9 best practices for AI compliance and how to implement them
As AI becomes more embedded in business operations—and more autonomous—questions about how to use and develop AI responsibly are increasingly top of mind. Recent incidents of noncompliance, like Clearview AI’s costly data privacy settlement, have only heightened this sense of urgency.
Faced with mounting examples of AI risks, countries, companies, and policymakers are working to define standards for how AI should be developed, deployed, and governed.
If there were a single, unified framework for AI compliance, the path forward would be clear. However, the push towards AI regulation is as fast-moving as it is fragmented. For example, in the US, hundreds of state-level bills have been introduced for 2025. Meanwhile, the EU AI Act, which took effect in February 2025, set a global precedent with penalties of up to €35 million or 7% of annual turnover for noncompliance.
In short, businesses must navigate an evolving patchwork of laws, guidelines, and ethical expectations—all while AI capabilities continue to expand—making it a challenge to ensure AI compliance.
This article outlines 9 best practices for AI compliance in this evolving regulatory landscape. It includes a high-level overview of the regulatory landscape and key resources to help your organization stay ahead in an increasingly complex regulatory environment.
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What is AI compliance?
AI compliance refers to the policies, practices, and governance frameworks that enable organizations to align with laws, regulations, and internal standards that govern how AI is used. These processes are designed to promote the responsible and ethical use of AI by addressing issues like accountability, transparency, data privacy, and security.
AI compliance is about more than meeting legal requirements. These processes play a critical role in building stakeholder trust, promoting fairness and transparency, as well as ensuring safety and responsibility. Since AI can be exploited by threat actors, AI compliance also includes robust cybersecurity measures and risk management strategies.
Why does AI compliance matter?
AI compliance helps businesses to avoid the evolving financial, legal, and reputational risks associated with the use of AI tools.
With 72% of businesses now using AI, there’s a growing appreciation that AI technology can be unpredictable, inaccurate, or unreliable at times. This opens to door to AI risks and their consequences, such as:
Biased outputs: AI systems can perpetuate bias in their training data. For example, the education platform iTutor settled with the Equal Employment Opportunity Commission (EEOC) after its AI rejected female applicants over the age of 55.
Data privacy issues: AI often handles sensitive data and corporate IP. Clearview AI was fined over $30 million in 2024 by the Netherlands’ data protection authority for its unethical use of private user data for building databases to train its AI facial recognition technology.
Reputational damage: Compliance is key to protecting brand reputation, as 78% of consumers believe that organizations using AI have a responsibility to ensure it's developed ethically. Failure to do so can lead to a loss of revenue and consumer trust.
Financial loss: Noncompliance is a real financial risk. Under the EU’s General Data Protection Regulation (GDPR), companies may be fined up to €20 million or 4% of global annual revenue, whichever is greater. In the US, the Federal Trade Commission (FTC) has the authority to take action against organizations for AI-related violations such as using biased AI systems.
Given these risks, businesses increasingly recognize the importance of aligning with current regulatory requirements and future standards. Per a Deloitte study, nearly 70% of companies using AI plan to increase investment in AI governance over the next two years. With AI fast-becoming a core technology for businesses, there's a growing awareness about the need for transparency, ethics, and compliance.
AI compliance is emerging as a new pillar of corporate governance. By designing, deploying, and maintaining AI systems with compliance in mind, businesses can set themselves up to build safe, responsible, and scalable AI solutions that deliver value even as new laws and regulations take effect.
Understanding the AI compliance landscape
With concerns around AI mounting, there’s a global push to standardize the development and use of AI in business. Currently, however, the regulatory landscape is a growing patchwork of international standards, state-level legislation, and industry-specific requirements
Countries are in the process of enacting AI standards that could impact global governance. However, these rules sometimes apply to anyone doing business in the region, not just those operating there. Further complicating this is the growing set of AI-specific rules around data privacy, cybersecurity, discrimination, and algorithmic transparency.
Here are a few examples:
The US
Currently, the US relies on existing federal laws and guidelines to regulate AI, but it aims to introduce federal legislation and a regulatory authority in the future. Until then, businesses must operate amidst an increasingly fragmented set of state and local laws.
