4 features your data-centric security strategy must provide

4 features your data-centric security strategy must provide

Following the advent of the big data movement at the turn of the twenty-first century, technological advancements enabled businesses to manage, store, and process unprecedented volumes of data. Almost all organizations face significant security risks as a result of the exponential growth of data generated and stored. Businesses must develop a data-centric security strategy to equip security teams and other stakeholders with the tools necessary to mitigate these risks.

Along with exponential data growth, TagCyber's report Investigating Data-centric Security Strategies notes that as application and website architectures became more complex, they became more interconnected with other internal and external applications, providing cybercriminals with additional access points to sensitive data. The proliferation of APIs for data sharing has also complicated the process of developing a security strategy to safeguard it.

Due to enterprise security teams' inability to manage data volume and complexity, as well as their lack of understanding of how and where data is accessed, it's difficult to distinguish malicious from legitimate data access. Data security requirements necessitate more mature risk management strategies. Security strategies must evolve at the same rate as innovation. A modern data security strategy must provide sufficient visibility and context at each stage of the data's journey and locate security controls as closely as possible to the data. As environments continue to be defined by data rather than by individual applications, data security solutions' visibility and context will be critical to an enterprise's success.

Securing data vs compliance and monitoring

Historically, data security has been primarily concerned with compliance. Numerous organizations have historically relied on Database Activity Monitoring (DAM) solutions to ensure that security policies are enforced and compliance regulations are followed. Additionally, they provide audit trails and basic analytic capabilities to alert administrators when unauthorized access is detected. Due to the current volume and complexity of data, combined with these solutions' inability to enforce security policies beyond on-premise databases, DAM solutions have been significantly marginalized. As organizations transition from a compliance to a security mindset, DAM becomes increasingly ineffective as a monitoring tool due to the fact that modern security threats – such as zero-day attacks – are simply unknown to DAM solutions.

Along with providing adequate visibility and context at the data level, a modern data security strategy must account for changes in architecture and privacy requirements. To address these imperatives, TagCyber's report outlines four requirements for a modern data-centric security strategy that go beyond compliance monitoring:

1. Capacity for cloud-based security policy enforcement. In serverless cloud environments, DAM tools are inoperable. Your solution must natively integrate with cloud-based data sources in order to provide adequate data visibility and a unified view of the entire data estate across disparate technology stacks.
2. Adopt the "Zero Trust" philosophy. Control data access strictly and gain a thorough understanding of what constitutes normal data interactions for all users. Define policies such as the typical time window during which certain actions occur, who performs these actions, and from where they are performed. Allow users to access the data they require without risking overexposure or forgetting to clean up temporary access permissions.
3. Build-in intelligent data analytics. Capture the full context of all data access paths and perform analytics natively to avoid reliance on a SIEM tool. Your solution should send only the analytics results to a SIEM. Collect all necessary insights in a single unified platform and save money by eliminating the need to process raw signal data or relying on in-house institutional knowledge to generate accurate analysis.
4. Establish context. Enhance data and aggregate key views with contextual information such as vulnerability assessments, identity access management, metadata, and data classification to improve risk understanding and avoid overwhelming SOC teams with false positives and irrelevant data.

In Conclusion

The report demonstrates unequivocally that all businesses must prioritize data-centric security and that it is critical for organizations to develop a strategy for mitigating these risks. These four elements – the ability to apply policies in the cloud, a focus on zero-trust methodologies, intelligent data analytics, and transparent contextual information – will be critical components of a more mature and future-proof risk management strategy.