Organizations are under pressure to move quickly with their AI initiatives and become AI-ready. However, scaling these projects securely can slow momentum to becoming AI-ready quickly. One way to address this is to build strong AI governance so that the transition to AI readiness is fair, transparent, and reliable, ensuring data protection, privacy, and security.
The basics of AI governance
Just like we need data governance to ensure that organization-wide governance aligns with security efforts, the same is true for AI governance. AI governance is essential for ensuring that the development of AI initiatives is instituted ethically, transparently, and accountable across the organization uniformly.
You can reduce data security risks by developing formal AI governance programs that define your organization’s oversight and coordination procedures for using data and analytics with AI. These procedures and oversight limit biases and reduce the likelihood of data misuse while enabling compliance with AI regulations.
Effective AI governance standards enhance the safety and reliability of AI models and reduce data security risks. A strong governance framework weighs AI technological advances with ethical considerations to develop an organization-wide secure and ethical framework.
What is AI governance?
Artificial intelligence governance is the overall management process that ensures the security and privacy of sensitive data across the organization. This includes the data fed to AI and large language models (LLM) models and how AI algorithms use this sensitive and personal data. AI governance oversees policies, standards, technologies, and controls to ensure that any sensitive information is protected and ethically used.
Data leaders, particularly Data and Analytics governance teams, must ensure that data is AI-ready. However, a critical step in having AI-ready data is that it is properly governed.
A strong AI governance framework includes stakeholders at all levels to ensure that security and risk management are coordinated and an organizational priority. Ultimately, it provides a balanced approach to developing AI-ready data, including risk management, compliance, and usability.
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Key elements of AI governance
Some key elements are necessary for a strong artificial intelligence governance framework governance so that AI systems are ethical, transparent, and accountable. These elements include.
- Data stewardship: A data steward oversees the entire process. This individual monitors observable metrics and ensures policies are applied and defined as designed. They are responsible for AI governance policy enforcement, including data management.
- Compliance: Governance must include staying abreast of the evolving AI standards and regulations. These include the General Data Protection Regulation (GDPR) regulations and the still-developing AI EU Act. Knowing and complying with regulations is necessary to ensure that data privacy and security are upheld.
- Ethical requirements: Ethical AI guidelines are there to prevent harm, so while it may be legal, AI governance should be concerned with whether it is ethical to train models using real data. AI governance must develop ethical requirements that align with the organization’s overall ethical outlook.
- Transparency: There needs to be clarity and understanding about the different processes involved within AI systems, including decision-making. This transparency can help mitigate data biases and anticipate any underlying biases.
- Robustness and safety: Reliable, secure, and safe AI systems are necessary. AI governance should include a system that includes testing, validation, and ongoing monitoring to prevent data security threats and ensure ethical outcomes.
- Inclusive participation: Since all stakeholders are included in the governance of AI systems, their perspectives should be included. This provides a more equitable and inclusive AI initiative.
- Accountability: Governance should include a clear feedback loop. So that there are mechanisms to address issues that arise from the development and deployment of AI systems and hold the responsible parties accountable.
Including these AI governance principles helps organizations balance their goal of becoming AI-ready. This holistic approach fosters trust and reliability in AI applications and reduces the impact of AI risk.
Implementing AI governance best practices
Some best practices for making sure you’re implementing AI governance well:
- Assign Data Stewards: Designate specific people responsible for overseeing data governance. This ensures accountability and reduces confusion.
- Define Responsibilities: Once you’ve chosen your data stewards, it’s important to define their responsibilities clearly. Typically, this will include data quality management, access control enforcement, and compliance monitoring.
- Use Automation Tools: Data validation, quality monitoring, and access management are time-consuming, so it’s best to automate them when possible.
- Build a Data-Driven Culture: Make sure that everybody at your organization, not just your data team, knows that data governance is a priority. This might involve training and resources to team members on the importance of data governance.
Conclusion
AI advancements mean businesses need to ensure AI reliability and speed as they transition to becoming AI-ready. This transition must include proper data security, compliance, and privacy governance. An effective AI governance program provides a defined set of procedures and coordinated policies for your organization’s AI management and displays your organization’s commitment to data security.
Satori’s Data Security Platform for AI & Analytics gives data teams visibility and policy enforcement by governing access to internal data for LLM using dynamic security policies and automatic access management that simplifies permissions.
