🔐 Strengthening AI Security: CUBIG’s Synthetic Data and LLM Capsule

Concerns over Large Language Model (LLM) security are rapidly growing.

Recent cases — such as DeepSeek’s AI model being banned in certain countries and OpenAI’s data collection practices — highlight the risks of data breaches 🔓, unauthorized storage, and hidden backdoors.

This poses a serious threat to both enterprises and individual users.

⚠️ Why Stronger AI Security Is Needed

While some companies are attempting to build their own secure LLMs, this is not a complete solution.

• Sensitive training data can still be exposed.

• Backdoor attacks may compromise even private models.

What’s needed is a comprehensive approach 🛡️ that protects not only the models but also the data they learn from.

🛠️ CUBIG’s Solutions: DTS and LLM Capsule

CUBIG addresses these challenges with two core technologies:

• DTS (Data Transform System): Generates privacy-preserving synthetic data that retains up to 99% of the utility of the original dataset while removing sensitive information.

• LLM Capsule: Acts as a security filter 🔍, detecting and anonymizing personal information at the input stage — ensuring safe AI chatbot usage for both enterprises and individuals.

🤝 Building Trust in AI

Users must adopt a more cautious mindset:

• Avoid inputting sensitive data 🔒 into AI services.

• Review data collection and storage policies.

• Implement protective tools like CUBIG’s LLM Capsule.

By combining secure synthetic data with real-time filtering, CUBIG is helping shape a safer AI ecosystem where privacy is never compromised.

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