Commvault Risk Analysis is a comprehensive solution aiding organizations in discovering, evaluating, overseeing, and implementing measures to minimize data-related risks. By gaining visibility into these, organizations can easily identify and categorize sensitive data to help mitigate potential data breaches and foster efficient cross-functional collaboration. Commvault Risk Analysis forms the backbone of smart, proactive data management strategies designed to reduce risks and costs.

Sensitive data detection relies heavily on AI methods that can process vast amounts of data quickly and accurately, and Commvault Risk Analysis is modernized with such capabilities. These AI-driven techniques significantly enhance the accuracy and efficiency of identifying sensitive information within large and diverse datasets.

• Comprehensive PII Management: Risk Analysis Entity Manager equips organizations with a versatile toolset for managing various PII types identified through content analysis. This feature includes predefined PII entities and the flexibility to create custom entities to finetune the model for specific datasets and datatypes. These entities encompass specific PII categories, allowing for sensitivity level adjustments and associated keywords. This system enables configuration for default and customized entity types, forming the foundation for effective PII management.
• AI-Powered Contextual Learning for Data Classification: Leveraging AI-driven contextual learning, Commvault Risk Analysis can achieve precise data classification. Unlike traditional methods, AI-driven approaches use machine learning algorithms to understand the context and adapt to evolving data patterns. This reduces false positives and false negatives by considering surrounding text, metadata, and contextual cues. These systems efficiently identify sensitive data and automate classification, bolstering data security, compliance, and governance.
• Empowering Data Classification Through Training Models: Training classification models with custom datasets is essential for accurate identification of document types. This involves uploading representative files that mirror desired categories, allowing the model to build a strong foundation. Continuous refinement is possible by adding additional documents that finetune the classification model, leading to more accurate sensitive data detection.
• Azure Cognitive Services for Data Classification Insights: Integrating Azure Cognitive Services with Commvault Risk Analysis further enhances the recognition of diverse data types within sensitive datasets. AI-driven algorithms can identify intricate patterns defining various data categories and business domains. Deep learning and text analytics capabilities of Azure Cognitive Services boost data classification accuracy, efficiency, and compliance even in complex unstructured data. The synergy between Azure’s Cognitive Services and sensitive data governance capabilities of Risk Analysis offers a cohesive and integrated experience, that not only facilitates more accurate data governance but also a modern AI-enabled deep learning approach that continuously adapts as datasets evolve.

In today’s digital landscape fraught with ransomware dangers, safeguarding PII data is not merely a necessity but a responsibility. By embracing AI-driven intelligent technologies, and a commitment to a robust data protection wrapper around sensitive data, organizations can fortify their defenses against ransomware attacks and ensure the security and privacy of the sensitive data they store.

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BEDADALA, A. (2023). Fortifying Sensitive Data against Ransomware Attacks with AI-Powered Solutions. Available at: https://www.commvault.com/blogs/fortifying-sensitive-data-against-ransomware-attacks [Accessed: 10th November 2023].