AI Compliance: Meaning, Regulations, Challenges

AI has rapidly evolved—from chatbots to predictive algorithms—becoming integral to business and daily life. A 2024 McKinsey survey found that 65% of organizations use generative AI, nearly doubling from the previous year.

As AI adoption grows, so does the need for responsible use. Compliance isn’t just about following rules—it’s key to building trust, ensuring fairness, and protecting privacy. Businesses must ensure AI systems are fair, transparent, and secure, conducting regular bias checks, safeguarding data, and being clear about AI-driven decisions. This not only ensures legal compliance but also strengthens customer trust, driving long-term success.

In this article, we will learn all about AI compliance and what you need to do to follow them.

What is AI compliance?

Artificial Intelligence (AI) compliance is the process of ensuring that AI systems adhere to applicable laws, regulations, and ethical standards. In recent years, several countries have introduced new regulations to govern AI. For example, in October 2023, Italy’s privacy authority approved a regulation emphasizing transparency, human oversight, and non-discrimination in AI-driven therapeutic decisions. 

Similarly, in September 2024, the United States, the United Kingdom, and the European Union signed the first international, legally binding treaty on AI use, emphasizing human rights and democratic values. This treaty mandates accountability for harmful AI system outcomes and upholds equality and privacy rights, providing legal recourse for affected individuals.

These developments highlight the global movement towards establishing comprehensive frameworks for AI governance. As a result, it’s quite possible that businesses operating internally must navigate multiple AI compliance standards at once, such as the EU AI Act, sector-specific U.S. regulations, and evolving UK guidelines to name a few.

What is the importance of AI compliance?

AI compliance ensures that AI systems operate ethically, securely, and in line with regulations, reducing risks like bias, data misuse, and security threats. As AI plays a bigger role in healthcare, finance, and hiring, organizations must ensure their systems are fair and transparent. Compliance frameworks like the EU AI Act, GDPR, and NIST AI RMF help enforce accountability and reduce the risk of harm or bias in AI-driven decisions.

Beyond legal requirements, strong AI compliance fosters trust and drives innovation. Businesses that proactively implement bias checks, data protection measures, and human oversight can deploy AI more responsibly while avoiding fines, lawsuits, and reputational damage. Ultimately, AI compliance is not just a regulatory necessity but a strategic enabler for safe, ethical, and globally scalable AI deployment.

Major AI regulatory-specific compliance laws

As AI technologies evolve, so does the regulatory terrain governing their use. Several key frameworks have been established to ensure the responsible development and deployment of AI systems. 

Some major AI regulatory compliance laws include:

1. EU AI Act

The European Union’s Artificial Intelligence Act (AI Act) is a pioneering legal framework designed to regulate AI within the EU. It was enacted on August 1, 2024, and most provisions will apply by August 2, 2026. 

Risk categories

The Act defines four levels of risk for AI systems based on their potential impact:

• Unacceptable risk: AI applications that are prohibited, such as those using subliminal techniques or exploiting vulnerabilities of specific groups.
• High risk: AI systems that significantly affect safety or fundamental rights, including those used in critical infrastructure, education, employment, and essential private and public services.
• Limited risk: AI systems requiring specific transparency obligations, like chatbots informing users they are interacting with AI.
• Minimal or no risk: AI systems with minimal or no impact on rights or safety, which are largely unregulated. 

The Act aims to protect EU citizens by ensuring AI systems are safe and respect fundamental rights, affecting businesses that develop or deploy AI within the EU market.

2. US AI Compliance landscape

In the United States, AI regulation is currently a patchwork of federal and state-level legislation. In October 2023, President Biden issued Executive Order 14110, mandating safety tests for AI systems posing risks to national security, the economy, or public health. 

However, in January 2025, President Trump rescinded this order, emphasizing the need to promote AI innovation free from ideological bias. He later signed an executive order titled “Removing Barriers to American Leadership in Artificial Intelligence,” which mandates an action plan within 180 days to sustain U.S. AI leadership.

