What is AI compliance?
AI compliance is your adherence to legal, regulatory, and industry standards that govern the responsible development, deployment, and maintenance of AI technologies.
Notable compliance standards include the EU AI Act and GDPR. As new AI regulations emerge, the global landscape continues to evolve. Examples include the African Union’s Continental AI Strategy and Canada’s current proposition of the Artificial Intelligence and Data Act (AIDA).
AI compliance vs. AI governance
While AI compliance is closely related to AI governance, the two are not the same:
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.
Here’s a quick comparison of both practices:
| Aspect | AI governance | AI compliance |
|---|---|---|
| Focus | Risk management, oversight, strategic deployment, and ethical use | Requirements from governing bodies and industry-specific standards |
| Scope | Internal policies, corporate governance, risk assessments, and strategic long-term AI practices | Audit readiness and alignment with regulatory frameworks |
| Objective | Responsible and ethical AI management | Legal risk prevention and stakeholder assurance |
| Approach | Monitoring AI across the SDLC | Documenting and auditing AI-related activities |
| Example | Aligning models with ethics, evaluating risks, and forming oversight committees | Performing assessments, maintaining documentation, and responding to audit requests |
By integrating compliance within a governance framework, your organization can create AI systems that are not only legal but also secure, fair, transparent, and accountable.
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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Why AI compliance matters in 2025
According to Gartner, by 2026, half of the world’s governments expect enterprises to adhere to AI laws, regulations, and data privacy requirements that ensure the safe and responsible use of AI. Now is the perfect time to embed compliance practices and systems to meet these and emerging standards.
Maintaining compliance is a core aspect of modern technology-driven operations. It fosters stakeholder trust and is essential for strong AI security in a cloud environment. As critical as AI compliance is now, with 85% of organizations using AI services, it will become even more important as AI adoption continues to grow.
Unfortunately, governance and compliance have struggled to keep pace with the technology’s rapid evolution. A lack of awareness, poor prioritization, and technical governance gaps are introducing serious risks, especially considering AI systems rely on sensitive data and evolving code. Here are some key reasons to prioritize compliance:
Sensitive data is at risk: AI models require large volumes of information, making alignment with privacy regulations like GDPR, HIPAA, and CCPA essential. Principles such as data minimization, storage limitations, and integrity help reduce exposure.
Cyber and cloud risk continues to grow: As AI opens new attack surfaces, compliance frameworks help embed security into development pipelines. Gartner identified AI-enabled cyberattacks and misinformation as top emerging risks in 2024.
AI practices need ethical guardrails: Compliance ensures organizations design, develop, and deploy AI systems with transparency, fairness, and accountability in mind.
Organizations need to build trust: Responsible AI use is now a reputational issue. Meeting compliance standards shows you take safety, privacy, and ethical risks seriously.
Who owns AI compliance?
No single team owns AI compliance. It requires collaboration across security, legal, governance, and engineering to ensure AI systems are secure, ethical, and aligned with regulatory expectations. Key stakeholders involved in this process include:
Governance, risk, and compliance teams: These teams define internal compliance frameworks and map them to external regulations like the EU AI Act or NIST AI RMF. They also coordinate risk assessments, audit readiness, and policy enforcement.
Legal and privacy teams: Legal teams manage regulatory risk and contractual obligations. Privacy teams ensure the use of personal data in model training, inference, and storage complies with data protection laws and internal privacy policies.
Security and AppSec teams: These stakeholders are responsible for protecting AI systems from exposure or abuse by assessing risk across AI supply chains, securing model pipelines, and monitoring for data leakage, model tampering, and 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. This makes them essential for ensuring technical compliance with responsible AI practices.
AI product or program owners: Product owners and program leads coordinate cross-team compliance, embed requirements into workflows, and assign clear ownership.
Top AI compliance frameworks and regulations
AI compliance involves more than new regulations. Comprehensive compliance means maintaining security with current cloud obligations, like GDPR, even as AI innovations emerge. Look at it this way: If your AI systems use more data than necessary, you could be violating existing regulations.
Keep that in mind as you review these critical AI frameworks, laws, and regulations.
The EU AI Act
Many cybersecurity experts view the EU AI Act as the first complete AI regulation. The EU implements it to secure AI use across sectors and scales regulations based on risk severity.