For example, the Colorado AI Act requires AI developers and deployers to use reasonable care to protect consumers from known or reasonably foreseeable risks of "algorithmic discrimination."
📕Helpful resource: AI watch: Global regulatory tracker for the United States, March 2025.
The EU
The EU provides two comprehensive regulations that relate to AI development and use. This includes the EU AI Act, which is the world’s first comprehensive regulatory framework for AI. It uses a risk-based approach and prohibits certain AI uses and imposes risk management and transparency requirements on others.
This is in addition to GDPR, which sets specific standards for data privacy, data analysis and personal data use. Individual countries, as with Spain's 2025 law on labelling generative AI content, are also enacting their own regulations.
📕Helpful resource: AI watch: Global regulatory tracker for the EU, March 2025
China
In 2022, China became the first country to introduce detailed, binding regulations on the transparency, labelling, and security of AI. This includes the Interim Measures for the Management of Generative AI Services, which sets standards and rules for data privacy, labeling and generative AI licensing, among other regulations.
📕Helpful resource: AI watch: Global regulatory tracker for China, March 2025
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9 best practices for AI compliance
Effective AI compliance is more than a legal box to check—it must be a strategic, cross-functional effort that evolves in step with AI regulations. This involves a proactive approach that invests in the resources, expertise, and technology needed for compliance processes that enable AI to operate in a way that's ethical, accountable and trustworthy.
Here are 9 best practices to help guide the path to mitigating risk and mixing the full potential of AI.
1. Stay informed about evolving regulations
The regulatory picture around the use of AI continues to evolve—albeit unevenly—across jurisdictions. While some foundation rules are already in place, international bodies are developing practice standards, and nearly a dozen US states are considering AI legislation in 2025.
With laws changing quickly, staying up to date is essential not only for maintaining compliance, but given the rapid pace of AI advancement—also for ensuring compliance programs keep pace with the technology itself.
Take action:
Assign a team or compliance lead to track changes in global and regional AI regulations. (EU AI Act, NIST RMF, ISO/IEC 42001).
Attend industry conferences and working groups on AI ethics and policy.
Subscribe to regulatory bulletins, legal news, and AI compliance newsletters. (International Association of Privacy Professionals, AI Now Institute).
2. Identify and align with relevant standards
AI compliance obligations vary by use case, industry, and jurisdiction, making it crucial to identify which standards apply to your organization. Map your organization’s AI use cases to the applicable standards of your industry and operating jurisdictions. Also be sure to also account for adjacent requirements around data privacy, cybersecurity, and anti-discrimination such as ISO/IEC 42001.
Take action:
Map your organization’s AI use cases to applicable standards such as GDPR, HIPAA, etc.
Work with legal and compliance teams to align internal policies with relevant standards and requirements.
Apply existing general data protection rules and emerging AI-specific regulations.
3. Conduct ethical impact assessments
Ethical AI use is increasingly important to consumers, stakeholders, and regulators, making it crucial to conduct an ethical impact assessment that can help identify and mitigate unintended consequences of AI systems before they’re deployed.
For example, Microsoft and Salesforce have established internal review boards for ethical AI use—demonstrating how a proactive, considered approach can build trust and reduce AI risks for users and your organization.
Take action:
Evaluate AI decision-making regularly for unintended consequences.
Engage ethicists, stakeholders, and domain experts in the review process.
Document ethical risks and remediation strategies before launch.
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4. Establish clear policies and procedures for AI governance
Without organization-wide policies, AI usage across departments can become risky, inconsistent, and non-compliant. According to McKinsey, organizations with centralized AI governance are twice as likely to scale AI use responsibly and effectively.
Take action:
Define acceptable AI use cases, data handling protocols, safeguards, and workflows with HITL (human in the loop) oversight.
Implement a centralized policy for AI procurement, development, and deployment.
Communicate policies across your organization and update them as laws evolve.
5. Develop a comprehensive AI compliance strategy
AI compliance is more than a legal concern—it spans IT, operations, and product. By involving legal, technical, and operational teams from the start in creating a cross-functional AI compliance strategy, organizations can avoid the blind spots that come with a siloed approach to better ensure compliance.