At the state level, several legislations have been enacted:

• California: A law banning AI-generated deepfake political ads to prevent election misinformation.
• New York: Requires state agencies to report AI use and restricts AI decision-making in social programs.
• Tennessee (ELVIS Act): First law regulating AI-generated voices and deepfake audio.
• Utah AI Policy Act: Establishes AI liability laws and an Office of AI Policy.
• Colorado AI Act: Introduces requirements for transparency and risk assessments in high-risk AI systems.

3. ISO/IEC 42001

The ISO/IEC 42001:2023 is the first international AI management system standard, designed to help organizations develop, implement, and govern AI responsibly. It provides a structured approach to ethical AI deployment, risk management, and compliance across various industries. 

The standard ensures AI applications align with regulatory expectations, addressing key areas such as bias mitigation, accountability, transparency, and security. Organizations adopting ISO 42001 can demonstrate responsible AI governance, making compliance easier with global AI regulations, including the EU AI Act and NIST AI RMF.

4. Council of Europe’s Framework Convention on Artificial Intelligence

The Council of Europe’s AI Convention, adopted in September 2024, is the first legally binding international treaty on AI. It focuses on protecting human rights, upholding democracy, and enforcing the rule of law in AI applications. 

The treaty requires signatories to establish national accountability frameworks for AI-related harms and ensures compliance with principles of fairness, transparency, privacy, and non-discrimination. It also mandates legal safeguards for high-risk AI systems, making it a crucial step toward global AI governance.

“Artificial intelligence has the capacity to radically improve the responsiveness and effectiveness of public services, and turbocharge economic growth,” said Lord Chancellor and Justice Secretary Shabana Mahmood. “However, we must not let AI shape us – we must shape AI. This convention is a major step to ensuring that these new technologies can be harnessed without eroding our oldest values, like human rights and the rule of law.”

5. Japan’s Social Principles of Human-Centric AI

Japan’s Social Principles of Human-Centric AI serve as ethical guidelines rather than legally binding regulations. These principles promote AI development that respects human dignity, sustainability, and societal well-being. 

The framework encourages organizations to prioritize fairness, inclusivity, and environmental sustainability in AI innovation. Japan integrates these principles into its AI governance policies, influencing national regulations and international AI discussions, particularly within the G7 and OECD frameworks.

6. China’s Generative AI Measures

China’s Generative AI regulations, enacted in August 2023, impose strict content moderation, security, and compliance requirements on AI developers and service providers. 

The framework mandates that AI-generated content align with socialist values and national security interests, reflecting China’s broader cyber governance strategy. Key requirements include:

• Mandatory user identity verification before accessing AI services.
• Real-time content moderation mechanisms to filter misinformation.
• Algorithmic transparency and audits to ensure AI systems comply with government policies.

Companies that fail to comply face heavy fines, service restrictions, or potential shutdowns.

7. NIST AI Risk Management Framework (AI RMF)

The NIST AI Risk Management Framework (AI RMF), developed by the U.S. National Institute of Standards and Technology, is a voluntary guideline aimed at enhancing AI trustworthiness, security, and risk mitigation. It focuses on managing AI risks related to bias, privacy, security, and reliability, helping organizations design, develop, and deploy AI responsibly. 

The AI RMF provides best practices for risk assessments, documentation, and governance, aligning closely with the White House AI Executive Order and U.S. regulatory efforts. While not legally mandatory, it is widely adopted by U.S. federal agencies, enterprises, and AI developers as a de facto industry standard for AI governance. 

Which industries require AI regulatory compliance? 

Artificial Intelligence is transforming numerous industries, leading to the emergence of regulatory frameworks to ensure its ethical and safe deployment. While AI compliance is becoming increasingly relevant across various sectors, certain industries face more stringent regulatory requirements due to the nature of their operations and the sensitivity of the data they handle.

