Artificial intelligence (AI) is revolutionizing business operations across industries, offering unprecedented opportunities for innovation and efficiency.  According to National University, 77% of companies are either using or exploring the use of AI in their businesses. AI is reshaping how companies operate, make decisions and interact with customers. However, it also brings new challenges and risks. 

Business leaders must prioritize AI compliance to avoid significant consequences, including financial penalties, reputational damage and operational disruptions. The time to act is now, as the AI landscape continues to evolve rapidly. With regulatory bodies worldwide focusing on AI governance, companies that proactively address compliance issues will be better positioned for success in the long run. 

What is AI compliance?

AI compliance ensures that AI systems adhere to legal and ethical standards, encompassing data privacy, fairness, transparency and security. For businesses, it’s not just about following rules; it’s about safeguarding financial health and maintaining operational integrity. AI compliance involves a comprehensive approach to managing AI systems, from development and deployment to ongoing monitoring and improvement. 

AI compliance in specific industries 

The scope of AI compliance varies across industries, reflecting the diverse applications of AI technology. Let’s explore how different sectors approach AI compliance: 

Financial Services 

Financial services focus on algorithmic trading and fraud detection, complying with regulations like the General Data Protection Regulation (GDPR), the Gramm-Leach-Bliley Act and the California Consumer Privacy Act (CCPA). Key AI compliance concerns include: 

• Financial data protection: Robust measures are required to protect sensitive financial data from breaches and unauthorized access, including improper use in training AI models. This involves implementing strong security measures, regular data audits, access controls and encryption protocols. 
• Algorithmic fairness and bias mitigation: Banks and investment firms must ensure their AI models don’t have bias or discriminate against certain groups when making lending or investment decisions. Regular audits of AI algorithms can help identify and correct unfair outcomes. 
• Transparency and explainability: Financial institutions should strive to make their AI systems as transparent as possible, explaining how AI arrives at specific decisions or recommendations, particularly for lending and investment decisions. 

Healthcare 

Healthcare providers must prioritize patient data protection and unbiased diagnostics while ensuring compliance with the Health Insurance Portability and Accountability Act (HIPAA) and Medicaid and Medicare regulations. Key AI compliance concerns include: 

• Data privacy and governance: Healthcare providers must ensure that AI doesn’t compromise patient privacy or confidentiality. This includes implementing clear policies on data collection, storage and usage, as well as obtaining proper consent for data use. 
• Ethical AI development and use: AI systems used in medical imaging or treatment recommendations must be rigorously tested for accuracy and fairness to comply with the Food and Drug Administration’s requirements for medical devices. This involves diverse representation in AI development teams and rigorous testing for fairness across different demographic groups. 
• Transparency and explainability: Certain diagnoses and medical payment claims must be reviewed and confirmed by humans, requiring AI systems to provide clear explanations for their suggestions. 

Retail and e-commerce 

Retail and e-commerce emphasize fair pricing and personalized recommendations while navigating complex consumer protection laws. Key AI compliance concerns include: 

• Algorithmic fairness and bias mitigation: AI-driven pricing algorithms must avoid collusion or unfair discrimination. Companies should implement fairness metrics and continuously monitor their AI systems for potential biases. 
• Data privacy and governance: Companies are required to disclose the use of personal information and are prohibited from using personal data for unapproved purposes. Recommendation systems should respect user privacy and provide transparent explanations for their suggestions. 
• Transparency and explainability: Businesses should strive to make their AI systems as transparent as possible, explaining how AI arrives at specific pricing decisions or personalized recommendations. 

Manufacturing and supply chain 

Manufacturing and supply chain target quality control and predictive maintenance, adhering to safety regulations and standards. Key AI compliance concerns include: 

• Cybersecurity: AI systems in this sector must be reliable and produce consistent results to maintain product quality and safety. Cybersecurity measures must be integrated into AI systems from the ground up, with regular testing and updates to keep AI defenses strong. 
• Ethical AI development and use: AI should be designed to identify and mitigate potential risks in the production process. While AI can be a great aid manufacturing and protecting workforce safety, it also needs to be managed and monitored so that it does not increase safety risks due to ambiguous instructions for unanticipated events. 
• Transparency and explainability: Companies should implement systems that can explain AI-driven decisions in quality control and predictive maintenance, building trust and accountability in the manufacturing process.  

Nine best practices for AI compliance

Organizations should implement the following best practices to stay on top of AI compliance: 

1. Establish AI governance frameworks with clear policies and procedures: This is crucial for addressing ethical AI development, transparency and algorithmic fairness. These frameworks define roles, responsibilities and accountability measures, often including a dedicated AI ethics committee to oversee AI development and deployment. 
2. Implement a comprehensive compliance program: The program should cover all aspects of AI use, from data collection to model deployment and monitoring. This holistic approach addresses all key compliance concerns, including data protection, ethical AI, algorithmic fairness, transparency and cybersecurity. 
3. Continuously monitor and audit AI systems and AI-driven financial processes: This will help catch issues early and ensure ongoing compliance. This practice is particularly important for addressing financial data protection, algorithmic fairness and cybersecurity concerns. Organizations should implement automated monitoring tools and conduct regular manual reviews to identify potential compliance risks. 
4. Provide employee training on AI compliance and ethics: The training supports a workplace culture of responsibility and awareness. It should include both technical instruction for AI developers and general awareness for all employees interacting with AI systems, addressing ethical AI development, data privacy and transparency concerns. 
5. Foster cross-functional collaboration: Cross-functional collaboration between finance, IT, legal and compliance teams is necessary for a holistic approach to AI compliance. Regular meetings and open communication channels can help address all aspects of AI compliance across various departments. 
6. Develop clear documentation practices for AI systems: These practices support transparency, explainability and regulatory compliance. Detailed records of data sources, model architectures and decision-making processes should be maintained, regularly updated and easily accessible for audits and regulatory inquiries. 
7. Establish a process for handling AI-related complaints or concerns: Setting up a process helps address ethical AI development and algorithmic fairness issues. It should include mechanisms for investigation, corrective action, a clear escalation path and timelines for addressing issues. 
8. Stay informed about evolving AI regulations and industry standards: This helps organizations adapt to changing regulatory landscapes and maintain compliance across all key areas. Participating in industry forums or consulting with legal experts can aid in this ongoing effort. 
9. Conduct regular risk assessments specific to AI systems: Such risk assessments help identify potential issues before they become problems, allowing for proactive management of AI compliance. These assessments should consider both technical and ethical risks, informing ongoing improvements to AI systems and compliance practices across all key concern areas. 

BPM for guidance with AI compliance

When it comes to AI compliance, a proactive approach helps you mitigate risks and build trust with customers and stakeholders. Companies that prioritize AI compliance are better positioned to leverage AI technologies effectively while minimizing potential legal and reputational risks. 

BPM has a team of specialists who focus on AI and its use by small and medium businesses. Our knowledge of AI compliance and risk management can help guide your organization. We understand the unique challenges businesses face when implementing AI systems and can support your business in: 

• Developing governance frameworks 
• Implementing effective monitoring systems  
• Training employees on best practices for AI compliance 

It’s important that your AI systems are not only powerful and efficient but also ethical and compliant. Our team stays current with the latest regulatory developments and industry best practices to provide comprehensive support for your AI compliance needs. 

Learn how we can help safeguard your AI investments and propel your business forward in the AI era. To start your journey towards AI compliance, contact us.