Is it easy to identify the personal information contained in an organization's electronic documents, including e-mails?
By Lucie Bélanger, Senior Archivist and Product Manager at Coginov
Identifying personal information contained in an organization’s electronic documents, including emails, can be a complex and challenging task. It depends on various factors, including the volume of documents, the diversity of document formats, the quality of data organization, and the specific content within the documents. Here are some factors that can make the identification process more challenging:
Unstructured Data: Electronic documents often contain unstructured data, such as free-form text in emails or scanned documents. Unstructured data lacks a predefined format, making it more difficult to identify and extract personal information. Analyzing and understanding unstructured data requires advanced techniques, such as natural language processing (NLP) and text mining.
Document Formats: Electronic documents come in various formats, such as PDF, Word documents, spreadsheets, or image files. Each format may require different techniques and tools to identify personal information effectively. Extracting personal information from non-textual formats, such as images or scanned documents, can be particularly challenging.
Contextual Understanding: Identifying personal information requires contextual understanding and domain knowledge. Personal information can take different forms and vary across industries and jurisdictions. It may include names, addresses, identification numbers, financial data, health information, or other sensitive details. Understanding the specific types of personal information relevant to the organization’s context is crucial.
Data Volume: Organizations often have a large volume of electronic documents, including a vast number of emails. Manually reviewing each document and email for personal information is time-consuming, resource-intensive, and prone to errors. Automation through machine learning and data analysis techniques can significantly improve efficiency in identifying personal information within large volumes of data.
Data Fragmentation: Personal information may be scattered across multiple documents or email threads. Piecing together fragmented information and identifying personal data elements can be challenging, especially when there is no consistent structure or clear linking between related documents.
Privacy by Design: Organizations may adopt privacy protection measures, such as data masking or encryption, to safeguard personal information within their electronic documents. These measures can make it more difficult to identify personal information without the appropriate decryption or access rights.
Despite the challenges, advancements in machine learning, NLP, and data analysis techniques have made it possible to automate and improve the identification of personal information within electronic documents. Implementing machine learning-based solutions or utilizing specialized software tools can enhance the accuracy and efficiency of personal information identification, saving time and resources for organizations while ensuring compliance with privacy regulations. However, it is important to note that no automated solution is foolproof, and human oversight and validation are still necessary to ensure the accuracy of the results.
Our QoreAudit product is a powerful tool that provides a complete view of an organization’s information assets, regardless of the origin of the documents. What’s more, thanks to its high accuracy and cutting-edge calculations, it can identify the personal information contained in each document, categorize it and establish a degree of danger for each document based on its content. This then enables organizations to adopt an appropriate action plan for managing these documents.
We create innovative solutions.
COGINOV is recognized as a world leader in semantic technologies and information management. We are a Canadian software company offering our customers innovative solutions for managing structured and unstructured information. Our head office is based in Montreal.
Coginov’s Qore platform technology enhances the information value chain, transforming unstructured content into highly contextualized, accessible and valuable information. Coginov’s solutions enable you to capture, analyze, engage, automate and manage your information assets, with unrivalled accuracy and efficiency.