Responsible AI Governance Best Practices For Business Leaders: Creating Transparency in AI Systems
The governance requirements for AI are rapidly evolving to keep up with innovations in this technology. As such, businesses that prioritize responsible and ethical AI practices will be better positioned to succeed in the long term.
What is AI Governance?
Recently, there have been increasing discussions and developments related to AI governance as governments and organizations worldwide recognize that AI adoption is only going to increase and, as such, must be supported by robust policies that monitor AI and ensure its responsible use. So, what does the term mean?
AI governance refers to the rules, policies, and frameworks that oversee the development, deployment, and use of AI-based technologies.
Responsible AI governance ensures that AI models are developed and used ethically, transparently, and in the best interest of society. Developing trustworthy AI tools means ensuring they uphold the privacy of the individuals whose data was used to train them. The tools should also be free from bias and fair in their decision-making. Part of effective AI governance is also ensuring accountability, or who is responsible for fixing any issues that come up, and addressing safety concerns.
Before your business deploys AI tools, it must be aware of the emerging regulations and guidelines related to AI governance and ensure that its intelligent systems are designed and implemented in compliance with these standards. You’d need to incorporate ethical considerations into your AI development processes. Additionally, you would need to conduct regular audits and risk assessments to provide clarity and explanations for AI-driven decisions.
Finally, your business may need to consider AI’s potential impact on its workforce and customers and implement measures to address any negative consequences or mitigate risks.
Why Is Implementing AI Governance Important?
You need a framework for AI governance for several reasons.
Reducing Ethical Concerns
AI-powered technologies have the potential to significantly impact individuals and society, such as through privacy violations, discrimination, and safety risks. For example, any biases inherent in the training data can creep into the model’s decision-making process. It can influence the results produced by generative AI. Read more about Agentic vs Generative AI.
An AI risk management framework can help prevent such issues throughout the AI lifecycle. AI safety and governance frameworks help ensure that these technologies are developed and used ethically and in the best interest of society. It also lays out the processes for fixing any issues that are discovered, along with who’s responsible for doing it, to minimize the impact of AI risks.
Eliminating Ambiguity
AI algorithms are often complex and opaque, making it difficult to understand how decisions are made. This lack of clarity means users can’t trust these systems. Governance frameworks promote transparency, which can help build confidence in AI technologies and enable effective oversight. Clear documentation can help provide insights into how the AI developers have structured the decision-making process used to train AI models.
Creating Accountability
The use of AI technologies can significantly impact individuals and society, both positively and negatively. As such, those responsible for any negative consequences should be held accountable. AI policies and regulations establish accountability mechanisms, such as liability and redress. With accountability built into the system, stakeholders must adhere to legal and ethical standards.
Encouraging Regulatory Compliance
Governments worldwide are introducing regulations related to AI, such as data protection laws and ethical guidelines, in an effort to govern AI effectively and ensure these systems are compliant. As a result, if your development and deployment of AI initiatives are not managed and governed properly, you risk legal and reputational consequences.
Driving Innovation
Comprehensive AI governance guidelines can foster innovation by providing clarity and certainty around the ethical and legal parameters within which AI-led technologies must operate. This can help your organization make informed decisions about developing and deploying these technologies.
Pending and Active AI Governance Legislation
Here are some legislations — both enacted and proposed — promote AI safety and security. These promote the responsible use of AI, help reduce risks, and promote effective governance.
National Artificial Intelligence Initiative Act of 2020 (NAIIA)
The National Artificial Intelligence Initiative Act of 2020 (NAIIA) was signed into law on January 1, 2021, as part of the National Defense Authorization Act for Fiscal Year 2021.
The law established a coordinated national strategy for AI, including the National AI Initiative Office, AI research institutes, and advisory committees. It continues to play a central role in promoting ethical, secure, and collaborative AI development across federal agencies and research institutions.
