according to predefined classification standards. Both existing data and new data acquisition require attention for several reasons: • Compliance and security: Both data sets must comply with legal, regulatory and security requirements. Misclassification or neglect could lead to breaches, legal penalties and loss of public trust. • Efficiency and accessibility: Proper classification ensures that data, whether old or new, is easily accessible to authorized personnel and systems, thereby improving operational efficiency and decision-making. • Scalability: As new data is acquired, systems that handle existing data must be scalable to accommodate growth without compromising classification standards or processes. While developing and managing a sound data classification policy is essential, it can be laborintensive to go back to decades of data and records management, often operating under different conditions and policies. Here, automation and technology can play a pivotal role. This is where one can leverage AI and machine learning tools to automate the data classification process. These technologies can handle large volumes of data efficiently and can adapt to changing data landscapes. The good news is that several tools and technologies are available that can automate much of the data classification process, making it more efficient and effective. These tools typically use rule-based systems, machine learning, and natural language processing (NLP) to identify, classify and manage data across various dimensions (e.g., sensitivity, relevance, compliance requirements). Some prominent examples include: • Data loss prevention (DLP) software: DLP tools are designed to prevent unauthorized access and transfer of sensitive information. They can automatically classify data based on predefined criteria and policies and apply appropriate security controls. • Information governance and compliance tools: These solutions help organizations manage their information in compliance with legal and regulatory requirements. They can automatically classify data based on compliance needs, and help manage retention, disposal and access policies. • Machine learning and AI-based tools: Some advanced tools use machine learning algorithms to classify data. They can learn from past classification decisions, improving their accuracy and efficiency. These tools effectively handle large volumes of unstructured data, such as text documents, emails and images. • Cloud data management interfaces: Many cloud storage and data management platforms offer built-in classification features that can be customized according to the organization’s needs. These tools can automatically tag and classify new data as it is uploaded, based on predefined rules and policies. Implementing these tools requires a clear understanding of the organization’s data classification needs, including the data types handled, regulatory requirements, and the information’s sensitivity level. It’s also crucial to regularly review and update the classification rules and machine learning models to adapt to new data types, changing regulations and evolving security threats. Data classification is not a one-time activity. Regular reviews and updates are necessary to ensure the classification reflects the current data environment and regulatory landscape. To sum up, data classification is a foundational element for successfully integrating AI in the public sector. It ensures the protection of sensitive information and enhances the efficiency and effectiveness of public services. By prioritizing accuracy, privacy, accessibility and scalability, data managers can lay the groundwork for responsible and effective AI applications that serve the public good 10 CIVIL AND MUNICIPAL VOLUME 5, ISSUE 03
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