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ANALYSIS avoid legal risks. This is particularly important in light of regulations such as GDPR, NIS2, and most recently, DORA, which impose stringent requirements on data management and processing practices. By classifying data, businesses can ensure they meet these requirements and avoid hefty fines.
However, simply complying with each new data regulation as it emerges is not best practice nor is it strategic. This approach can lead to a reactive and fragmented compliance strategy, where businesses are constantly scrambling to meet the latest requirements. This not only increases the risk of non-compliance but also consumes significant resources and can disrupt business operations.
Businesses should focus on creating a standardised process for data governance.
Instead, businesses should focus on creating a standardised process for data governance. A robust data governance framework provides a consistent and comprehensive approach to managing data across the organisation. This framework can serve as a template for regulatory compliance, ensuring that all data management practices are aligned with the highest standards and can be easily adapted to meet new regulations as they arise.
Artificial Intelligence
AI needs no introduction currently as it grips conversations at both a technical and economic level. As businesses increasingly adopt AI, having a robust data management strategy becomes even more critical. Having Redundant, Obsolete and Trivial( ROT) data can often lead to hallucinations or to the sharing of private data in LLMs, potential reputational damage can follow if data is exposed, or inappropriately accessed.
Proper data classification and indexing are essential for effective AI systems. Organised and categorised
Mark Molyneux, EMEA CTO at Cohesity
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