What is AI compliance?
Artificial intelligence (AI) compliance describes the adherence to legal, ethical, and operational standards in AI system design and deployment. AI compliance can be pretty complicated. It’s basically a tangled web of frameworks, regulations, laws, and policies set by governing bodies at federal, local, and industry levels. According to Gartner, half of the world’s governments expect enterprises to follow various laws, regulations, and data privacy requirements to make sure that they use AI safely and responsibly.
So here’s what you need to keep in mind: Maintaining a healthy AI compliance posture is more than just ticking boxes. View it as a core aspect of modern technology-driven operations, a key ingredient in fostering stakeholder trust, and the secret to strong AI security in the cloud. And remember that 2025's avalanche of AI regulations and frameworks means that you can’t afford to procrastinate.
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The Role of AI Governance in AI Compliance
AI compliance is closely related to AI governance, but the two are not the same. While AI compliance ensures adherence to legal, ethical, and security standards, AI governance is a broader concept that includes risk management, oversight, and the strategic deployment of AI technologies.
A well-structured AI governance framework ensures that AI models align with company policies, regulatory mandates, and ethical principles while maintaining robust security. Compliance, on the other hand, focuses on meeting external regulatory and industry standards like the EU AI Act or GDPR.
By integrating AI compliance within a governance framework, organizations can create AI systems that are not only legal but also secure, fair, transparent, and accountable.
Why is AI compliance crucial in 2025?
Ensuring data privacy: To drive AI initiatives, you need heaps of data. That’s why step one in AI compliance is to protect sensitive enterprise and customer data. Specifically, you need to ensure that AI-related data and data management practices follow frameworks like GDPR, HIPAA, and CCPA. Principles like data minimization, storage limitations, confidentiality, and integrity can keep your data safe. Remember that even the biggest names in the AI sphere are subject to compliance violations, as seen in OpenAI's data privacy rules breach.
Mitigating cyber risks: For every benefit that AI provides to a company, there are new attack vectors and security headaches. In fact, Gartner says that the two biggest jumps in audit coverage priorities for chief audit executives from 2023 to 2024 were AI-enabled cyberattacks and AI control failures. AI compliance can help you systematically sort out AI-adjacent cyber and cloud risks. It also helps embed risk reduction and management into every phase of your AI development pipelines.
Upholding AI ethics: Keeping a solid handle on AI compliance can help you navigate the ethical minefield of AI adoption. Implementing and using regulatory guardrails can guarantee ethical practices across the AI lifecycle—from initial designs and concepts to technical controls across AI development and deployment processes. To sum it up, by acing compliance, you’re sure to use AI in a way that’s safe, sustainable, ethical, and useful for everyone.
Gaining customer trust: When it comes to AI, you're in the spotlight. Customers are curious about how enterprises use AI technologies, and the reality is, you will be judged on how safely, ethically, and responsibly you use AI. By adhering to AI compliance standards, you can create secure, powerful, and highly resilient AI products and services while giving your customers guarantees on safety, security, and accountability.
Fostering continuous AI improvement: Almost every business is locked in a tight battle for AI-driven competitive advantage, so it's important for you to continuously evolve and improve AI projects. This is almost impossible if you're constantly held back by security, legal, and operational roadblocks. By introducing and sticking to strict AI regulatory standards, you can build a springboard to take your AI projects to the next level.
Promoting transparency and answerability: As you weave AI-driven autonomous and semi-autonomous decision-making into your operations, you’re going to run into some difficult questions: What are those decisions based on, who is accountable for those decisions, and what happens when AI systems make wrong decisions? AI compliance can help you understand AI decisions and clearly explain them if customers, regulators, or other key stakeholders come knocking.
Satisfying proactive regulators: As a response to the proliferation of AI technologies and the speed and scale of cyberattacks, regulators and supervisors are going on the offensive with AI security and cloud compliance. That means you'll likely have to keep up with many crisscrossing and occasionally conflicting AI laws and regulations, especially if you operate across sectors or countries. With this many AI laws to reckon with, you simply cannot afford to ignore AI compliance in 2025.
In 2023, OpenAI was investigated by Italy’s data protection authority for allegedly violating GDPR due to insufficient transparency about how ChatGPT collects and processes user data. The regulator temporarily banned ChatGPT in Italy and required OpenAI to implement stronger compliance measures before reinstating access.
