AI Governance for Generative AI: Ensuring Compliance, Mitigating Risks, and Maximizing Business Value

Generative AI-Driven Business: Four Keys to Success for Producing Outstanding Results

Part 2: Addressing AI Governance Demanded by Updated Laws and Regulations

Generative AI is increasingly integrated into business operations, with technologies like Retrieved-Augmented-Generation (RAG) becoming common. RAG uses internal documents to generate responses via large language models (LLMs). Since 2025, AI agents capable of autonomous tasks have also gained traction, fueled by deep research breakthroughs.

While these AI tools are more accessible, they carry risks. Examples include accidental uploads of confidential data to AI platforms and chatbots suggesting illegal actions. As AI use deepens, regulatory scrutiny grows worldwide.

The European Union led the way with the European Artificial Intelligence Act (AI Act), effective August 1, 2024. This law applies to any company offering AI systems within the EU, regardless of location, with penalties reaching up to 35 million euros or 7% of global annual sales.

The G7 plans to require generative AI developers to report on risk management, security, safety, and investments starting February 2025. These reports will be public on the OECD website. Meanwhile, South Korea enacted the AI Basic Act in December 2024, and Japan’s METI and MIC released “AI Guidelines for Business” in April 2024, updated in March 2025.

Japan is also considering a “Bill on the Promotion of Research, Development and Utilization of Artificial Intelligence-Related Technologies,” which may introduce a central government body for AI oversight, mandatory cooperation from companies, and public naming of offenders.

Public awareness is rising alongside regulatory pressure. In October 2024, Japanese voice actors campaigned against unauthorized use of their voices to train generative AI, highlighting ethical concerns. Both AI developers and users face growing demands for responsible AI governance.

Risks of Using Generative AI

Generative AI introduces new social risks such as intellectual property violations and the creation of false or misleading information. Challenges vary depending on organizational roles. Below are key considerations for leadership, business, IT, and legal departments.

Leadership

• Align AI governance with corporate strategy and societal impact.
• Prevent data leaks and incorrect decision-making.
• Track international AI governance trends and ethical standards.
• Define leadership roles and coordinate governance across group companies, especially for emergency responses.

Business Departments

• Support employees to use generative AI correctly to boost efficiency.
• Develop usage rules, guidelines, and verification checklists.
• Train staff on AI hallucinations, copyright issues, and adapting to evolving tools.

Digital/IT Departments

• Ensure AI security, safety, ethics, and accountability.
• Centralize AI tool management with internal registries.
• Implement defenses against prompt injections and related attacks.
• Evaluate deployment risks and communicate clearly with other departments.

Legal Departments

• Manage compliance with data protection, copyright, and privacy laws.
• Oversee contracts like licensing and data-sharing agreements.
• Raise awareness among AI users about legal risks.
• Establish frameworks to review and mitigate legal exposure.

AI Governance Required by Companies

Creating AI policies and organizational structures that balance effective AI use with risk control is essential. Japan’s “AI Guidelines for Business” recommend agile governance updated regularly to keep pace with societal changes rather than fixed rules.

Agile governance is best approached in three phases: assessment, strategy formulation, and implementation. Support should cover organizational design, personnel development, system deployment, and business operations.

Assessment Phase

Assess your current AI governance status. Identify existing generative AI use cases, known risks, and issues. The governance level depends on your AI systems, data, contracts, and users—accurate understanding is crucial.

Strategy Formulation Phase

Conduct research on regulations and industry practices. Develop an AI code of ethics, perform ethical risk analyses, and create a governance roadmap aligned with business goals and sustainability strategies.

Implementation Phase

Deploy governance across four perspectives:

• Organization: Define roles and update structures to manage AI risks, especially for companies offering AI services externally. Risk management should span legal, IT, and business teams.
• Talent: Specify required skills, assess current capabilities, and implement ongoing training on personal information, intellectual property, and AI ethics aligned with legal updates.
• System: Set system requirements, select vendors, deploy tools, and educate end users. Identify risks specific to your AI tools and apply tailored governance solutions.
• Business: Standardize AI projects with checklists and risk analysis. Monitor AI outputs and adapt governance continuously to emerging challenges.

These phases help build an adaptable governance framework that evolves with social and technological changes. With AI agents gaining adoption, risk management must cover not only data inputs and outputs but also autonomous tasks performed by AI.

A Future with AI Governance

Establishing strong AI governance enables companies to identify risks related to generative AI and build the necessary organizational structures and rules to manage them. Evaluating and mitigating risks proactively helps maximize AI benefits while maintaining control.

Addressing AI governance is a strategic priority that can reduce future liabilities and enhance brand value. Implemented well, it drives profit and innovation without becoming a cost burden.

For those interested in expanding their AI knowledge and skills to support governance and practical use, consider exploring Complete AI Training for relevant courses and resources.