance, regulatory compliance, or business-level risk management. |
Microsoft Copilot Security Architecture | Securing a Microsoft Copilot deployment specifically. Useful if you are a Microsoft shop. | It only applies to Microsoft Copilot. It will not help you secure Claude, Gemini, or custom AI. |
AWS CAF Security Perspective | Securing AI workloads on AWS. Aligning with AWS security best practices. | Vendor-specific. Will not help you outside AWS. |
Google Cloud Well-Architected AI Security | Securing AI workloads on Google Cloud. Aligning with GCP security best practices. | Vendor-specific. Will not help you outside Google Cloud. |
The practical observation: for most mid-market organisations, the right combination is NIST AI RMF for governance, OWASP LLM Top 10 for technical controls, and whichever vendor-specific framework matches your dominant cloud or AI platform. That gets you 80% of the way to a defensible security posture.
The remaining 20% is where implementation work lives. That is the harder, slower, and more important conversation.
What the frameworks will not do for you
If you read all six frameworks this week, you will be informed in a way that most of your peers are not. You will also still be missing 95% of what a real AI security program requires.
Here is what frameworks do not give you.
Framework gap | What it actually requires |
|---|---|
The frameworks do not tell you which systems in your organisation are using AI today | A live inventory exercise across every team, including shadow AI in SaaS tools |
The frameworks do not tell you who in your organisation owns the response when AI goes wrong | A named accountability registry mapped to specific AI systems |
The frameworks do not tell you how to interpret their requirements in the context of your specific data, regulatory exposure, and business model | Translation work that connects general principles to your specific situation |
The frameworks do not tell you how to make different teams (IT, security, compliance, legal, business units) actually work together on AI risk | Cross-functional design work that requires authority and political capital |
The frameworks do not tell you what to prioritise when you cannot do everything at once | Risk-based sequencing tied to your specific business |
The frameworks do not give you a defensible governance posture if a regulator audits you tomorrow | Documentation, audit trails, demonstrable controls, and incident response capability |
The frameworks do not tell you when to say no to a vendor pitch or an internal AI deployment | Judgement built from experience implementing this work across multiple organisations |
This is the gap that frameworks cannot close. The framework is the recipe. The implementation is the meal. Reading a recipe will not make you a cook, and reading NIST AI RMF will not give you a working AI security program.
The 5% of the work that is reading the frameworks is genuinely important. It is also the part that anyone can do for free in a weekend. The other 95% is the work that determines whether your AI security posture is real or theatre.
How to tell the difference between real work and PDF tours
If you are evaluating external help on AI security, here are five questions that separate the serious advisors from the framework-readers.
Question to ask | What a serious advisor says | What a framework-reader says |
|---|---|---|
"Can you give me an example of an AI security gap you found that the framework did not catch?" | Specific stories with named gaps and named fixes | Generic statements about "going beyond the framework" |
"What does your AI inventory process actually look like in practice?" | A concrete methodology with examples, timeline, and deliverables | Vague references to "discovery workshops" |
"What does your implementation timeline look like, and what are the most common things that slip?" | Honest assessment with specific risks and mitigations | "It depends on the engagement" |
"Can you describe an engagement where you recommended NOT deploying AI?" | At least one specific story | Awkward silence or generic answers |
"Who on your team will actually be doing the work, and what is their experience?" | Specific named people with specific named credentials | Marketing materials about "our team of experts" |
If you cannot get specific answers to these five questions, the advisor is probably running a PDF tour. If you can, you are probably looking at someone doing the real work.
This is the same screen you would apply to a lawyer, a doctor, or a financial advisor. Domain expertise is real. Cargo-cult expertise is also real. The questions above separate them.
The honest bottom line
The frameworks are useful. The frameworks are also free. Read them.
Reading them will get you 5% of the way to a defensible AI security posture. The other 95% is the implementation work that no framework can do for you.
Some of that implementation work you can do internally if you have the right people. Some of it you will need outside help for. Either way, the framework is the input. The defensible AI security posture is the output. They are not the same thing.
If you want to start with the framework knowledge, the links above will take you there for free.