Additionally, understanding AI technology can be technically challenging, which complicates compliance efforts. Involving technical stakeholders in compliance processes can help keep pace with evolving technology and its regulations based on expertise instead of guesswork.
Take action:
Create a comprehensive AI compliance strategy that involves legal, compliance, IT, data science, and business units.
Classify AI systems by risk level (e.g., minimal, limited, high-risk) and tailor controls accordingly.
Implement AI compliance KPIs, testing, regular audits, and performance reviews to track compliance progress.
6. Promote AI transparency and explainability
It’s important for organizations and regulators to be able to understand how AI systems make decisions, especially when outcomes impact individuals. However, AI models often function as “black boxes,” making their reasoning and outcomes difficult to understand and explain—and even more difficult to defend under scrutiny.
Take action:
Adopt explainable AI techniques such as continuous AI model evaluation and documentation.
Audit and review AI outputs based on explainability standards like ISO/IEC 42001.
Test systems for bias regularly using explainable AI tools like LIME, SHAP, or AI Explainability 360 and publish fairness metrics.
7. Strengthen data governance
AI systems are only as compliant as the data they’re trained on. Data that’s inaccurate, incomplete, or inconsistent can lead to biased, inaccurate, or harmful outcomes. This makes it essential to establish robust AI data governance practices that support data integrity, security, and privacy to help ensure compliance.
Take action:
Define and enforce data quality standards by establishing data lineage tracking and metadata standards.
Invest in data management, collection, and transformation processes to clean and validate datasets before use in AI training.
Monitor AI systems for data drift and retrain AI models regularly to maintain accurate, compliant, ans safe outputs.
8. Ensure data privacy and security
AI systems often handle sensitive personal user data and corporate intellectual property (IP), and violations can lead to regulatory penalties and loss of public trust. However, retroactively securing AI systems is often inefficient and ineffective, so it’s best to design AI for data privacy and security by default.
A proactive approach helps to ensure data transparency, user control, and data security are considered throughout the AI lifecycle, helping to mitigate the risk of data breaches, cybersecurity threats, and ensure compliant AI governance.
Take action:
Establish AI use policies that align with relevant data privacy laws like GDPR, CCPA, or HIPAA.
Implement data minimization, encryption, and anonymization techniques to reduce risk exposure.
Integrate safeguards across AI lifecycles to ensure privacy concerns are considered at every stage.
Conduct regular data privacy impact assessments.
9. Build in human oversight and accountability
Even the most advanced AI requires human oversight and accountability, as it’s people who ultimately bear the responsibility for AI’s decisions and outcomes. Left unchecked, AI systems can reinforce bias, misinterpret data, or posit falsehood as truth (hallucinate), potentially leading to regulatory violations, cybersecurity risks, or harmful outcomes.
The EU AI Act explicitly requires human oversight for all high-risk AI systems, mandating that systems be designed to allow meaningful human intervention, and similar rules are likely to emerge in other regions. Beyond mitigating the risks associated with AI use, human oversight also helps to build trust with regulators, employees, and the public.
Take action:
Assign clear roles and responsibilities for AI monitoring, escalation, and review.
Build “human-in-the-loop” or “human-on-the-loop” protocols into high-risk systems.
Require documented approvals and rationale for key AI decisions with role-based access control.
Train oversight teams on AI risks, system limitations, and relevant regulations.
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AI compliance is a proactive path to responsible AI
As AI technologies continue to evolve, so do the risks and challenges for compliance teams. As such, AI compliance requires a continuous commitment to building, monitoring, and maintaining AI systems so they're as safe, fair, and trustworthy as they are powerful.
As regulations continue to evolve, organizations that adopt these best practices now will be better positioned to innovate confidently, avoid costly missteps, and earn long-term trust from users, partners, and regulators alike.
If you’re looking to build AI agents that are compliant, Sendbird can help. Our robust AI agent platform makes it easy to build AI agents on a foundation of enterprise-grade infrastructure that ensures optimal performance with unmatched adaptability, security, compliance, and scalability.
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