Data Governance: Securing the Data Lifecycle in Cloud Environments

As enterprises increasingly adopt cloud technologies, security has become a top priority, and security architecture teams now play a critical role in architecture review committees. Different cloud models—public and hybrid—require stricter rules around applications and the data they handle. Business units want 360-degree visibility of their data at any point in time. What do security teams care about most? Data. How can we alleviate their concerns? By implementing data governance.

Data governance is a disciplined approach that enterprises use to protect data throughout its entire lifecycle (collection, storage, processing, and deletion).

Data Governance Definition:

Data governance encompasses everything you do to ensure data is secure, private, accurate, and usable. It includes the actions people must take, the processes they must follow, and the technologies that support them throughout the data lifecycle.

Enterprises must consider all four stages of data governance across the data lifecycle. These stages and activities are unique to each organization. For example, for a traditional enterprise, storing data might mean storing physical paper outputs, focusing on how paper is stored, who can access printers, shredding procedures, and desktop policies. In cloud‑enabled enterprises, storing data refers to data in the cloud, emphasizing role‑based access control (RBAC), who can act on the data, static data, data in transit, and other cloud‑specific considerations.

According to the SABSA‑Sherwood Business Security Architecture, any enterprise’s data governance should broadly address the following categories:

Data Governance categories according to SABSA

For any given application or implementation, the data governance team should be able to answer a concise set of questions:

How is the data collected?

How is it transferred to storage after collection?

Where is it stored and how secure is it?

How is the data retrieved and processed?

Where does the processed data go?

How long must the data be retained?

What should happen to the data after the retention period ends?

What is the backup strategy in case of data loss or corruption?

How quickly can lost data be recovered?

I aim to keep this article generic rather than diving into a specific scenario. The point I want to emphasize is that data is a critical element of enterprise application implementations and must be handled carefully. There are many tools, methods, and frameworks available to support its implementation.

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