AI Compliance: What It Is, Why It Matters, and How to Achieve It

AI compliance refers to the process of ensuring that companies remain compliant with new and emerging regulatory and legal standards governing security and usage of artificial intelligence (AI) tools.  

As AI becomes more pervasive across industries, governing bodies across the globe have recognized the growing need to regulate its deployment, usage, and decision-making processes to avoid harm, bias, or unintended consequences. They have also begun to acknowledge the application of existing privacy and security laws to the new technologies and scenarios that AI have introduced.  

AI compliance is not just about following regulations but about building trust, protecting consumers, and ensuring the safe and responsible use of AI. It aims to mitigate risks that can arise from automated decisions, such as data breaches, biased outcomes, or unintended manipulation. 

Why is AI Compliance Important? 

Legal and Ethical AI Usage 

With the increasing presence of AI in daily operations, there is a legal requirement to ensure that AI systems abide by established laws, such as data protection regulations (GDPR) and anti-discrimination statutes. Ethical usage of AI helps protect individuals’ rights and avoids harmful consequences caused by biased or faulty algorithms. 

Strengthening Risk Mitigation 

Compliance helps in identifying potential risks in AI systems early on, preventing unintended outcomes. Through regular audits and assessments, organizations can address risks such as bias, privacy violations, and security breaches. 

Fostering Customer Trust 

AI compliance is crucial for building and maintaining customer trust. When businesses are transparent and adhere to regulations, consumers feel safer and are more likely to engage with AI-driven products or services. 

Privacy and Security Protection 

AI systems often rely on large datasets, which may contain sensitive personal information. Regulatory compliance ensures these systems protect user privacy and adhere to stringent data security protocols. 

Enhancing Data Protection 

AI compliance guarantees that data used for AI model training is handled securely and in line with data protection regulations like GDPR. This ensures the ethical use of data, preventing unauthorized access or misuse. 

Boosting Innovation and Adoption 

Clear regulatory frameworks for AI compliance provide a structure that encourages innovation. Companies feel more comfortable investing in AI technologies when they know the compliance standards to which they must adhere, thus fostering broader adoption of AI systems. 

Display Forward-Thinking Approach 

As new AI-specific standards and regulations emerge, more companies will look for their partners to demonstrate compliance. Your organization can demonstrate awareness and security consciousness around its AI usage by complying with these standards early, before clients begin requiring that compliance. 

Examples of AI-Specific Compliance Standards 

Overview of Prominent AI Regulatory Frameworks 

Various regions and industries have introduced AI regulatory frameworks aimed at governing the use of AI technologies. These frameworks often focus on issues like privacy, fairness, and accountability. 

ISO/IEC 42001 

ISO/IEC 42001:2023 is a new standard released by the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC) released what is considered one of the first international AI-specific compliance standards. Also known simply as ISO 42001, this new standard addresses common concerns around AI usage by organizations, including ethical, transparency, and model-training components. 

NIST AI Risk Management Framework 

The United States’ National Institute for Standards and Technology (NIST) has released its Artificial Intelligence Risk Management Framework to help organizations manage AI-related risk. This US-specific framework attempts to fill gaps that existing compliance standards may miss regarding new developments in generative AI.  

EU Artificial Intelligence Act 

The European Union’s AI Act is one of the first AI-specific regulations from a major regulator. The EU AI Act applies the principles of the EU’s General Data Protection Regulation (GDPR) to the new realities and risks presented by artificial intelligence usage.  

The EU AI Act is one of the most comprehensive AI-based regulatory frameworks, focusing on risk-based classification of AI systems and ensuring compliance in high-risk areas. 

ISO Standards for AI Compliance

What is ISO?

The International Organization for Standardization (ISO) develops voluntary, consensus-based standards that help organizations align on common definitions, processes, and controls. For digital and AI topics, ISO often publishes joint standards with the International Electrotechnical Commission (IEC). These documents don’t tell you how to code a model; they tell you how to manage risk, quality, and governance around it so auditors, customers, and regulators have a shared yardstick.

