By Eric Caouette, VP Business Development at Coginov

Digital mapping of personal data using semantic technology is distinguished by its advanced approach, based on semantic analysis of data content. This methodology goes beyond the simple identification and classification of data, focusing on the meaning and context of the information processed. In the context of a privacy law such as Quebec’s Bill 25, this is an inescapable exercise if we are to meet certain legal requirements for the control and cleansing of data that has reached the end of its useful life.

Main features of digital data mapping with semantics:

1. Contextual understanding: Coginov’s QoreAudit uses semantic analysis to understand the context in which personal data is used. This enables more precise identification of the meaning of data, going beyond its simple format or location. 
2. Relationships and interconnections: Semantics enables the detection of relationships and interconnections between different data, providing a holistic view of information flows within the enterprise. Attention needs to be paid to the co-occurrence of personal data in the same file, which has a strong influence on the latter’s risk value. This includes understanding the links between different records and the way in which data coexist.
3. Automated analysis: Semantic tools like QoreAudit use automatic machine learning techniques, and artificial intelligence in natural language recognition to automate data analysis. This enables faster, more accurate mapping, by automatically identifying data types, and adapting the mapping to changes in the IT ecosystem.
4. Pattern and trend detection: Semantic analysis facilitates the detection of patterns and trends in the way personal data is processed. This can include recognizing patterns of behavior, access or use that may require special attention in terms of security or compliance. Some information processing rules and procedures can be derived from mapping analysis. For example: copy and duplicate management, access permissions, user rights and retention periods are just a few examples.
5. Dynamic cartographic evolution: Semantics enable dynamic mapping that evolves with changes in data and processes. This guarantees a real-time representation of the data ecosystem, which is crucial in a constantly evolving environment. Whether the information pool or structure grows or shrinks, the cartography will adapt immediately.
6. Natural language support: Some semantic tools can interpret natural language, facilitating communication between data security managers and non-technical users. This contributes to closer collaboration between different departments within the company. To be precise, depending on the sector of activity, several elements are used, such as linguistic rules and ontological dictionaries, to ensure the relevance of the mapping in context.
7. Integration with other management tools:

   Semantic solutions can be integrated with other data management, security, or compliance tools, strengthening a company’s ability to holistically address its data management challenges. Using semantics, personal data mapping tools like QoreAudit bring a more intelligent and contextual dimension to information management. They help companies to better understand their data, strengthen security and compliance, while dynamically adapting to changes in the IT environment.