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Governance Consulting
One-stop support,
through AI governance establishment.
As AI projects proliferate and organizational demands grow, concerns about AI governance grow. Faced with challenges such as unclear responsibilities, abstract ethical standards, regulatory opacity, and insufficient organizational capacity, even large corporations are experiencing trial and error as they establish a strategic foundation for securing corporate trust and mitigating risks. Daewon CTS, in partnership with key partners, provides one-stop consulting services, from establishing an AI governance system to implementing it.
A comprehensive approach is
Necessary tasks
As the adoption of generative AI by enterprises has surged in recent years, AI has become a core component of the business value chain. However, the expanded use of AI technologies also introduces new security risks and governance challenges for organizations. Companies must establish effective security response frameworks and governance structures that can identify and manage potential risks at every stage of AI implementation. By doing so, they can ensure customer trust and regulatory compliance while minimizing technical and cultural risks and maximizing the positive impact of AI.
Challenge
Challenges
related to AI governance
Legal and technical risks
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Violations of data protection regulations due to excessive collection and analysis of personal data by AI services
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Copyright ownership issues related to generative AI outputs and user harm caused by misinformation
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Inaccurate outcomes resulting from data bias
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Safety incidents caused by opaque AI decision-making processes and model malfunctions
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These risks can lead to reputational damage and financial losses for enterprises
The Difficulty of Establishing AI Ethical Standards
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The issue of biased statements by chatbots has raised awareness of AI ethics among enterprises.
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Most organizations have only declared “trustworthy AI”, but concrete methods for applying it to product and service development remain unclear.
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Even experts provide mostly abstract definitions, acknowledging the difficulty of offering actionable guidance.
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The majority of organizations lack internal ethical standards or training programs.
Lack of clarity about responsibility for AI results
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Unclear accountability when AI system issues occur, with ambiguity over whether responsibility lies with developers, users, or vendors
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Legal experts highlight a “diffusion of responsibility” phenomenon, emphasizing the need for accountability to be defined in advance
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Most enterprises lack formalized policies and processes that clearly specify responsibility and accountability
Ambiguity and uncertainty in the interpretation of relevant regulations
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AI governance–related regulations are still in the establishment phase, with unclear interpretation and compliance requirements.
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The AI Basic Act, scheduled to take effect in 2026, provides only broad guidance, without detailed instructions for development or operations.
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High-risk AI operators are required to implement safety and reliability measures, but the specifics are delegated to subordinate regulations, leaving details ambiguous.
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Enterprises need to closely monitor forthcoming detailed rules and identify mandatory actions and preparatory measures prior to enforcement.
Service
Optimization services
DIA NEXUS focuses on.
AI use case analysis
Identify AI model use cases by business function and analyze the organization’s AI adoption level and business outcomes to assess the current situation.
Establishment of an AI governance framework
Starting with the design of an AI governance framework, establish criteria for managed assets, define organizational roles and responsibilities, and set AI operational policies and guidelines to build the AI governance process.
Security and regulatory compliance
Select AI models that require evaluation and define validation metrics to assess their performance.