Similarly, in the U.S. Executive Order on AI (2023), the federal government introduced mandatory risk assessments for AI models used in critical sectors, signaling a shift toward stricter AI compliance. These cases highlight how regulators are actively monitoring AI systems—and why organizations must proactively address AI compliance to avoid penalties or operational disruptions.
Top AI Compliance Frameworks and Regulations (2025)
This is a good time to remind you that AI compliance isn't just about new regulations and that your existing cloud compliance obligations, such as GDPR, are just as important. Look at it this way: If your AI systems use more data than they need to, you might be in violation of existing as well as emerging AI regulations. Keep that in mind while we take a look at some of the most important AI frameworks, laws, and regulations.
EU AI Act
The EU AI Act is a good starting point because it's widely considered to be the first complete AI regulation. Enforced by the EU to secure the use of AI across different spheres, the AI Act scales regulations based on risk severity. AI systems with minimal risk will be assessed, but the regulations might not be too harsh. On the other hand, AI systems with high risk levels will be thoroughly vetted and analyzed before they can hit the market.
Plus, any company that uses generative AI (GenAI) will have to follow some pretty stringent transparency obligations. But remember that the AI Act isn't designed to stifle innovation but rather to encourage responsible AI-driven growth. For example, the AI Act mandates national authorities must set up testing environments for smaller enterprises to experiment with AI.
NIST AI RMF
Next up is NIST's AI Risk Management Framework (AI RMF). NIST AI RMF is more of a guide than a rule and is designed to help pretty much anyone who wants to develop AI systems. The main objective of NIST AI RMF is to take the edge off emerging AI risks and help companies strengthen and secure their AI systems.
AI RMF covers the entire AI development lifecycle with four major components: Govern, Map, Measure, and Manage. It also acknowledges that AI security goes beyond technical functions and extends into social and ethical issues like data privacy, transparency, fairness, and bias. One of the most useful features of NIST AI RMF is that its AI security best practices can be used by a wide range of enterprises, from the smallest startups to the most prominent multinational corporations.
UNESCO’s Ethical Impact Assessment
A supplementary resource to UNESCO's "Recommendation on the Ethics of Artificial Intelligence" publication, the Ethical Impact Assessment is a useful framework for any company developing AI systems and trying to establish a strong AI governance posture. In simpler terms, the Ethical Impact Assessment helps identify AI risks and enforce AI security best practices.
It touches on the entire AI development lifecycle, from ensuring that AI systems use high-quality data and transparent algorithms to supporting audit requirements and setting up diverse and capable AI teams. A word of advice: To make the most of the Ethical Impact Assessment, keep it up-to-date because assessments can become stale over time.
ISO/IEC 42001
Let's bring this home with ISO/IEC 42001. This international standard sets out obligations for building, managing, securing, and continuously improving AI management systems. It's a useful standard for anyone who wants to strike a perfect balance between strong AI security best practices / governance protocols and high-octane development and deployment.
ISO has many similar standards and resources that businesses can pair with 42001, including:
ISO/IEC 22989: A glossary of important AI concepts
ISO/IEC 23894: An AI risk management resource
ISO/IEC 23053: A framework for AI and machine learning (ML)
AI compliance is not one-size-fits-all—regulations vary by industry. Organizations operating in finance, healthcare, and cybersecurity must adhere to specialized AI regulatory requirements:
Financial Services: AI-driven risk assessments, fraud detection, and credit scoring models must comply with Basel III, the Fair Lending Act, and the SEC's AI risk guidelines.
Healthcare & Life Sciences: AI compliance in this sector must meet HIPAA (U.S.), the EU’s AI Act, and FDA regulations for AI-powered medical diagnostics and research applications.
Cybersecurity & Defense: The U.S. NIST AI RMF, EO 13960 (Trustworthy AI in Government), and CISA’s AI security guidance govern AI’s use in national security and critical infrastructure.
Businesses must map AI compliance to their sector-specific requirements while also adhering to broader AI security and privacy frameworks.
What are the key components of a powerful AI compliance strategy?
Strong governance framework: If you want to establish strong AI security best practices, governance policies, and roles and responsibilities, make sure that you follow a strong governance framework. You can use one of the frameworks we listed in the previous section or mix-and-match frameworks to concoct something unique. Whichever way you go, the most important thing is that your AI security and compliance strategy is bound by a well-defined framework.