Algorithmic Justice and Online Transparency Act
The Algorithmic Justice and Online Transparency Act is proposed legislation that would require online platforms to disclose how algorithms use personal data, evaluate algorithmic impacts on civil rights, and prohibit discriminatory practices in digital services.
As of July 2025, the bill remains in committee and has not advanced, but it represents one of several recent efforts in Congress to regulate algorithmic transparency and fairness. If passed, it could impose significant obligations on social media platforms and other digital service providers, particularly regarding content moderation, targeted advertising, and civil rights compliance.
AI LEAD Act
The AI Training Act, signed into law in 2022, provided foundational AI education for federal procurement professionals.
Building on this foundation, the AI Leadership to Enable Accountable Deployment (AI LEAD) Act was introduced in 2023 as a broader and more ambitious bill to create a comprehensive federal framework for AI oversight.
This bill—currently pending—would require federal agencies to appoint Chief Artificial Intelligence Officers, create AI Governance Boards, and publish detailed AI strategies, establishing formal governance structures to manage and oversee AI deployment. It emphasizes privacy, civil liberties, transparency, algorithmic accountability, and public trust in government AI systems.
While not yet enacted, the new version of the AI LEAD Act signals a major policy shift toward structured, institutionalized AI governance across the U.S. federal government.
AI RMF
The NIST AI Risk Management Framework (AI RMF) was released on January 26, 2023, as a voluntary tool to help organizations manage the risks associated with AI systems while promoting trustworthy and responsible AI development and use.
In July 2024, NIST published the Generative AI Profile (NIST-AI-600-1), an extension of the AI RMF that provides specific risk guidance for generative AI systems like large language models and image synthesis tools. This profile supports tailored risk mitigation strategies while aligning with the broader framework’s principles.
NIST also maintains a Trustworthy and Responsible AI Resource Center, which offers implementation tools, best practices, and use case examples to support organizations adopting the framework across sectors.
EU AI Act
The EU AI Act is a comprehensive legislation that ensures the safe and ethical development and deployment of artificial intelligence within the European Union. It was formally adopted on May 21, 2024, and entered into force on August 1, 2024, becoming the world’s first law regulating AI. The Act is designed to promote trustworthy, human-centric AI and is expected to influence global standards, much like the GDPR’s impact on data privacy.
It categorizes AI systems based on their risk levels—from minimal to unacceptable—and imposes specific obligations and restrictions accordingly:
- Minimal-risk systems, such as AI-powered games or spam filters, are not regulated under the Act.
- Limited-risk systems, like chatbots or AI-generated content tools, are subject to transparency requirements, such as notifying users that they are interacting with AI.
- High-risk systems, including those used in critical infrastructures, employment, healthcare, or law enforcement, must meet stringent requirements such as risk assessments, documentation, data governance, and human oversight.
- Unacceptable-risk systems are prohibited. These include AI used for cognitive behavioral manipulation, social scoring, predictive policing, and certain forms of biometric surveillance in public spaces.
The Act also introduces rules for general-purpose AI (GPAI) systems. GPAI models that do not pose systemic risks are subject to transparency obligations, while those that do must comply with more rigorous requirements.
To enforce these rules, the legislation establishes a robust governance framework, including:
- An AI Office within the European Commission
- A scientific panel of independent experts
- An AI Board with representatives from member states
- An Advisory Forum for stakeholder input
It sets penalties for non-compliance based on company size and global turnover, with proportional fines for SMEs and start-ups. Member States are also required to implement AI regulatory sandboxes and oversight structures by August 2026 to support responsible innovation.
This legislation represents a significant step in establishing legal standards for AI, requiring that systems be designed to ensure safety, fairness, and accountability throughout the EU.
How to Prepare for AI Governance
To prepare for emerging regulations for the development of AI, you can take the following steps:
- Conduct an AI Audit: Perform a comprehensive audit of your organization’s AI-based systems to identify potential risks or ethical concerns, ensuring the advancement and governance of AI align with established AI principles. This includes assessing data collection and usage practices, algorithmic decision-making processes, and impact on stakeholders.
