🌱 Welcome from CUBIG: enabling safe data use in a regulated world

As data-driven innovation accelerates, organizations around the world face a common challenge: how to use data effectively without compromising privacy. In a recent Japanese media interview, CUBIG was introduced as a company tackling this challenge at its core.

The article highlights CUBIG’s mission to protect sensitive data while making it practically usable for analysis and AI development. Rather than treating privacy and utilization as opposing goals, CUBIG approaches them as two elements that must be designed together.

This philosophy sets the foundation for CUBIG’s technology and global strategy.

🔐 Zero-Access synthetic data as a structural solution

One of the key points emphasized in the interview is CUBIG’s Zero-Access architecture for synthetic data generation.

CUBIG creates high-fidelity synthetic data without directly accessing original datasets, preserving statistical characteristics while eliminating re-identification risk.

By applying Differential Privacy, the system maintains analytical value while structurally protecting personal and confidential information.

This approach supports tabular, text, image, and time-series data within a single framework.

It is particularly effective in highly regulated environments such as finance, healthcare, and the public sector.

🤖 Practical AI adoption with LLM Capsule

The article also highlights LLM Capsule, CUBIG’s solution for secure use of generative AI. While many organizations want to adopt tools like ChatGPT or Claude, concerns around data leakage and internal network restrictions remain major barriers.

LLM Capsule allows enterprises to use large language models without sending sensitive data outside their environment.
Compared to building private LLMs, it significantly reduces cost and deployment time.

This positions CUBIG as a practical enabler of enterprise-grade generative AI adoption.

📊 Proven performance in public and private sectors

CUBIG’s synthetic data technology has already been validated across public and private sectors in Korea.
The system preserves over 99 percent of the original data’s statistical structure while correcting bias, imbalance, and missing values.

By treating synthetic data as a quality-improvement engine rather than a simple anonymization tool, CUBIG enhances AI training performance.

Use cases include document anonymization, policy simulation, internal AI deployment, and financial risk analysis.

Organizations value the ability to conduct AI analysis without exporting sensitive original data.

🌍 Expanding globally under diverse regulatory environments

The interview underscores that data protection challenges are not unique to Japan. In Europe, GDPR has accelerated demand for synthetic data and AI privacy technologies, leading CUBIG to establish a UK subsidiary.

In the United States, rapid adoption of generative AI is also increasing the need for data protection solutions.

CUBIG’s strategy focuses on adapting its technology to each region’s regulatory and industrial context.

Through this approach, the company aims to set a new global standard for safe data utilization.

🚀 A vision for a connected and privacy-safe data ecosystem

CUBIG’s long-term vision is to create a world where data can be used freely without compromising security. Protection mechanisms should not create friction but instead enable confident collaboration and innovation.

By reconnecting previously unusable data to AI and analytics through synthetic data, CUBIG is building a new data environment.

The company aims not just to deliver products, but to establish a comprehensive data infrastructure ecosystem.

Together with global partners, including those in Japan, CUBIG plans to expand this vision worldwide.

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