AI bill of materials (AI-BOM): An AI-BOM is basically a long list of all the working parts and components of your AI development lifecycle. We’re talking about training, applications, hardware, archives of iterations, and key metrics. Why should you care about an AI-BOM for AI compliance? Because it allows key AI teams to map and trace your entire AI ecosystem, a crucial requirement to ensure AI security, compliance, and performance.
Dialogue with policymakers: With AI compliance, it’s dangerously easy to drift off course. That's because AI technologies churn at remarkable speeds, and regulators constantly roll out new regulations and updates. The best way for you to stay on top of these changes is to start talking with supervisors and regulators. In other words, don't play the guessing game with AI compliance—simply ask.
AI security tools: If you want to build a strong AI compliance posture, you need to have the right AI security tools. The AISecOps (also known as the MLSecOps) market has a deep pool of AI security tools that you can choose from. But remember that the best way to unlock and unify these tools, including explainable AI tools and vulnerability management tools, is to use a comprehensive AI security posture management (AI-SPM) platform. More on that soon.
Strong cloud compliance: Soon, the terms “cloud compliance” and “AI compliance” will mean the same thing. That’s how connected they are. For businesses with strong cloud moorings, it's important to use cloud compliance tools that were built for the cloud rather than repurpose legacy compliance tools. It's the only way to drive cloud-based AI projects while staying on top of AI compliance standards. AI and cloud compliance are becoming inseparable. As enterprises move AI workloads to cloud platforms like AWS, Azure, and Google Cloud, compliance teams must ensure that AI-specific security risks are addressed within cloud governance policies.
Leading cloud providers now offer AI compliance tools to help businesses manage these challenges:
AWS AI Risk Management: Supports AI security and compliance with built-in guardrails for model transparency and bias detection.
Azure AI Compliance Hub: Helps enterprises align AI projects with global regulatory standards (e.g., GDPR, HIPAA).
Google AI Principles & Responsible AI Tools: Offer governance controls for ensuring fairness, privacy, and AI accountability.
A strong cloud compliance strategy ensures that AI models are deployed securely while maintaining regulatory adherence across AI-driven applications.
AI training and awareness: Having an AI compliance strategy is one half of the puzzle, but executing it is what really matters. To do that, you need your teams and key stakeholders to be on the same page when it comes to AI security, compliance, and risks. Your next move is simple: Add AI compliance sessions to your calendar, but make sure that training and awareness programs are gamified, engaging, and regularly updated.
AI ecosystem visibility: Last but not least, to ensure AI compliance, you need to know what your AI stack looks like. This is only possible if you have complete visibility into your cloud-based AI environments. Even the smallest blind spot can lead to risks, breaches, and major AI compliance violations. Always stay vigilant and keep a close watch on your AI resources.
How Wiz AI-SPM can support your AI compliance strategy
AI security and AI compliance are often discussed together, but they serve different functions.
AI security focuses on protecting AI models, data, and pipelines from cyber threats, adversarial attacks, and unauthorized access. This includes securing AI training datasets, model explainability, and attack path mitigation.
AI compliance ensures that AI systems meet legal, regulatory, and ethical obligations, such as GDPR, the EU AI Act, and ISO standards.
However, AI security is a foundational pillar of AI compliance. If an AI system lacks security (e.g., is vulnerable to data poisoning attacks or model extraction), it cannot meet compliance requirements. Organizations must integrate security-first compliance strategies—ensuring that AI systems are both legally compliant and cyber-resilient.
AI compliance requires real-time visibility into AI assets, risks, and regulatory requirements. Wiz AI-SPM (AI Security Posture Management) provides full-stack insight into AI security risks, compliance gaps, and attack surface exposure across cloud-based AI environments.
Key benefits of Wiz AI-SPM for AI compliance:
AI Bill of Materials (AI-BOM): Gain end-to-end visibility into all AI components (models, datasets, APIs, training pipelines) to ensure compliance with data security policies.
Real-time compliance risk detection: Identify and remediate AI misconfigurations, unauthorized access, and regulatory non-compliance before they become violations.
Attack path analysis for AI environments: Detect vulnerable AI models, cloud misconfigurations, and lateral movement risks within AI infrastructures.
Automated compliance mapping: Compare AI security postures against GDPR, ISO/IEC 42001, NIST AI RMF, and industry-specific regulations.
Wiz AI-SPM ensures that AI-driven enterprises can innovate at scale while maintaining compliance with evolving AI regulations.
Get a demo now to test out Wiz AI-SPM’s cutting-edge features and reinforce your cloud compliance posture.
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