Ways to Ensure a Secure Data Supply Chain | Eureka Security

Today’s cloud-based environments are complex and consist of various components and moving parts that form your organizational ecosystems. These ecosystems involve various data assets and flows. However, without proper visibility and understanding of these data components, the decision-making process of business owners can be affected, leading to increased risk and potential exposure. This is where the concept of Data Supply Chain comes into play.

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What is a Data Supply Chain?

Data Supply Chain is the process of collecting, processing, and distributing data across platforms, systems, and organizations within a company or between different organizations. It includes operations like data flows, transformation, processing, analysis, as well as the inputs (data suppliers) and outputs (data consumers) of data. Data can be obtained from external sources, shared with teams or organizations, or made publicly available.

Data supply chain and data lineage are related concepts, but they have different meanings.

While the former refers to managing data and its risks throughout an organization, the latter focuses specifically on the history and movement of data. 

For the purposes of this article, we will concentrate on Data Supply Chain and related challenges

Establishing Trust

Trust is broken when sensitive data is ingested or shared without prior acknowledgement. Establishing trust in the Data Supply Chain requires transparency and accountability at every stage, starting from data ingestion to data consumption. It involves correctly classifying data, implementing appropriate access controls to prevent unauthorized access or data breaches, and complying with legal and regulatory requirements. Continuous monitoring and evaluation of data handling processes are also crucial. 

Dynamic Boundaries and Sensitivity Levels 

The lack of automated data discovery and classification is a significant challenge. Ingress data may contain more data than what was initially agreed upon, and data ownership can be a fluid concept, making it difficult to assign accountability. This can result in the ingestion of unknown or unexpected data, which can be sensitive or pose legal and compliance risks. Data ownership being a fluid concept can additionally make it difficult to assign accountability and ensure that data is appropriately handled, stored, and accessed. This creates a lack of transparency and trust in the data supply chain, leading to legal and financial consequences.

Inadequate Control Plane

The absence of a proper control plane is a challenge for tracking data lineage and ensuring proper handling. Inconsistent data protection methods among organizations can lead to compliance breaches. The lack of a control plane makes it difficult to track data lineage and ensure proper handling throughout its lifecycle, resulting in inconsistencies in data protection, access controls, and compliance requirements. These inconsistencies can lead to breaches and data exposure.

Remediation of Risky Data Flow is Complex

Remediating risky data flow is complex and requires understanding the data architecture and business processes. Fixing configuration issues may seem easy, but modifying data flow and business processes can be challenging due to their interconnected nature and downstream effects. Moreover, identifying the data owner over time can be difficult. Therefore, remediating risky data flow demands a careful approach considering the impact on the organization and its data ecosystem.

Missing Context

Data security depends on understanding the data’s context and its broader environment. Without context, it’s challenging to assess risks and identify vulnerabilities in data processes. To ensure robust security, a comprehensive understanding of the data and its broader context in the form of data supply chain is vital. Additionally, classifying data collections and approving legacy data use-cases can be resource-intensive, especially when evaluating unstructured data objects. Enforcing privacy requirements for shared or distributed data is crucial to prevent legal and financial repercussions from data breaches or unauthorized access.

Risks in Data Supply Chain

There are several risks associated with the data supply chain, including unknown sensitive data, stale data, legal and compliance issues, and data exposure. Unidentified sensitive data can jeopardize security and compliance if not protected. Stale data creates vulnerabilities for hackers to exploit. Legal and compliance problems arise from improper data collection and storage practices. Different permissions on the same data set can expose it to breaches. Lengthy remediation times increase legal exposure and security risks. Establishing effective data governance practices is challenging due to the complex and large-scale nature of the data supply chain, which affects data access, usage, ownership, and compliance management.

How to Address These Risks and Challenges

To address the risks and challenges in the Data Supply Chain, several suggestions can be followed:

• Establish clear data policies and procedures covering data usage, access, privacy, and compliance
• Implement automated data discovery and classification tools to ensure sensitive data is identified and protected
• Assign owners and establish accountability for data management
• Align data security controls between organizations or create a data-handover procedure to secure data during handover processes
• Provide training and education to enhance the knowledge and approach to data controls
• Develop an incident response plan to effectively respond to data breaches or incidents

Conclusion

The data supply chain is crucial for data collection, processing, and distribution. However, addressing the associated challenges and risks is essential to ensure data security and compliance. By implementing the suggestions mentioned above, organizations can mitigate risks and create a more secure data supply chain.

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