Written by Daniel Tan, Head of Solution Engineering, Asean, Japan, Korea & Greater China, Commvault.
As Asia Pacific organisations face insurmountable ransomware threats, defending Personally Identifiable Information (PII) has become essential.
A recent IDC report revealed a staggering 60 percent of enterprises in Asia Pacific including Japan experienced ransomware attacks in 2022.
Singapore, a major commercial hub in the region, experiences one ransomware incident every three days with small and medium enterprises (SMEs) often targeted, according to the Cyber Security Agency of Singapore (CSA).
With plenty of unreported cases, any compromise on organisational data, especially, PII can lead to colossal legal, financial, and reputational consequences.
As ransomware attacks continue to evolve in sophistication, safeguarding PII data has become paramount for effective defence strategies and here’s why.
Mitigating Legal and Financial Risks: After a ransomware attack, the unveiling of PII data can expose organisations to significant legal and financial risks.
Data breaches may result in hefty fines, regulatory penalties, and class-action lawsuits. To reduce vulnerability to such liabilities, organisations must proactively discover and secure PII data, demonstrating their commitment to data protection and privacy.
Thwarting Data Exfiltration: Ransomware attackers frequently threaten to publish or sell stolen data to put pressure on victims into paying ransoms.
Identifying and securing PII data is required to pinpoint critical assets that attackers might target for exfiltration. Robust access controls, encryption measures, and data loss prevention protocols act as strong deterrents against unauthorised extraction.
Tailored Protection by Prioritizing PII Data: Recognising that not all data is of equal value, organisations should adopt a strategic approach towards protection.
PII data, given its high susceptibility to identity theft and fraud, demands special attention. Through meticulous discovery and categorisation, organisations can allocate resources to fortify their most sensitive and valuable data, thereby enhancing the efficacy of overall cybersecurity measures.
Recognising the need for strong protection of PII data is only one part of the equation. Identifying a solution to accomplish this task is the second piece of the puzzle.
How to Use AI to Fortify Defence Strategies
A comprehensive risk analysis is crucial in aiding organisations to discover, evaluate, oversee, and implement measures to minimise data-related risks.
By gaining visibility into these threats, organisations can easily identify and categorise sensitive data to help mitigate potential data breaches and foster efficient cross-functional collaboration.
The breadth and depth of risk analysis forms the backbone of smart, proactive data management strategies designed to reduce risks and costs.
Hailed for many practical applications, AI is harnessed to ensure sensitive data detection relies. Modern data protection leverages AI-driven techniques to significantly enhance the accuracy and efficiency of identifying sensitive information within large and diverse datasets.
- Comprehensive PII Management: The use of AI provides organisations with a versatile toolset for managing various PII types identified through content analysis. Using custom entities to finetune the model for specific datasets and datatypes allow for sensitivity level adjustments and associated keywords. This system enables configuration for default and customised entity types, forming the foundation for effective PII management.
- AI-Powered Contextual Learning for Data Classification: Leveraging AI-driven contextual learning in risk analysis allow for a precise data classification, bolstering data security, compliance, and governance. 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.
- 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.
- Marrying Cognitive Services and Risk Analysis for Data Classification Insights: Integrating cognitive services and 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 can boost data classification accuracy, efficiency, and compliance even in complex unstructured data. The synergy between cognitive services and 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 also a responsibility.
By embracing AI-driven intelligent technologies, and a commitment to a robust data protection wrapper around sensitive data, organisations can fortify their defences against ransomware attacks and ensure the security and privacy of the sensitive data they store.