Why ISO Matters for AI

• A common language: Shared terms and life-cycle definitions for models, data, and ML pipelines reduce ambiguity across legal, security, and engineering teams.

• Governance and auditability: Management-system and risk standards give you show your work artifacts (policies, records, metrics) that map well to due diligence, MSA/security addenda, and regulator inquiries.

• Interoperability with regulators: Even when not mandated, ISO/IEC AI standards align with U.S. NIST’s AI Risk Management Framework and EU risk-based approaches, making them a pragmatic baseline for global programs.

IS ISO Compliance Required By Law?

ISO standards are voluntary, though effectively required when they are:

• Written into contracts by customers or partners.
• Referenced by regulators or used to demonstrate “reasonable” practices under risk-based laws.
• Adopted internally as policy (then auditors test you against them).
  Many organizations use ISO/IEC standards to show conformity with broader frameworks (e.g., NIST AI RMF) and to evidence diligence under the EU AI Act’s risk-management expectations.

Key ISO Standards for AI in Business

• ISO/IEC 42001:2023 — AI Management System (AIMS): Requirements to establish, operate, and continually improve AI governance (roles, policies, KPIs, controls). Think “ISO 27001 but for AI.” Ideal for program-level certification.
• ISO/IEC 23894:2023 — AI Risk Management: How to identify, assess, treat, and monitor AI-specific risks across the life cycle; harmonizes with ISO 31000 principles. Used for threat models, DPIAs/impact assessments, and risk registers.
• ISO/IEC 23053:2022 — Framework for AI Systems Using ML: A conceptual model of ML system components and flows; helpful for architecture diagrams and RACI across data, model, and runtime.
• ISO/IEC 22989:2022 — AI Concepts & Terminology: Canonical definitions to reduce policy drift and speed cross-functional reviews.
• ISO/IEC TR 24028:2020 — Trustworthiness in AI: Survey of techniques for transparency, robustness, safety, security, and privacy, useful for control catalogs and design reviews.

AI Legislation in the EU and U.S.

The EU Artificial Intelligence Act (AI Act)

The EU AI Act (published July 2024) is the world’s first comprehensive horizontal AI law, using a risk-based model with outright bans (prohibited AI), strict obligations for high-risk systems, and lighter duties for limited/minimal risk. Penalties scale with severity and revenue.

Key Aspects of AI Compliance 

Compliance with Existing Laws and Regulations 

AI systems must comply with existing legal frameworks, depending on the region and sector. These laws govern data usage, privacy protection, and discrimination prevention to ensure AI does not cause legal harm. 

Compliance with New and Emerging Standards 

As new standards are released to address raising security concerns with AI usage, those new standards may introduce new considerations and requirements. Demonstrating adherence to these AI-specific standards can bolster client confidence in your handling of AI tools and technologies. 

Challenges in Achieving AI Compliance 

Changing and Emerging Regulations 

Because governing bodies and compliance standards organizations are still grappling with the security implications of Artificial Intelligence, new standards are continually being released and edited to address security concerns. Keeping up with these changing standards requires dedicated attention and expertise. 

Shadow AI Usage 

Achieving compliance across an organization can be complex, as different departments may have varying levels of AI adoption and awareness of security responsibilities. Many may be using AI usage in non-compliant ways, especially if no clear policy exists.  

Risk Management Frameworks Limitations 

Traditional risk management frameworks may not be adequately equipped to handle the nuances of AI, such as algorithmic transparency and bias mitigation. 

Compliance Gaps with Third-Party Associates 

Ensuring third-party vendors and partners adhere to AI compliance standards is another challenge. Companies must extend compliance frameworks to cover their entire supply chain and external associates. 

Shortage of ‘Responsible’ AI Talent 

There is a growing demand for AI professionals with knowledge of compliance, ethics, and regulation. This shortage makes it challenging for organizations to ensure their AI systems are responsibly developed and monitored. 