- Develop an AI Ethics Framework: Create a policy outlining your organization’s values, principles, and policies for responsible AI development and use. This document should include guidelines for risk management, data privacy, bias mitigation, clarity, and accountability.
- Train Employees: Ensure that all employees involved in developing, deploying, or using AI-based technologies are trained on ethical considerations and best practices for AI governance.
- Implement Monitoring and Reporting Mechanisms: Establish monitoring and reporting mechanisms to track the performance and impact of your AI systems over time, using clearly defined governance metrics to measure outcomes such as accuracy, fairness, and potential bias.
- Stay Informed: Keep up-to-date with the latest developments in AI regulations by following relevant news sources, attending industry events, and engaging with experts in the field.
AI Governance Framework Examples
AI Governance policies can be applied across various industries to ensure responsible AI use and data security. Here are some industry-specific examples:
Healthcare
Patient Data Protection: AI Governance practices in healthcare ensure that AI systems only allow access to patient medical records and sensitive health data by authorized healthcare professionals. Data encryption, strict access controls, and anonymization techniques protect patient privacy and promote responsible use.
Clinical Decision Support: AI can enhance decision-making in medical diagnostics and treatment planning. AI Governance solutions ensure that AI recommendations align with medical ethics and regulations while maintaining data security.
Government
Public Safety: AI is being used for surveillance and threat detection, which means individuals might be monitored or tracked using technology. AI governance aims to ensure that data collected for security purposes is used within legal boundaries and that individual privacy is respected.
Public Services: AI in public services, such as healthcare or transportation, must adhere to strict data protection standards outlined in governance frameworks to maintain citizen trust.
Education
Personalized Learning: AI can tailor educational content for students. Governance ensures that student data privacy is maintained and AI platforms are used to improve learning outcomes without compromising security.
Administrative Efficiency: AI can optimize administrative processes. Regulations, in this case, can ensure AI protects sensitive student records and ensure compliance with data protection laws.
Retail
Personalized Marketing: AI-driven recommendation systems enhance customer experiences. Governance ensures customer data is used responsibly, anonymized when necessary, and protected against unauthorized access.
Inventory Management: AI helps optimize inventory levels. Governance guidelines ensure data accuracy and security in supply chain operations.
Best Practices for Implementing an AI Governance Framework
Here is a step-by-step approach for companies to use AI responsibly without compromising the use of sensitive data or risking exposure, ensuring compliance with artificial intelligence governance standards:
- Data Governance and Classification:
- Start by establishing a clear data governance protocol within your organization.
- Classify your data into categories based on sensitivity, ensuring that sensitive data is clearly identified and protected.
- Collect and retain only the data that is necessary for your AI applications.
- Implement strict access control measures.
- Conduct Privacy Impact Assessments (PIAs) to evaluate the potential risks to individuals’ privacy when implementing AI systems.
- Ensure that your AI models and algorithms are transparent and explainable.
- Implement measures to detect and mitigate bias in AI models, especially when dealing with sensitive data.
- Define data retention policies that specify how long sensitive data will be stored.
- Use secure and well-maintained data storage solutions.
- Train employees in responsible AI use and data handling practices.
- Consider engaging third-party auditors or experts to assess your AI systems for compliance and security.
- Develop a robust incident response plan in case of data breaches or security incidents.
- Continuously monitor the performance of your AI systems and data protection measures.
BigID’s Approach to AI Governance
BigID puts data privacy, security, and governance at the forefront of its solutions. Using advanced AI algorithms and next-gen machine learning, the platform enables organizations to understand their data better and comply with regulations while empowering them to discover their enterprise data in all its forms.
As the importance of AI governance continues to grow, BigID continues to deliver cutting-edge solutions that prioritize privacy, security, and compliance with a data-centric approach.
Find out how BigID enables data governance automation.
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