1. Financial services

The financial sector is leveraging AI for tasks such as fraud detection, risk assessment, and customer service enhancements. However, the integration of AI introduces risks related to data privacy, algorithmic bias, and systemic stability. Regulatory bodies are focusing on ensuring that AI applications in finance comply with existing laws and uphold ethical standards.

2. Healthcare

In healthcare, AI is utilized for diagnostics, treatment planning, and patient management. Given the critical nature of medical decisions and the sensitivity of health data, regulatory compliance is paramount. Frameworks are being developed to ensure that AI systems in healthcare are safe and effective and respect patient privacy.

3. Manufacturing

The manufacturing industry employs AI for process optimization, predictive maintenance, and quality control. As AI systems become integral to production processes, ensuring their compliance with safety and operational standards is essential to prevent accidents and maintain product quality.

4. Energy and utilities

AI applications in the energy sector include grid management, predictive maintenance, and energy efficiency optimization. Regulatory compliance ensures that these AI systems operate safely, securely, and in a manner that supports environmental sustainability.

5. Creative industries

The creative sectors, encompassing arts, music, publishing, and entertainment, are increasingly impacted by AI, particularly in content creation and distribution. Regulations are being considered to protect intellectual property rights and ensure fair compensation for creators in the face of AI-generated content.

As AI continues to permeate various sectors, the development and enforcement of industry-specific regulatory frameworks are crucial to ensuring ethical practices, protecting consumer rights, and maintaining public trust. There are ongoing efforts in the intellectual property laws to address AI-generated content more comprehensively. 

What are the major challenges of becoming AI compliant?

Achieving compliance in AI involves navigating several complex challenges. Addressing these challenges necessitates a proactive approach, including continuous monitoring of AI systems, regular audits, stakeholder engagement, and fostering a culture of ethical AI use within organizations.

1. Data privacy and security

AI systems often require vast amounts of data, raising concerns about the protection of personal information. Ensuring compliance with data protection regulations, such as the General Data Protection Regulation (GDPR) in Europe, necessitates robust data governance practices to prevent unauthorized access and breaches.

2. Algorithmic bias and discrimination

AI models can inadvertently learn and perpetuate biases present in their training data, leading to unfair or discriminatory outcomes. Identifying and mitigating these biases is crucial to ensure ethical AI deployment and to comply with anti-discrimination laws.

3. Lack of transparency and explainability

Many AI systems, particularly those based on deep learning, operate as “black boxes,” making it difficult to understand their decision-making processes. This opacity poses challenges for compliance, as regulations increasingly demand transparency and the ability to explain AI-driven decisions. 

4. Rapid technological evolution

The swift pace of AI development often outstrips the establishment of regulatory frameworks, leading to uncertainties in compliance requirements. Organizations must stay abreast of evolving standards and be agile in adapting their compliance strategies accordingly. 

5. Integration with existing compliance programs

Incorporating AI compliance into existing regulatory frameworks can be challenging, especially in industries with stringent regulations. Organizations need to assess how AI technologies align with current compliance obligations and modify their programs to address new risks introduced by AI.

6. Resource constraints

Implementing comprehensive AI compliance measures requires significant investment in expertise, technology, and processes. Organizations, particularly smaller ones, may struggle with allocating sufficient resources to develop and maintain effective compliance programs.

Best practices for an effective AI regulatory compliance program 

Implementing an effective AI regulatory compliance program is crucial for organizations to navigate the complex landscape of artificial intelligence responsibly. Here are some best practices to consider:

1. Develop clear policies and procedures

Establish comprehensive policies that outline the ethical use of AI within your organization. These should align with industry standards and regulatory requirements, providing a framework for responsible AI development and deployment.

2. Implement a multi-layered risk management strategy

Adopt a multi-layered defense approach to manage AI-related risks effectively. This involves assigning specific risk ownership across different stages of AI development and deployment, ensuring accountability and thorough oversight. 

3. Foster AI literacy and provide targeted training

Promote AI literacy across all levels of your organization. Conduct regular, role-specific training sessions to ensure employees understand AI principles, potential risks, and compliance obligations pertinent to their responsibilities.

