{"id":6820,"date":"2024-12-19T16:16:06","date_gmt":"2024-12-19T21:16:06","guid":{"rendered":"https:\/\/solutionsreview.com\/data-management\/?p=6820"},"modified":"2024-12-19T16:37:07","modified_gmt":"2024-12-19T21:37:07","slug":"shift-in-focus-why-data-classification-and-categorization-are-key-in-ensuring-data-security-and-privacy","status":"publish","type":"post","link":"https:\/\/solutionsreview.com\/data-management\/shift-in-focus-why-data-classification-and-categorization-are-key-in-ensuring-data-security-and-privacy\/","title":{"rendered":"Shift in Focus: Why Data Classification and Categorization are Key in Ensuring Data Security and Privacy"},"content":{"rendered":"

\"\"<\/p>\n

ByteSafe’s Raghavan Chellappan offers insights on why data classification and categorization are key in ensuring data security and privacy. <\/strong><\/em>This article originally appeared on Solutions Review’s Insight Jam<\/a>, an enterprise IT community enabling the human conversation on AI.<\/strong><\/em><\/p>\n

Information is essential in understanding the health of a business, given how data serves as the foundational fabric underpinning all business decision-making. However, there are several challenges to overcome.<\/p>\n

While data offers powerful advantages, the complexity of managing disparate data within and across organizations is a daunting challenge due to the varying degrees of data maturity and controls that exist.<\/p>\n

Furthermore, as enterprises embrace transformational generative AI solutions to mobilize their capabilities and capitalize on their investments, there are challenges inherent in harnessing and leveraging high-quality datasets (real and synthetic) that must be addressed on the path to increased productivity enabled by these AI solutions.<\/p>\n

Additionally, the adoption of advanced technologies (like IoT, AI, M\/L, LLM, Robotics, 5G\/6G) along with the increase in connected systems and cross platform data sharing applications have exponentially increased the risk of cyberattacks and data breaches.<\/p>\n

Lastly, the large quantities of data in play coupled with the complexity of unstructured and semi-structured data generated by humans and automated systems, have created data chaos and concomitant data quality management challenges leading to an unprecedented loss of data security and personal privacy.<\/p>\n

Future-Oriented Systemic Approach to Secure and Protect Data<\/strong><\/h3>\n

To secure and manage the data explosion, enterprises require a balanced approach to data classification and categorization while emphasizing data security and privacy. Carefully managing these massive volumes of data in a way that mitigates cybersecurity risks requires going beyond traditional frameworks and developing data management solutions designed to ensure data integrity and consistency.<\/p>\n

Decentralization<\/strong><\/h4>\n

The currently used systems based on centralized architecture, disparate infrastructure environments, and loosely secured data repositories have critical limitations that allow data breaches. Avoiding or minimizing these limitations and breaches requires systemic changes and a redefinition of how organizations protect their data assets. Such an approach would offer a proactive, protection-focused, decentralized framework for data classification and processing that improves current business processes and underlying architecture through the use of data encoding mechanisms, data minimization, anonymization, and tokenization techniques adhering to security and privacy standards.<\/p>\n

Classification Practices<\/strong><\/h4>\n

Data classification, at an elementary level, refers to the categorization and tagging of metadata based on risk levels and adequately securing the data to protect against bad actors. Current data classification procedures primarily involve the organization of structured, semi-structured, and unstructured data into logical categories (Public, Internal, Confidential, Restricted) and the application of labels to ensure that data can be effectively and accurately searched and tracked.<\/p>\n

To improve data quality, secure and protect data, and prevent data loss, more robust data classification practices (Discover, Analyze, Classify, Validate, Cleanse, Categorize, Label, Automate, and Continuous Monitoring) utilizing mathematical models and numerical methods must be applied to ensure sensitive information is properly secured and protected. To accomplish this, organizations must know exactly:<\/p>\n