Consequences of Non-Compliance 

Legal Consequences and Case Studies 

Failing to ensure that company AI usage complies with existing regulations – such as HIPAA or GDPR – can lead to significant legal consequences, including hefty fines, litigation, and enforcement actions from regulators. Recent cases have seen organizations face sanctions for violating data privacy and discriminatory outcomes in AI-driven decisions. 

Impact on Business Opportunities 

Falling short on AI compliance may also close the door to business opportunities, especially in highly regulated industries such as healthcare, finance, and government contracting. Companies in these industries will have increased pressure to ensure that organizations are equally applying relevant compliance standards, including AI usage.  

Historical Examples of AI Non-Compliance 

Privacy Concerns of Public GenAI Tools 

Many Generative AI (GenAI) users – especially those using free tools – may be unaware of the public nature of the data shared. For example, entering private client data into a public GenAI tool may unwittingly be exposing that data in a violation of privacy rules.  

Deepfakes and National Security Threats 

AI-generated deepfakes have raised significant concerns regarding misinformation and national security. Non-compliance with emerging regulations on deepfakes can result in serious legal and security consequences. 

AI-Powered Photo Editing and Data Protection Concerns 

AI-powered tools that manipulate images have raised questions about privacy, with concerns that manipulated images could be used without consent, leading to violations of data protection laws. 

How to Achieve AI Compliance? 

Achieving AI compliance is not a one-time effort but an ongoing process. It involves preparation, implementation, and continuous monitoring. Organizations should:  

• Stay Informed About Regulations: Regularly monitor updates to AI-related laws and regulations to stay compliant. 
• Identify Relevant Standards: Identify the relevant compliant standards that may govern your AI usage – whether applying existing standards to new AI-based situations, or taking on entirely new AI-specific compliance. 
• Conduct Ethical Impact Assessments: Evaluate the ethical implications of AI systems before deployment. 
• Establish Clear Policies and Procedures: Create policies that ensure compliance with relevant laws and ethical guidelines. 
• Develop a Comprehensive Compliance Program: Implement a structured approach to manage AI compliance across all departments. 
• Transparency, Explainability, and Fairness: Ensure AI systems are transparent and provide explanations for their decisions. 
• Data Governance and Quality: Maintain high standards for data governance and ensure the quality of data used in AI systems. 
• Ensure Data Privacy and Security: Implement robust security measures to protect personal data. 
• Human Oversight and Accountability: Ensure human oversight in critical decision-making processes involving AI. 
• Security Measures and Privacy by Design: Integrate privacy and security measures into the design of AI systems. 
• Establish an Audit Process: Regularly audit AI systems to identify and rectify compliance gaps. 
• Reporting and Responding to Compliance Issues: Develop clear reporting mechanisms for compliance breaches. 
• Employee Training and Awareness: Train employees on AI compliance requirements and best practices. 
• Collaborate with Stakeholders: Engage with stakeholders to ensure alignment on compliance goals. 
• Continuous Monitoring and Improvement: Regularly update AI systems and compliance programs in response to new regulations and emerging risks. 

How organizations can prepare for AI Governance

Automation and Data Management

Effective AI governance starts with automating the fundamentals. Manual processes can’t keep pace with the scale and speed of AI adoption. Organizations should:

• Centralize model and data inventories with automated discovery tools to track where AI is used, what data it relies on, and its risk classification.
• Automate data lifecycle management (collection, labeling, retention, deletion) to enforce privacy obligations and reduce exposure to bias or drift.
• Deploy monitoring pipelines that continuously test models for accuracy, fairness, and security, surfacing issues before they impact customers or regulators.
• Integrate governance with existing systems (GRC, IAM, data catalogues) so AI risks aren’t siloed.

Automation ensures compliance tasks, like logging, reporting, and audit prep—are handled consistently and with minimal human error, freeing teams to focus on higher-value analysis.