4. Prioritize data privacy and security

Implement robust data governance frameworks to protect sensitive information used in AI systems. Ensure compliance with data protection regulations by enforcing strict access controls, encryption, and regular audits to prevent unauthorized data usage. 

5. Establish transparent monitoring and reporting mechanisms

Develop clear processes for monitoring AI system performance and reporting any anomalies or compliance issues. This enhances transparency and facilitates prompt corrective actions when necessary.

6. Conduct regular ethical impact assessments

Perform ongoing assessments to evaluate the ethical implications of AI applications. This proactive approach helps identify potential biases or unintended consequences, allowing for timely mitigation strategies.

“AI leadership isn’t just about innovation and efficiency—it’s about responsibility. If you’re leading AI teams, you don’t need to be an ethicist, but you do need to speak the language of AI ethics. That’s the new baseline for leadership in a world where AI decisions can have massive real-world consequences.” – Troy Latter

7. Maintain comprehensive documentation and audit trails

Keep detailed records of AI system development processes, decision-making protocols, and data sources. Comprehensive documentation facilitates internal audits and demonstrates compliance with external regulators.

8. Engage with external stakeholders and stay informed

Actively participate in industry forums and collaborate with regulatory bodies to stay abreast of evolving AI regulations and standards. Engaging with external stakeholders ensures your compliance program remains current and effective.

What are the consequences of not complying with AI regulations? 

Non-compliance with AI regulations can lead to significant repercussions for organizations. Key consequences include:

1. Financial penalties

Regulatory bodies may impose substantial fines on organizations that fail to adhere to AI laws. For instance, under the European Union’s AI Act, violations can result in fines up to €35 million or 7% of a company’s worldwide annual turnover, whichever is higher.

2. Legal liabilities

Organizations may face legal consequences, including lawsuits and sanctions, for deploying non-compliant AI systems. The severity of these actions depends on local jurisdiction and the specifics of the violation, potentially resulting in costly legal battles and settlements.

3. Reputational damage

Non-compliance can erode public trust and damage an organization’s reputation. Negative publicity surrounding unethical or unlawful AI practices can deter customers and partners, leading to long-term financial losses.

4. Operational disruptions

Regulatory enforcement actions may require organizations to halt or modify AI operations, leading to disruptions in business processes and potential loss of revenue.

5. Increased scrutiny and oversight

Non-compliant organizations may become subject to heightened regulatory scrutiny, resulting in more frequent audits and monitoring, which can strain resources and hinder innovation.

Adhering to AI regulations is essential to mitigate these risks and ensure the ethical and lawful deployment of AI technologies.

Does AI compliance software simplify the certification process?

Yes, AI compliance tools are essential for streamlining and ensuring adherence to regulatory requirements. These tools automate compliance workflows, monitor regulatory changes in real time, and reduce the risk of human error. Given the complexity of AI regulations, relying solely on manual processes can lead to oversight, inefficiencies, and increased non-compliance risks. 

AI-powered compliance solutions enhance transparency, provide continuous risk assessment, and facilitate audit readiness, making them a crucial component of an effective compliance strategy. However, while these tools provide significant support, they should be complemented by human oversight to ensure ethical and contextually appropriate AI governance.

“True AI compliance involves navigating an interconnected web of regulations—ranging from data protection to cybersecurity and competition law. For AI solutions to be legally sound, ethically grounded, and sustainable in the marketplace, organizations must develop and maintain a holistic compliance strategy that evolves alongside new technologies and legislative updates.” – Eckhart M., Chief Information Security Officer | CISO | Cybersecurity Strategist | Cloud Security Expert | AI Security Engineer

What are some AI regulatory-compliant software solutions?

