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Data-centric Security
Authors
Gary Zimmerman – Principal Consulting Analyst
Abstract
Data-centric security is crucial for protecting sensitive information, ensuring compliance with data privacy regulations, and enabling secure collaboration. Organizations must adopt this approach to safeguard their most valuable asset—data—to maintain customer trust and adapt to the evolving data landscape.
Data-centric security emphasizes the protection of data itself rather than traditional system, network, and perimeter defenses. This approach embeds security controls directly at the data source, applies granular access controls, and continuously monitors data usage. It addresses the challenges posed by cloud computing, remote work, AI-generated data, and IoT by ensuring consistent data protection across diverse environments.
Key benefits include enhanced data privacy and transparency, improved incident resilience, and regulatory compliance. Treating data as a valuable asset, as defined by “Infonomics,” involves managing, measuring, and monetizing information to unlock its full potential.
However, implementing data-centric security presents challenges such as accurate data classification, encryption key management, and integration with legacy systems. Future trends highlight a shift towards data safety, increased adoption of inside-out security models, and the integration of AI/ML for enhanced security capabilities.
This report lays out the steps, tools, workflows, and policies necessary to implement a data-centric security strategy while highlighting the business benefits associated with developing a robust and diverse data portfolio.