Ongoing training and Awareness

AI governance is not just a technical challenge; it’s a cultural one. Employees across roles need to understand how AI fits into risk and compliance obligations. Leading practices include:

• Role-specific training: Developers should learn about secure coding and bias mitigation; business units need to understand disclosure obligations; and require training on regulatory accountability.
• Awareness campaigns: Regular briefings, newsletters, and workshops to reinforce updates on new laws (e.g., EU AI Act, U.S. state laws) and organizational policies.
• Tabletop exercises: Scenario-based training (e.g., “what if a model fails an audit?”) builds preparedness for real-world incidents and regulator inquiries.
• Ethics and accountability culture: Encourage employees to flag concerns about AI use without fear of reprisal. This creates an early-warning system for governance issues.

Sustained training keeps policies alive, and helps prevent compliance fatigue, and ensures AI governance matures alongside evolving laws and business priorities.

How Technology Enhances AI Compliance 

AI in Risk and Compliance 

AI can be used to enhance risk management by identifying and mitigating potential compliance risks, such as bias or data breaches, before they cause harm. 

Automated Tools for Monitoring Compliance 

The reality is that AI has been a component of many different software tools for many years. Automated tools powered by AI can streamline compliance monitoring by flagging potential issues and ensuring adherence to regulatory frameworks. 

Leveraging AI for Regulatory Adherence 

AI-driven technologies can support regulatory adherence by identifying patterns and anomalies in large datasets that may signal non-compliance or unethical practices. 

Becoming an Expert in AI Compliance 

Organizations need professionals with a deep understanding of AI ethics and data protection to ensure successful AI compliance. Certifications in AI ethics, data protection, and compliance management are increasingly important for those looking to specialize in this area. 

Conclusion 

AI compliance is not just a regulatory requirement but a strategic necessity for organizations looking to navigate the complex AI landscape. By adopting responsible AI practices and leveraging AI compliance services, businesses can mitigate risks, foster consumer trust, and capitalize on the opportunities AI presents. Ensuring compliance will be crucial as regulations continue to evolve and as AI becomes more deeply embedded in society.

How Tevora can help you achieve AI Compliance

We bring security expertise among a range of disciplines to help you craft a comprehensive, forward-looking strategy to address Artificial Intelligence. Our team examines the various risk and opportunities of AI, from addressing threats to your security, to identifying areas where AI implementation can elevate existing solutions.

Our compliance experts stay up-to-date with the myriad of domestic and global frameworks and standards that address AI usage. This includes:

• ISO 42001
• NIST AI Framework
• EU AI Act
• HITRUST AI Risk Management Assessment

To learn more on our AI Compliance Services check out our webpage: https://www.tevora.com/outcomes/ai-security/ai-compliance-services/

AI Compliance FAQs 

What is AI Regulatory Compliance? 

AI regulatory compliance refers to adhering to laws and guidelines that govern the ethical, legal, and safe use of AI technologies. 

Why is AI Compliance Important? 

AI compliance is important because it helps mitigate legal, ethical, and reputational risks, fostering trust, protecting privacy, and encouraging innovation. 

What are some examples AI-specific Compliance Standards? 

Some emerging AI-specific compliance standards include ISO/IEC 42001:2023, the European Union’s AI Act, and NIST AI Risk Management Framework. 

What are the Consequences of Not Implementing AI Compliance? 

Non-compliance can lead to legal penalties, reputational damage, loss of business opportunities, and negative societal impacts, such as privacy violations or biased outcomes. 

What are Some Best Practices for an Effective AI Compliance Program? 

Best practices include staying updated on regulations, conducting ethical assessments, implementing clear policies, ensuring transparency, and conducting regular audits. 

How Can Technology Improve AI Regulatory Compliance? 

AI-driven tools can automate compliance monitoring, provide real-time risk detection, and streamline adherence to complex regulatory frameworks.