Ensuring compliance with AI regulations is crucial for organizations to mitigate risks and adhere to legal standards. Several software solutions have been developed to assist in this process by offering advanced features tailored to compliance needs. Here are a few notable examples:

1. Scrut

Scrut is an advanced compliance automation platform that helps businesses streamline their regulatory processes, including AI compliance. It provides real-time compliance monitoring, automated policy management, and AI-powered risk assessment to help companies adhere to regulations like ISO 42001, GDPR, and the EU AI Act.

Key Features:

• Continuous compliance monitoring
• AI-driven risk assessment
• Automated policy and audit management
  

2. PwC’s AI Compliance Tool

PwC’s AI Compliance Tool helps organizations manage AI governance and compliance. It provides risk assessment and classification features, compliance guidance, and auditing capabilities. The tool enables organizations to identify AI system risks, document compliance measures, and centralize regulatory reporting.

Key Features:

• AI risk assessment and classification
• Automated regulatory tracking
• Compliance documentation and audit support

3. Compliance.ai

Compliance.ai is a regulatory change management platform that monitors global compliance updates and delivers relevant information to businesses. The platform integrates AI-driven content analysis with human expertise to assess regulatory obligations and their impact on policies and procedures.

Key Features:

• AI-powered real-time regulatory monitoring
• Automated compliance risk assessment
• Industry-specific compliance tracking

These platforms enable organizations to effectively manage AI compliance, mitigate risks, and stay updated with evolving regulatory frameworks.

What key features should AI compliance software include?

Implementing AI compliance software is essential for organizations aiming to adhere to regulatory standards effectively. Key features to consider when selecting such software include:

1. Real-time regulatory monitoring

The software should continuously track and analyze regulatory updates from various jurisdictions, providing timely alerts about changes that could impact the organization’s compliance status. This ensures that businesses remain informed and can promptly adjust their practices to align with new requirements.

2. Automated compliance management

Automation capabilities are crucial for streamlining routine compliance tasks, such as document reviews, audit trail maintenance, and regulatory reporting. By reducing manual effort, the software minimizes errors and allows compliance teams to focus on strategic initiatives.

3. Risk assessment and management

Advanced AI tools within the software should assess potential risks by analyzing diverse datasets to identify emerging threats. This includes evaluating the impact of regulatory changes and recommending proactive mitigation strategies to maintain organizational resilience.

4. Transparent reporting and audit support

Effective compliance software provides comprehensive reporting features that facilitate the generation of detailed compliance reports. This transparency supports internal audits and demonstrates adherence to regulations during external reviews.

5. Integration capabilities

The ability to integrate seamlessly with existing systems is vital. This ensures that the compliance software can access necessary data across the organization, providing a holistic view of compliance status and facilitating efficient workflow management.

By prioritizing these features, organizations can select AI compliance software that not only ensures adherence to regulations but also enhances overall operational efficiency.

What key trends are shaping AI compliance?

Staying updated on the latest trends in AI compliance is essential for organizations aiming to navigate the evolving regulatory landscape effectively. According to Scrut Automation’s “Top 10 GRC and AI Predictions for 2025,” key developments include:

1. Emergence of AI governance professionals

As AI systems become more integral to business operations, there’s a growing demand for professionals specializing in AI governance. These experts are responsible for ensuring that AI deployments adhere to ethical standards and comply with regulatory requirements.

2. Integration of cryptocurrencies in compliance frameworks

The increasing adoption of cryptocurrencies necessitates the incorporation of crypto-related considerations into governance, risk, and compliance (GRC) strategies. Organizations must develop policies to manage the unique risks associated with digital assets.

3. Adoption of quantitative risk management techniques

There’s a shift towards utilizing quantitative methods in risk management, leveraging data analytics and AI to assess and mitigate risks more precisely. This approach enhances decision-making processes and supports proactive compliance management.

By embracing these trends, organizations can strengthen their compliance frameworks and better prepare for the future landscape of AI governance.

Ensure your AI systems comply with evolving regulations like the EU AI Act, ISO 42001, and NIST AI RMF—without the complexity. Scrut’s AI Compliance Management System helps you automate compliance, mitigate risks, and build trust in your AI operations.