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Transform every dataset into a secure, shared workspace for faster collaboration without privacy risk.

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Use LLMs on enterprise data — safely.

Mask sensitive fields before they reach the model. Compliance logging, policy control, on-prem deployment.

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Discover and trade verified synthetic datasets — all in one marketplace.

Buy, sell, and sponsor synthetic datasets with full transparency and compliance.

Built for data creators, buyers, and enterprise partners.

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Your AI is only as good as the data it trains on.

DTS solves unusable data — whether it's restricted, imbalanced, or missing coverage your model needs.

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Measure what matters.

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Redefining how organisations trust, protect, and use data.

CUBIG builds AI-driven systems that redefine data security — from generation and transformation to validation and integration.

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CUBIG's non-access architecture and differential privacy framework ensure 100% protection — no exposure, no compromise.

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READ MORE Award ISO-Certified AI-Ready Data Infrastructure | CUBIG Achieves 27001 & 42001
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Home / Glossary / LLM Audit Trail

What is LLM Audit Trail?

LLM Audit Trail refers to the record of how an LLM interaction was executed across prompts, transformations, policies, endpoints, and runtime conditions. It makes it possible to review what was used, how it was processed, and under which conditions it ran.

Related Glossaries

  • Enterprise data management Enterprise data management (EDM) is a strategic approach to handling an organization's data assets, ensuring consistency, security, and accessibility. It includes data governance, integration, quality management, and compliance, enabling businesses to optimize decision-making, enhance operational efficiency, and maintain regulatory standards.
  • Data Protection Directive The Data Protection Directive (95/46/EC) was a European Union directive that set data protection standards before being replaced by GDPR. It established rules for processing personal data within the EU, ensuring data privacy and free movement of information while maintaining…
  • Synthetic Data Synthetic data refers to artificially generated data that mimics real-world data while preserving privacy and security. It is used in AI training, testing environments, and data augmentation to enhance model performance.
  • Data engineering Data engineering is the field that focuses on designing, building, and maintaining data infrastructures. It involves ETL pipelines, data storage solutions, and database optimization to support analytics and AI-driven workflows.

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