AI governance refers to the framework of policies, regulations, ethical principles and guidelines that govern the development, deployment and use of Artificial Intelligence (AI) systems. AI governance helps ensure redesigned or new AI-enabled business processes and workflows are implemented responsibly and transparently.
AI governance faces several significant challenges. Rapid technological advancement often outpaces regulatory frameworks, creating gaps in oversight. Balancing the need for innovation with effective risk management adds further complexity, as promoting progress must be weighed against ensuring safety and fairness. Global cooperation is also essential but difficult to achieve, as countries must align on standards despite differing political and cultural views.
Additionally, the complexity and opacity of AI models make accountability and transparency hard to enforce, complicating regulatory efforts. These challenges call for adaptive policies, ongoing dialogue and cross-sector collaboration to keep governance in step with evolving AI technologies.
AI governance: an integral part of enterprise strategy
AI governance can help organizations to accelerate responsible, transparent and explainable AI workflows. It also helps companies to direct, manage and monitor their organization’s AI activities. A sound AI governance strategy can help companies minimize risk while strengthening its ability to meet ethical principles and government regulations. If a company wants to operate successfully internationally, meeting internal policies and external regulatory and compliance obligations, then AI governance must be an integral part of company’s enterprise AI strategy.
Successful and responsible AI requires an awareness of local, regional and national laws and regulations, which are growing at a rapid pace. Noncompliance might ultimately cost an organization millions of dollars in fines and a loss of reputation. If organizations do not want to run afoul of these regulations, they must consider using AI governance to effectively govern the creation, deployment and management of their AI/GenAI services throughout the AI/GenAI lifecycle.
As explored in the new Artificial InteIligence Governance Brief from ISACA, AI governance must be evidenced throughout the AI lifecycle to realize its benefits in achieving the enterprise business strategy and minimizing inherent risk. The oversight provided by the AI governance function will help enable responsible, explainable, high-quality AI models by ensuring the appropriate policies are in place, and controls and compliance frameworks are established.
To bridge the gap between AI governance strategy and implementation details, adopt practical measures, from establishing robust oversight mechanisms to embedding ethical decision-making into daily operations, such as:
- Establishing governance structures, including accountability frameworks.
- Implementing policy development and ethical guidelines in terms of defining ethical AI principles, developing AI usage policies and implementing bias mitigation strategies.
- Risk management and compliance implementation by conducting regular risk assessments and ensuring adherence to regulatory details.
- Integrating ethical decision-making into AI development by implementing ethics by design.
- Implementing data governance and model transparency for the AI model lifecycle.
Operationalize AI governance by accelerating model building, and by automating and consolidating multiple tools, applications and platforms.
Comprehensive governance throughout the lifecycle
AI governance must include a few critical characteristics to be truly effective. First and foremost, it should be comprehensive, overseeing the entire AI lifecycle from start to finish. This includes capturing relevant metadata at every stage, ensuring that the governance framework covers all aspects of model development, deployment and monitoring.
Additionally, AI governance should be open, providing full visibility of all AI models across the enterprise ecosystem. This openness fosters transparency and allows stakeholders to understand how models are created, used and managed within the organization.
Finally, AI governance must be automatic, with automated processes for capturing metadata, data transformations and data lineage. Automating these aspects ensures consistency, efficiency and reduces the potential for human error, allowing for seamless oversight and traceability of AI operations. Together, these features create an AI governance system that is thorough, transparent and efficient.
Realize AI governance benefits
With the above elements in place, organizations can realize significant AI governance benefits. The benefits of comprehensive AI governance include:
- Monitors, catalogs and governs models (on-prem, cloud), across the AI lifecycle
- Provides an accurate, always up-to-date view of model details
- Assists with automatic metadata, data transformation, and data lineage capture, and captures model metadata for effortless report generation
- Accesses automated, scalable governance, risk, and compliance (GRC) tools
- Integrates internal governance, risk, and compliance policies and practices with external regulatory guidelines and laws
- Ensures translation of external AI regulations into enforceable policies for automated enforcement
- Increases stakeholder visibility and communications
- Detects model bias and drift with the capability of alerting stakeholders when thresholds/guardrails are exceeded
AI implementations continue to grow across the enterprise landscape, but for organizations to derive the value they are looking for in responsible and sustainable fashion, sound AI governance is a necessity. Find out more by downloading ISACA’s Artificial Intelligence Governance Brief.