    \n
  • WHAT<\/em><\/strong>\u00a0data are in their possession,<\/li>\n
  • WHERE<\/em><\/strong>\u00a0the data are located,<\/li>\n
  • WHEN<\/em>\u00a0<\/strong>the data was created, modified, or deleted and accessed,<\/li>\n
  • WHO<\/em>\u00a0<\/strong>has access to the data, and<\/li>\n
  • HOW<\/em><\/strong>\u00a0sensitive are the data.<\/li>\n<\/ul>\n

    Accordingly, organizations can then apply appropriate security measures and implement the right access controls based on the sensitivity and confidentiality of the data.<\/p>\n

    Governance Structure<\/strong><\/h4>\n

    For data classification to be effective and efficient, it is also essential to have a well-defined governance framework for data management and security policies that enables organizations to identify and protect their most valuable and sensitive data assets. The basics of such a framework include:<\/p>\n

      \n
    • Precisely capturing of the data lineage to track the origins and movement of data to enforce data integrity,<\/li>\n
    • Diligently managing compliance by implementing regulatory and sensitivity tags to data to mitigate privacy risks,<\/li>\n
    • Proactively identifying sensitive data early to minimize exposure to potential vulnerabilities and threats.<\/li>\n<\/ul>\n

      Hybrid Approach<\/strong><\/h4>\n

      Countering data quality management challenges would also benefit greatly from a hybrid approach, which is a combination of automation and human-in-the-loop processes.\u00a0<\/strong>Instead of eliminating human involvement entirely with full automation, a hybrid approach with the right level of human involvement would go a long way towards achieving the best outcomes as we transition towards agent-driven algorithmic models. For example, \u201clow code\u201d and\u201d no code\u201d automated options allow for quick development and deployment of AI applications, but human coders continue to be useful for fixing unanticipated errors and optimizing these automated options.<\/p>\n

      Main Takeaways & Conclusion<\/strong><\/p>\n

        \n
      • Data classification and categorization can lead to improved data security and privacy.<\/li>\n
      • Decentralized frameworks for data classification and processing can benefit enterprises by improving data quality and protection.<\/li>\n
      • Well defined governance structures and security and privacy policies can help organizations proactively identify and protect their most valuable and sensitive data assets.<\/li>\n
      • Hybrid approaches combining automation and human-in-the-loop processes can improve data quality.<\/li>\n<\/ul>\n

        An update, change or shift in focus is required to optimize current data classification and categorization methods and better address security, privacy and compliance needs right at the beginning. Establishing decentralized frameworks, adopting robust data classification practices using mathematical concepts, applying well-defined governance frameworks and leveraging hybrid approaches offer a pathway to secure and protect data assets. Top-down review and bottom-up data-driven approaches further ensure efficient and secure operations, while minimizing data complexity and risk of data loss.<\/p>\n

        Though AI adoption is still in its early stages, a structured approach to data management would enable organizations to unlock the full potential of the data assets they possess, in turn offering a more accessible and cost-effective option that allows harnessing high-quality data to drive business growth.<\/p>\n","protected":false},"excerpt":{"rendered":"

        ByteSafe’s Raghavan Chellappan offers insights on why data classification and categorization are key in ensuring data security and privacy. This article originally appeared on Solutions Review’s Insight Jam, an enterprise IT community enabling the human conversation on AI. Information is essential in understanding the health of a business, given how data serves as the foundational […]<\/p>\n","protected":false},"author":1204,"featured_media":6826,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[3],"tags":[1475,1472],"acf":[],"yoast_head":"\nWhy Data Classification & Categorization are Key in Ensuring Data Security and Privacy<\/title>\n<meta name=\"description\" content=\"ByteSafe's Raghavan Chellappan offers insights on why data classification and categorization are key in ensuring data security and privacy.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/solutionsreview.com\/data-management\/shift-in-focus-why-data-classification-and-categorization-are-key-in-ensuring-data-security-and-privacy\/\" \/>\n<meta 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