Tabular Data Anonymisation Solution for Safe Use in AI Systems Developed at HSE University
The AI and Digital Science Institute at the HSE Faculty of Computer Science has developed a tabular data anonymisation service designed to prepare corporate datasets for use in analytics and AI applications. The solution can identify personal data in structured datasets, apply consistent and reproducible anonymisation rules, and generate the artifacts required for quality control, auditing, and subsequent use of data in secure environments.
The solution addresses one of the key challenges of AI adoption in organisations: real-world data is essential for training, testing, and monitoring models, yet its direct use often carries the risk of exposing personal information. This challenge is particularly acute when working with data from corporate information systems, where information about users, employees, students, or clients is stored in the form of interconnected tables, identifiers, and attributes.
The service developed at HSE University addresses this challenge by combining processing rules, a replacement registry, and a reproducible anonymisation model. Given identical input data, the system produces consistent and predictable results, which is essential for the replicability of experiments, data quality assurance, and subsequent auditing. This approach preserves the structure of the dataset and maintains its suitability for analytical applications and AI use scenarios.
The solution is being developed in compliance with Russian personal data legislation and applicable requirements for data anonymisation. Its architecture provides for separate storage of source data and processing artifacts, as well as management of replacement rules, access controls, integrity checks, and a replacement registry. Together, these mechanisms enable the service to be integrated into a controlled AI data lifecycle management framework.
Currently, the service is used within HSE University's SmartMLOps platform to process data from the university's corporate information systems. Its applications include preparing data for analytics, testing, and the deployment of AI services. The solution can also be adapted for use in secure environments by organisations handling sensitive datasets, including those in education, healthcare, industry, finance, and government.
A separate line of development focuses on creating a version for unstructured data such as text documents, communications, contracts, and other materials in which personal data appears in free form. This version is currently under development and undergoing pilot testing. It will use a combination of rule-based methods, NLP (natural language processing) tools, and NEM (named entity recognition) models to identify personal data in texts while considering the context.
Hadi Saleh
'It is not enough for AI projects to simply have access to data. It is necessary to prepare data in a way that preserves its analytical value while ensuring that personal information is not disclosed. Our service addresses exactly this engineering challenge: it integrates anonymisation into a managed process for preparing data for AI,' said project leader Hadi Saleh, Head of the Unit for Applied Technological Solutions at the AI and Digital Science Institute, HSE Faculty of Computer Science.
The rights to the core components of the solution are reserved. The team envisions further development of the service both as an internal tool at HSE University and as a solution for deployment in secure environments within organisations that require data preparation for AI while complying with personal data protection requirements.
The project is ranked among the top 10 in the Data Security, Trust, and Quality category of the 2026 Gravitation International University Award in AI and Big Data.
The service was developed by the team of the Strategic Technological Project 'Multi-Agent AI Platform for Sectoral Solutions' as part of HSE University’s Development Programme for 2025–2036, supported under the Priority 2030 Strategic Academic Leadership Programme.
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