Mitsubishi UFJ Bank deploys private artificial intelligence to secure generative artificial intelligence usage

Mitsubishi UFJ Bank is using Private AI’s anonymization technology on its OCEAN platform to unlock generative artificial intelligence while maintaining strict financial-grade data protection. The deployment aims to safely tap unstructured data across emails, call recordings, internal documents, and chat logs without exposing personal information.

Mitsubishi UFJ Bank has adopted a private data anonymization solution from Toronto-based Private AI to support secure, large-scale generative artificial intelligence on its OCEAN platform. The move is positioned as a response to the challenge of handling vast volumes of unstructured data such as emails, call center recordings, internal documents, PDFs, and chat logs while maintaining strict data protection and governance standards. By integrating Private AI’s technology, the bank can automatically detect and hide sensitive personal and confidential information before it is processed by generative artificial intelligence systems, aligning innovation with the tighter regulatory expectations placed on financial institutions.

Private AI’s solution, founded in 2019 and focused on privacy and machine learning, uses specialized algorithms that can anonymize over 50 kinds of personally identifiable information in 52 languages. Mitsubishi UFJ Bank runs this capability in a closed, on-premise environment that operates in real time so that sensitive data stays within its own infrastructure and is not sent to external cloud services. This architecture allows the bank to advance its generative artificial intelligence and analytics agenda while preserving OCEAN’s existing governance framework and reducing the risk of data leakage as artificial intelligence models become more deeply embedded in critical operations.

The deployment is designed to unlock the long underused value of unstructured data in financial services by safely handling free text and audio that may contain names, addresses, phone numbers, account numbers, and insurance card details, with accuracy supported by technical testing on real business data. Once anonymized, unstructured data can be linked to the enterprise data lake and analyzed alongside structured data, enabling use cases such as document summarization, internal knowledge discovery, and conversational artificial intelligence while preserving privacy. Mitsubishi UFJ Bank plans to extend the technology beyond OCEAN into call center optimization, fraud and risk management, and enterprise knowledge improvement, offering a blueprint for Japanese banks, insurers, and other regulated organizations to combine generative artificial intelligence adoption with compliance, privacy-first design, and long-term resilience in a data-driven economy.

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