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.
AI Governance vs AI Compliance
AI compliance is closely related to AI governance, but the two are not the same. While 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 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 important?
AI adoption is surging – 85% of organizations now use managed or self-hosted AI services –but governance hasn’t kept pace. This gap introduces serious risks, especially as AI systems rely on sensitive data and rapidly evolving code.
Protecting sensitive data is a foundational compliance concern. AI models require large volumes of information, making alignment with privacy regulations like GDPR, HIPAA, and CCPA essential. Principles like data minimization, storage limitations, and integrity help reduce exposure.
Reducing cyber and cloud risk is another key benefit. As AI opens new attack surfaces, compliance frameworks help embed security into development pipelines. In fact, Gartner highlights AI-enabled cyberattacks and control failures as top audit priorities in 2024.
Driving responsible and ethical AI depends on clear regulatory guardrails. Compliance ensures AI systems are designed, developed, and deployed with transparency, fairness, and accountability in mind.
Building trust with customers and regulators is non-negotiable. Responsible AI use is now a reputational issue. Meeting compliance standards shows that your organization takes safety, privacy, and ethical risks seriously.
Recent enforcement actions underscore this shift. In 2023, OpenAI faced a temporary ban in Italy over GDPR violations, while the U.S. Executive Order on AI introduced mandatory risk assessments for high-impact models. Regulatory pressure is rising—AI compliance is no longer optional.
State of AI Security Report 2025
As AI compliance becomes increasingly critical, understanding the evolving landscape of AI security is essential. Dive deeper into the latest trends and challenges with Wiz’s State of AI Security Report 2025. Discover how organizations are balancing innovation with governance and security in the cloud.
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Who's Responsible for AI Compliance in an Organization?
AI compliance is not owned by a single team. It requires collaboration across security, legal, governance, and engineering to ensure that AI systems are secure, ethical, and aligned with regulatory expectations. Here are the key stakeholders involved:
Governance, Risk, and Compliance (GRC)
GRC teams define internal compliance frameworks and map them to external regulations like the EU AI Act or NIST AI RMF. They coordinate risk assessments, audit readiness, and policy enforcement across the organization.
Legal and Privacy Teams
Legal teams manage regulatory risk and contractual obligations. Privacy teams ensure that personal data used in model training, inference, or storage complies with data protection laws and internal privacy policies.
Security and AppSec Teams
These teams are responsible for protecting AI systems from exposure or abuse. They assess risk across AI supply chains, secure model pipelines, and monitor for data leakage, model tampering, or unsafe third-party integrations.
Machine Learning and Data Science Teams
As the builders of AI systems, these teams are responsible for documenting model behavior, data lineage, and fairness controls. They are key to ensuring technical compliance with responsible AI practices.
AI Product or Program Owners
When AI is embedded into customer-facing products or internal tools, product owners or program leads coordinate compliance across teams. They ensure requirements are built into workflows and that ownership is clearly assigned.
Top AI Compliance Frameworks and Regulations
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.
The U.S. AI Bill of Rights
An emerging reference point in AI compliance discussions is the AI Bill of Rights, introduced by the U.S. White House Office of Science and Technology Policy. While not yet legally binding, this framework provides important guidance for ethical AI usage, outlining five core principles:
Safe and Effective Systems: AI systems should minimize harm and perform reliably.
Algorithmic Discrimination Protections: AI solutions must avoid bias and discriminatory outcomes.
Data Privacy: Individuals should maintain clear control and transparency over their personal data usage in AI.
Notice and Explanation: Users deserve transparency regarding AI decisions and operations.
Human Alternatives and Oversight: AI systems must include human oversight and offer alternatives to automated decisions.
Update – July 2025: The Trump administration has rescinded the Biden-era Executive Order on AI, effectively shelving the AI Bill of Rights and its associated principles. While the document hasn’t been formally repealed, it no longer guides federal policy. In its place, the government is pursuing an innovation-first strategy focused on growth and deregulation. Organizations should monitor both federal shifts and growing state-level requirements to stay ahead of compliance risks.
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 compliance to their sector-specific requirements while also adhering to broader AI security and privacy frameworks.
25% of organizations don’t know what AI services are running in their environments, underscoring a critical visibility and governance challenge.
Wiz AI Security Readiness Report
Key Components of a Strong AI Compliance Strategy
A strong AI compliance strategy requires a combination of governance, technical visibility, and consistent execution across teams. These are the essential building blocks:
1. Clear governance framework
Establish clear policies, roles, and decision-making processes for how AI systems are developed, deployed, and monitored. You can adopt a framework like NIST AI RMF or tailor one to your organization’s needs – the key is consistency and accountability.
2. AI Bill of Materials (AI-BOM)
An AI-BOM tracks all models, datasets, tools, and third-party services in your environment. It helps teams understand what AI systems exist, where data comes from, and how components interact – all critical for compliance, security, and audit readiness.
3. Regulator alignment
AI regulations are moving fast. Staying aligned with legal and compliance teams – and engaging directly with regulators when possible – helps reduce uncertainty and ensures you're meeting current and emerging requirements.
4. Purpose-built AI security tools
AI-specific risks require AI-specific tools. Look for capabilities like explainability, bias detection, model validation, and secure deployment. An AI security posture management (AI-SPM) solution can unify these signals and help teams prioritize action.
5. Cloud-native compliance practices
Most modern AI workloads run in the cloud. Use compliance tools that are built for cloud platforms like AWS, Azure, and Google Cloud – not repurposed from on-prem environments. Many providers now offer AI-specific compliance controls for transparency, data protection, and auditability.
6. Training and awareness
Compliance depends on people as much as policy. Make sure developers, data scientists, and stakeholders understand AI risks and responsibilities – and keep training practical, recurring, and relevant to their roles.
7. Full AI ecosystem visibility
You can’t secure what you can’t see. Maintain real-time visibility into all AI components – models, data pipelines, access paths, and third-party integrations – to eliminate blind spots and support effective oversight.
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 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.