Daviyon DanielsWhen someone mentions "NIST compliance" or "CIS Controls" in a meeting, it is natural to nod along while privately wondering what any of it means in practical terms. Security frameworks have a reputation for being dense, technical, and inaccessible. But at their core, they answer a simple question: are we doing the right things to protect our organization?
This guide explains the most relevant AI security frameworks in plain language, what they cover, and why they matter for your business.
What Is a Security Framework?
A security framework is an organized collection of best practices, guidelines, and controls that help organizations manage risk. Think of it as a comprehensive checklist developed by experts who have studied what goes wrong at thousands of organizations and distilled that knowledge into actionable steps.
Frameworks do not tell you exactly what software to buy or which settings to configure. They describe the outcomes you should achieve: protect sensitive data, control who can access what, detect problems quickly, and recover when things go wrong. How you achieve those outcomes depends on your organization's size, industry, and technical environment.
The Frameworks That Matter for AI Security
Several established frameworks have evolved to address AI-specific risks. Here are the ones most relevant to organizations managing AI adoption.
NIST Cybersecurity Framework (CSF 2.0)
Who created it: The U.S. National Institute of Standards and Technology, a federal agency that develops technology standards.
What it covers: The NIST CSF organizes security into six core functions: Govern, Identify, Protect, Detect, Respond, and Recover. Each function contains categories and subcategories that describe specific security outcomes.
Why it matters for AI: The framework's Govern function, added in version 2.0, directly addresses organizational risk management decisions, including those related to AI adoption. It helps you think through questions like: who is responsible for AI risk decisions, how do we evaluate new AI tools before deployment, and what policies govern AI usage across the organization?
In practical terms: If someone asks whether your organization follows NIST CSF, they want to know that you have a structured approach to identifying risks, protecting assets, detecting threats, and recovering from incidents. It is the most widely referenced framework in the United States and increasingly recognized internationally.
CIS Controls v8
Who created it: The Center for Internet Security, a nonprofit organization that publishes consensus-based security best practices.
What it covers: CIS Controls is a prioritized set of 18 security controls, ordered by effectiveness. Unlike NIST's broader framework, CIS Controls are more prescriptive. They tell you specifically what to do, starting with the most impactful actions.
Why it matters for AI: Several CIS Controls directly address the risks created by AI adoption. Control 2 (Software Asset Inventory) helps you track which AI tools are in use. Control 3 (Data Protection) addresses how data flows to AI services. Control 6 (Access Management) governs who can use which AI tools. Control 14 (Security Awareness Training) covers educating employees about safe AI usage.
In practical terms: CIS Controls are often described as "what to do first." If you are starting from scratch with AI security, CIS gives you a prioritized path. Start with the fundamentals, then build toward more advanced controls as your program matures.
ISO 27001
Who created it: The International Organization for Standardization (ISO), in partnership with the International Electrotechnical Commission (IEC).
What it covers: ISO 27001 specifies the requirements for establishing, implementing, maintaining, and continually improving an information security management system (ISMS). It includes a comprehensive set of controls in its Annex A, covering everything from access control to supplier relationships.
Why it matters for AI: ISO 27001 is the gold standard for organizations that need to demonstrate security maturity to clients, partners, or regulators. Its supplier management controls are particularly relevant for AI, since most organizations use AI tools provided by third parties. The standard requires you to assess and manage the risks introduced by these suppliers.
In practical terms: If a client or partner asks whether you are "ISO 27001 certified," they want assurance that you have a formal, audited security management system. Certification involves an external audit and ongoing surveillance. It is a significant investment, but it carries weight in enterprise sales and regulated industries.
NIST AI Risk Management Framework (AI RMF)
Who created it: NIST, specifically to address AI-related risks.
What it covers: Published in 2023, the AI RMF provides guidance for managing risks throughout the AI lifecycle. It is organized around four functions: Govern, Map, Measure, and Manage. Unlike the frameworks above, it was built from the ground up for AI-specific risks.
Why it matters for AI: This is the most directly relevant framework for AI governance. It addresses risks like bias, lack of transparency, data privacy, and the unique challenges of AI systems that learn and evolve over time. It also covers third-party AI risk, which applies to every organization using commercial AI tools.
In practical terms: The AI RMF is newer and not yet as widely adopted as NIST CSF or ISO 27001, but it is gaining traction quickly. If your organization is building an AI governance program from scratch, this framework provides the most AI-specific guidance.
How Frameworks Work Together
These frameworks are not competing alternatives. They are complementary layers. Many organizations use NIST CSF as their overarching risk management approach, CIS Controls for prioritized implementation guidance, ISO 27001 for formal certification, and the NIST AI RMF for AI-specific governance.
The key is to start with one framework that matches your immediate needs and expand from there. You do not need to implement everything at once, and you do not need to achieve perfect compliance before you see value.
Where Assessments Fit In
A security assessment measures your current practices against a framework's requirements and identifies the gaps. It is the practical bridge between "we should do something about AI security" and "here is exactly what we need to do, in what order."
At Ayliea, our assessment methodology evaluates your organization across multiple frameworks simultaneously. Rather than conducting separate assessments for NIST, CIS, and ISO compliance, we map your AI security posture across all applicable frameworks in a single engagement. The result is a unified view of where you stand and a prioritized roadmap for improvement.
Frameworks are not bureaucratic exercises. They are accumulated wisdom from organizations that learned hard lessons so you do not have to. Understanding them, even at a high level, puts you in a much stronger position to make informed decisions about your AI security program.