2026.06.09(화) 19:26
교육타임즈 지면신문
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이번 로드맵은 기존에 각기 운영 중이던 기술 연구개발(R&D)과 표준화 로드맵을 통합·연계하여 조기 개정한 것이다. 최근 에이전틱·피지컬 인공지능(AI) 등 신기술 확산으로 개인정보 처리 환경이 급변하면서 개인정보 유·노출, 인공지능 학습데이터의 노출·재식별 등에 대한 우려가 증가함에 따라 선제적 대응 체계를 마련하기 위해 추진됐다. 개인정보위는 ▲개인정보 주권보장 ▲유·노출 위험경감 ▲신뢰기반 안전활용 ▲인공지능(AI) 대응 기술개발 등 4대 분야에서 11대 핵심기술을 선정했다. 여기에는 AI 모델 학습·추론 과정의 안전성 평가와 에이전틱·피지컬 AI 환경의 오·남용 방지 기술이 신규 반영됐다. 아울러 가명·익명처리, 합성데이터, 동형암호 등 개인정보보호 강화 기술(PET)과 인공지능을 융합한 AI-PET 기술 연구를 추진하고, 향후 10년간의 전문인력 양성 방향을 담은 「개인정보 분야 전문인력 양성 로드맵(2026~2035)」을 함께 추진할 계획이다.
정부가 급변하는 AI 기술 트렌드에 발맞춰 기존 분절되어 있던 R&D와 표준화 로드맵을 하나로 통합하고, 에이전틱 및 피지컬 AI 환경의 위험성까지 시의성 있게 반영한 청사진을 제시한 것은 기술과 제도의 연속성을 확보한다는 측면에서 진일보한 행정으로 평가된다. 특히 단순히 제도적 규제에 머물지 않고 보호 강화 기술(PET)과의 융합 등 기술적 해결책을 도모하고 중장기 인력양성 방안까지 연계한 점은 생태계 전반을 고려한 실용적인 접근으로 보인다.
다만 이러한 방대한 R&D 및 표준화 로드맵이 선언적 계획에 그치지 않고 시장에 안착하기 위해서는, 공공 주도의 기술 개발이 실제 글로벌 빅테크 기업들이 주도하는 초거대 AI 고도화 속도를 따라잡을 수 있도록 규제 샌드박스와 연계한 민간 실증 인프라가 실질적으로 뒷받침되어야 한다는 지적도 나온다. 고도의 기술력이 요구되는 AI-PET나 동형암호 등의 상용화는 막대한 자본과 인력이 투입되는 영역인 만큼, 단기적인 정부 과제 중심의 지원을 넘어 민간 기업이 자발적으로 보안 기술을 도입하도록 유도하는 세제 혜택이나 인센티브 제도 마련 등이 향후 해결해야 할 과제로 꼽힌다.
[영문번역 기사-AI활용]
Ko Hak-soo, chairperson of the Personal Information Protection Commission, stated, "As new privacy risks are increasing along with the development of artificial intelligence technology, the importance of technological research and development to prevent them in advance is growing even more. Based on the revised roadmap, we will develop commercializable personal information protection and utilization technologies that reflect field demands, and organically link them with domestic and international standardization and expert training to build a personal information protection and utilization technology ecosystem necessary for the AI era." The Personal Information Protection Commission announced that it has established and disclosed the 'Personal Information Full Life-Cycle Protection and Utilization Technology R&D and Standardization Roadmap (2026–2030)' to support the protection and safe utilization of personal information in an AI and data-centered society and to create a trusted environment suited to the AI era.
This roadmap is an early revision that integrates and links the technological research and development (R&D) roadmap and the standardization roadmap, which had previously been operated separately. It was prepared to proactively respond to changes as concerns grow over the exposure and leakage of personal information, as well as the exposure and re-identification of AI training data, due to the rapid shift in the personal information processing environment driven by the recent proliferation of new technologies such as agentic and physical artificial intelligence (AI). The Commission selected 11 core technologies across four major areas: △Guaranteeing personal information sovereignty, △Mitigating exposure and leakage risks, △Safe utilization based on trust, and △Developing technologies responding to AI. New elements were added, including safety evaluations for potential personal information leakage risks during the AI model training and inference processes, and technologies to prevent personal information misuse in agentic and physical AI environments. Furthermore, it plans to pursue research on AI-PET technology, which merges AI with Privacy Enhancing Technologies (PET) such as pseudonymization, synthetic data, and homomorphic encryption, while simultaneously pushing forward the 'Personal Information Expert Training Roadmap (2026–2035)' that outlines the direction for human resource development over the next 10 years.
The government's presentation of a blueprint that integrates the previously fragmented R&D and standardization roadmaps to keep pace with rapidly changing AI technology trends, while timely reflecting the risks of agentic and physical AI environments, is evaluated as a progressive administrative step in terms of ensuring continuity between technology and systems. In particular, moving beyond simple institutional regulations to seek technological solutions, such as integration with Privacy Enhancing Technologies (PET), and linking this with medium- to long-term human resource development plans appears to be a practical approach that considers the ecosystem as a whole.
However, critics point out that for such an extensive R&D and standardization roadmap to settle into the market rather than remaining a declarative plan, a private sector verification infrastructure linked with regulatory sandboxes must be practically supported so that public-led technological development can keep up with the advancement speed of hyper-scale AI driven by global big tech companies. Since the commercialization of areas requiring high-level technological capabilities, such as AI-PET or homomorphic encryption, involves massive capital and manpower, moving beyond short-term government project-oriented support to establish incentive systems or tax benefits that encourage private companies to voluntarily adopt security technologies is cited as a future task to be resolved.
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가요진 기자 gyj1119@naver.com
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