Responsible AI Technical Report

Systematic Risk Assessment is Essential: KT developed a comprehensive AI risk taxonomy tailored to the domestic environment, demonstrating that organizations need structured methodologies to systematically identify and manage potential risks across the entire AI lifecycle—from development through operation—rather than addressing safety concerns ad hoc.

Regulatory Compliance Requires Proactive Alignment: By analyzing the Basic Act on AI implementation and global AI governance trends, the research shows that responsible AI development must anticipate regulatory requirements early, enabling organizations to build compliance into their systems from the start rather than retrofitting safety measures later.

Real-Time Safety Tools Are Practical Solutions: The release of Guardrail: SafetyGuard—a technology that blocks harmful AI responses in real-time—illustrates that responsible AI isn't just theoretical; practical, deployable tools can actively prevent unsafe outputs and enhance safety in operational AI systems.

Model Robustness Verification is Measurable: The report presents a reliable assessment methodology that systematically verifies both model safety and robustness, suggesting that organizations can objectively evaluate whether their AI systems meet safety standards rather than relying on subjective judgments.

Shared Frameworks Benefit the Entire Ecosystem: By releasing both their assessment methodology and proprietary safety tools, KT demonstrates that responsible AI practices can be collaborative, providing valuable insights and resources that help other organizations develop safer AI systems across the industry.