AI governance is a rapidly evolving area, and 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, which refers to the rules, policies, and frameworks that govern the development, deployment, and use of AI technologies.

Governments and organizations around the world are recognizing the importance of responsible AI governance to ensure that AI is developed and used ethically, transparently, and in the best interest of society. This includes addressing concerns around privacy, bias, fairness, accountability, and safety.

For businesses, this means that they need to be aware of the emerging regulations and guidelines related to AI governance and ensure that their AI systems are designed and implemented in compliance with these standards. This may involve incorporating ethical considerations into their AI development processes, conducting regular audits and risk assessments, and providing transparency and explanations for AI-driven decisions.

Additionally, businesses may need to consider the potential impact of AI on their workforce and customers, and implement measures to address any negative consequences or mitigate risks.

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Why is AI Governance needed?

AI governance is needed in this digital technologies era for several reasons:

  • Ethical concerns: AI technologies have the potential to impact individuals and society in significant ways, such as privacy violations, discrimination, and safety risks. AI governance frameworks help ensure that these technologies are developed and used ethically and in the best interest of society.
  • Transparency: AI algorithms are often complex and opaque, making it difficult to understand how decisions are made. Governance frameworks promote transparency, which can help build trust in AI technologies and enable effective oversight.
  • Accountability: AI technologies can have significant impacts on individuals and society, and it is essential to hold those responsible for any negative consequences. Governance frameworks establish accountability mechanisms, such as liability and redress, to ensure that responsible parties are held accountable.
  • Regulatory compliance: Governments around the world are increasingly introducing regulations related to AI, such as data protection laws and ethical guidelines. Compliance with these regulations is critical for organizations to avoid legal and reputational risks.
  • Innovation: AI governance frameworks can foster innovation by providing clarity and certainty around the ethical and legal parameters within which AI technologies must operate. This can help organizations make informed decisions about the development and deployment of AI technologies.
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Pending and active AI Governance legislation

National Artificial Intelligence Initiative Act of 2020 (NAIIA)

The National Artificial Intelligence Initiative Act of 2020 (NAIIA) is proposed legislation aimed at advancing artificial intelligence research, development, and policy in the United States. If enacted, it could significantly impact AI Governance by setting standards, promoting responsible AI practices, and providing resources to strengthen AI capabilities while addressing potential regulatory challenges and ethical considerations.

Algorithmic Justice and Online Transparency Act

The Algorithmic Justice and Online Transparency Act is proposed legislation designed to regulate and bring transparency to algorithmic systems, particularly in online platforms. If enacted, it could shape the future of AI Governance by requiring accountability, fairness, and transparency in AI algorithms used in various online services, influencing how companies handle sensitive data and algorithmic decision-making.


The AI LEAD Act is proposed legislation focused on improving the development and use of artificial intelligence by addressing workforce development, research, and international collaboration. If enacted, it could positively impact AI Governance by fostering responsible AI practices, promoting AI research, and facilitating international cooperation on AI standards and regulations, contributing to a more ethical and secure AI ecosystem.

How to prepare for AI Governance

To prepare for emerging regulations for AI, organizations can take the following steps:

  1. Stay informed: Keep up-to-date with the latest developments in AI governance by following relevant news sources, attending industry events, and engaging with experts in the field.
  2. Conduct an AI audit: Conduct a comprehensive audit of your organization’s AI systems to identify any potential risks or ethical concerns. This includes assessing data collection and usage practices, algorithmic decision-making processes, and impact on stakeholders.
  3. Develop an AI ethics framework: Develop an AI ethics framework that outlines your organization’s values, principles, and policies related to responsible AI development and use. This framework should include guidelines for data privacy, bias mitigation, transparency, and accountability.
  4. Train employees: Ensure that all employees involved in developing, deploying, or using AI systems are trained on ethical considerations and best practices related to AI governance.
  5. Implement monitoring and reporting mechanisms: Establish monitoring and reporting mechanisms to track the performance and impact of your AI systems over time. This includes regular assessments of the system’s accuracy, fairness, and potential biases.
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AI Governance Framework Examples

AI Governance frameworks can be applied across various industries to ensure responsible AI use and data security. Here are some industry-specific examples:

Healthcare AI

Patient Data Protection: AI Governance frameworks in healthcare ensure that patient medical records and sensitive health data are accessed only by authorized healthcare professionals. Data encryption, strict access controls, and anonymization techniques protect patient privacy.