The Act adopts a tiered regulatory approach: AI systems with minimal risk face only basic requirements, while high-risk systems undergo thorough vetting before deployment. Additionally, any company that uses generative AI must follow stringent transparency obligations. The EU, however, is constrained by the practical capacities of individual member states when it comes to enforcement.
The AI Act aims to foster responsible AI innovation, not hinder it. For example, it mandates that national authorities provide testing environments for smaller enterprises to experiment with AI.
The US AI Bill of Rights
An emerging reference point in AI compliance discussions is the AI Bill of Rights, a legally non-binding framework introduced by the United States White House Office of Science and Technology Policy. It provides guidance for ethical AI usage across 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 the use of their personal data 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.
July 2025 update: The Trump administration has rescinded the previous administration’s executive order on AI, effectively shelving the AI Bill of Rights and its associated principles. Although the current administration hasn’t formally repealed the document, it no longer guides federal policy. In its place, the US government is pursuing an innovation-first strategy that focuses on growth and deregulation. Continuously monitor both federal shifts and developing state-level requirements to stay up to date with this evolving legislation.
NIST AI RMF
NIST's AI Risk Management Framework (AI RMF) is more of a guide than a rule, designed to help almost anyone who wants to develop AI systems. Its goal is to mitigate emerging AI risks and help companies strengthen and secure their AI systems.
The AI RMF covers the entire AI development lifecycle through four major components: Govern, Map, Measure, and Manage. It acknowledges that AI security extends beyond technical functions into social and ethical issues like data privacy, transparency, fairness, and bias. A key feature of the framework is its flexibility, allowing teams to use AI security best practices regardless of company size.
UNESCO’s Ethical Impact Assessment
A supplementary resource to UNESCO's “Recommendation on the Ethics of Artificial Intelligence,” the Ethical Impact Assessment is a framework designed to help any company developing AI systems establish a strong AI governance posture. It covers the entire AI development lifecycle, from ensuring AI systems use high-quality data and transparent algorithms to supporting audit requirements and setting up diverse, capable AI teams. However, for the Ethical Impact Assessment to remain effective, teams must continually update it.
ISO/IEC 42001
This international standard outlines obligations for building, managing, securing, and continuously improving AI management systems. It's useful for balancing strong AI security best practices, governance protocols, and agile development and deployment.
ISO provides complementary standards and resources, 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
Compliance and its nuances per organization
AI compliance is not one-size-fits-all. Regulations vary by industry, so organizations in finance, healthcare, and cybersecurity must adhere to the following 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 SEC AI risk guidelines.
Healthcare and life sciences: AI compliance in this sector must meet HIPAA (US), the EU AI Act, and FDA regulations for AI-powered medical diagnostics and research applications.
Cybersecurity and defense: The NIST AI RMF, EO 13960 (Trustworthy AI in Government), and CISA’s AI security guidance govern AI use in national security and critical infrastructure.
Businesses must map compliance to 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’s AI Security Readiness report
Key components of a powerful AI compliance strategy
A robust AI compliance strategy requires governance, technical visibility, and consistent execution across teams. Below are the essential building blocks, with maturity stages that match NIST AI RMF functions:
Clear governance framework and consistent reviews: Establish clear policies, roles, and decision-making processes for how to develop, deploy, and monitor AI systems. You can adopt a framework like NIST AI RMF or tailor one to your needs—the key is consistency and accountability.
NIST AI RMF function: Govern
Alignment and AI Bill of Materials (AI-BOM): Align your compliance strategy with standards, internal policies, and long-term growth. You can use an AI-BOM, like Wiz’s, to track all models, datasets, tools, and third-party services in your environment. This helps teams understand which AI systems exist, where data comes from, and how components interact—all of which are critical for compliance, security, and audit readiness.
NIST AI RMF function: Map
Purpose-built AI security tools: AI-specific risks require AI-specific tools. Look for capabilities like explainability, bias detection, model validation, and secure deployment. Wiz’s AI security posture management (AI-SPM) solution can unify these signals and help teams prioritize action.
NIST AI RMF function: Manage
Cloud native compliance practices: Since most modern AI workloads run in the cloud, use compliance tools built for cloud platforms—such as AWS, Azure, and Google Cloud—rather than repurposing tools from on-prem environments. Many providers now offer AI-specific compliance controls for transparency, data protection, and auditability.
NIST AI RMF function: Measure
Full AI ecosystem visibility: You can’t secure what you can’t see. Maintaining real-time visibility into all AI components—models, data pipelines, access paths, and third-party integrations—is crucial for eliminating blind spots and supporting effective oversight.
NIST AI RMF function: Measure and manage
Use Wiz for automated compliance. It maps over 100 built-in frameworks, including NIST, HIPAA, HITRUST, SOC 2, and CIS.
AI compliance in action: Real cases and implementation steps
Putting AI into practice requires strategic planning and execution. Below are some steps you can take:
1. Define your compliance scope and build your AI-BOM: Review the current state of your AI systems. Identify the models, services, datasets, and third-party tools that require compliance so you can ensure AI security readiness.
Wiz’s AI-BOM helps teams map ownership and inventory of these assets.
2. Embed policies as code into CI/CD pipelines: Integrate compliance checks into your dev workflows to identify violations early and stop noncompliant AI models from deploying.
Use tools like Wiz Code to define and enforce security and compliance policies throughout the entire SDLC, including CI/CD environments.
3. Automate framework mapping and continuous scanning: Streamline compliance by automating alignment with relevant frameworks and standards while monitoring for risk and drift.
Leverage Wiz to map your systems against frameworks and scan for misconfigurations or policy violations.
4. Implement regular auditing and reporting processes: Schedule routine compliance reviews and use a CNAPP to generate audit-ready reports on your security posture.
Wiz’s CNAPP offers the reporting and agentless scanning you need to maintain a compliance-ready environment.
AI Security Posture Assessment Sample Report
See how an AI security assessment uncovers hidden risks like shadow AI, misconfigurations, and exposure paths while showing which insights help teams keep AI innovation secure.
Material Security implements best practices for visibility
Material Security, a platform for Google Workspace and Microsoft 365, recognized the evolving cloud landscape and needed to enhance visibility and minimize the use of siloed tools. The company adopted Wiz to implement multi-cloud visibility, threat detection, and personalized insights, which helped it optimize response and collaboration.
This integration reduced investigation time with context, minimized manual effort on threat detection engineering, and enabled implementation of graph-based queries for proactive investigation. As a result, the company can address evolving threats in AI and cloud security while maintaining compliance.
Synthesia tackles AI compliance head-on
Synthesia, a video generation platform, has known the benefits and risks of AI since its founding in 2017. To meet compliance standards for its AI technology, it needed contextualized alerts that prioritize risks, reduce alert fatigue, and equip engineers to patch issues quickly.
As Martin Tschammer, head of security at Synthesia, said, “Our previous security solution attempted to contextualize alerts, but the information provided was unclear. Without that, we weren’t able to prioritize remediation.”
Synthesia adopted Wiz to provide prioritized alerts with context. With this change, its team can now focus on the biggest vulnerabilities first and gain full visibility into risks across its infrastructure.
“With Wiz,” Tschammer adds, “we can enable our engineers and development teams to confidently resolve issues on their own.”
Simplify your AI compliance with Wiz’s AI-SPM
AI compliance requires real-time visibility into AI assets, risks, and regulatory requirements. However, many organizations struggle to gain comprehensive insight across cloud-based environments, making it challenging to meet evolving standards and manage risk effectively. Wiz’s AI Security Posture Management (AI-SPM) provides full-stack insight into AI security risks, compliance gaps, and attack surface exposure.
Key benefits of Wiz AI-SPM for AI compliance include:
Full stack visibility and AI-BOM: Gain end-to-end visibility into all AI components (like models, datasets, APIs, and training pipelines) to ensure compliance with data security policies and operations.
Real-time compliance risk alerts: Identify and remediate AI misconfigurations, unauthorized access, and regulatory noncompliance before they become violations.
AI-powered remediation: Leverage customized guidance and automatically fix issues to prevent them from escalating.
Automated compliance mapping: Compare AI security postures against GDPR, ISO/IEC 42001, NIST AI RMF, and industry-specific regulations to ensure compliance.
Wiz's AI-SPM ensures AI-driven enterprises can innovate at scale while maintaining compliance with evolving AI regulations. Ready to test our cutting-edge features? Request a demo today to learn how you can reinforce your cloud compliance posture.