Clinical Decision Support: In medical diagnostics and treatment planning, AI can be used to enhance decision-making. Governance frameworks ensure that AI recommendations align with medical ethics and regulations while maintaining data security.

Government AI

Public Safety: AI is used for surveillance and threat detection. Governance frameworks 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 AI

Personalized Learning: AI can tailor educational content for students. Governance ensures that student data privacy is maintained, and that AI systems are used to improve learning outcomes without compromising security.

Administrative Efficiency: AI can optimize administrative processes. Governance frameworks protect sensitive student records and ensure compliance with data protection laws.

Retail AI

Personalized Marketing: AI-driven recommendation systems enhance customer experiences. Governance ensures that customer data is used responsibly, anonymized when necessary, and protected against unauthorized access.

Inventory Management: AI helps optimize inventory levels. Governance frameworks ensure data accuracy and security in supply chain operations.

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Ensuring Responsible AI

Here is a step-by-step approach for companies to use AI responsibly without compromising the use of sensitive data or risking exposure:

  • Data Governance and Classification:
    • Start by establishing a clear data governance framework within your organization.
    • Classify your data into categories based on sensitivity, ensuring that sensitive data is clearly identified and protected.
  • Data Minimization:
    • Collect and retain only the data that is necessary for your AI applications.
    • Avoid unnecessary data collection to minimize the risk associated with storing sensitive information.
  • Access Control:
    • Implement strict access control measures.
    • Ensure that only authorized personnel have access to sensitive data, and use role-based access control (RBAC) to manage permissions.
  • Privacy Impact Assessments:
    • Conduct Privacy Impact Assessments (PIAs) to evaluate the potential risks to individuals’ privacy when implementing AI systems.
    • Address identified risks and implement necessary safeguards.
  • Transparency and Explainability:
    • Ensure that your AI models and algorithms are transparent and explainable.
    • Provide clear documentation on how the AI system processes sensitive data and makes decisions.
  • Bias Mitigation:
    • Implement measures to detect and mitigate bias in AI models, especially when dealing with sensitive data.
    • Regularly monitor and update your models to reduce bias.
  • Data Retention and Deletion:
    • Define data retention policies that specify how long sensitive data will be stored.
    • Implement secure data deletion processes when data is no longer needed.
  • Secure Data Storage and Processing:
    • Use secure and well-maintained data storage solutions.
    • Ensure that AI systems are protected from cyber threats by employing robust cybersecurity measures.
  • Compliance with Regulations:
    • Stay informed about relevant data protection regulations and privacy laws, such as GDPR, HIPAA, or CCPA.
    • Ensure that your AI practices align with these regulations.
  • Employee Training and Awareness:
    • Train employees on responsible AI use and data handling practices.
    • Foster a culture of data privacy and security within your organization.
  • Third-Party Audits:
    • Consider engaging third-party auditors or experts to assess your AI systems for compliance and security.
    • External audits can provide an objective evaluation of your data protection measures.
  • Incident Response Plan:
    • Develop a robust incident response plan in case of data breaches or security incidents.
    • Ensure that your team knows how to respond swiftly and effectively to mitigate any potential damage.
  • Continuous Monitoring and Improvement:
    • Continuously monitor the performance of your AI systems and data protection measures.
    • Be prepared to adapt and improve your practices as technology evolves and new risks emerge.

BigID’s Approach to AI Governance

BigID’s approach to AI governance sets it apart in the industry by putting data privacy, security, and governance at the forefront of its solutions. Through its use of advanced AI algorithms and next-gen machine learning, BigID enables organizations to better understand their data and comply with regulations, while also empowering them to discover their enterprise data in all its forms. BigID’s comprehensive platform provides a holistic solution for data lifecycle management— giving organizations the tools to automatically and accurately scan, identify, classify, and tag all their sensitive data without the hazard of human error.

As the importance of AI governance continues to grow, BigID remains at the forefront of the conversation, delivering cutting-edge solutions that prioritize privacy, security, and compliance with a data-centric approach.

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