Data loss prevention (DLP) is one of the most important considerations for any organization looking to build an effective cybersecurity strategy. Data breaches can come in many forms and originate from a variety of sources and causes, so comprehensively protecting against them can be a daunting task. There is no one-size-fits-all DLP solution, but there are a number of methods and measures a company can utilize in order to protect its data. Data lineage is one way to help prevent attacks and accidents that may lead sensitive data to fall into the wrong hands, effectively protecting the organization against potentially catastrophic levels of damage.
Defining Data Lineage
Data lineage, “the process of tracking data as it moves within an organization,” is a fairly simple concept with far-reaching implications. It helps an organization to understand where data comes from, how it gets edited and modified, who accesses and uses it, and what actions they take with it. The additional context provided by data lineage is useful—even necessary—for effective DLP. While traditional DLP solutions work by scanning data for known risks as it leaves the company, data lineage allows security teams to view and make use of an abundance of information that can indicate whether or not data is being used maliciously.
Data lineage works by tracking each action related to each piece of data and combining them into a graph database; these are used in many different contexts to enable “the computation of relationships between different pieces of data.” In the particular case of data lineage, this often involves complex connections, and carrying out these computations via the cloud allows for quick calculations without slowing down processes. With all of this information about the movement and modification of data, an organization and its systems have more of a basis for analysis when determining whether data is being used maliciously or suspiciously.
What Data Lineage Can Do
There are a number of use cases for data lineage that a company can use to their benefit. It can help with incident prevention by providing the necessary context to tell when data is, for example, being exported to places it shouldn’t be. It increases visibility into the data pipeline with a focus on data infrastructure, and it can be a factor in maintaining regulatory compliance. Data lineage can also prevent bottlenecks and other issues that slow down cloud migration, simplify data virtualization by enabling the unified visualization of data, and address gaps in data engineering labor by automating certain processes of self-service data management.
The benefits of data lineage go on: it improves data quality, reduces technical debt, enhances impact analysis, and increases users’ trust in data products. In a world where many employees are remote or hybrid workers, data lineage addresses many of the risks involved by documenting all of the relevant information about the data to prevent data breaches and other types of cybersecurity incidents. Tracking where data originates, where it goes, who uses it, and how they use it means that sending and receiving files, collaborating on documents, editing, modifying, transferring, and downloading data are all safer processes than they otherwise would be.
Data Lineage Best Practices
While data lineage is certainly helpful in a wide variety of ways, it is important to utilize best practices to keep track of data lineage securely and to “ensure that it provides accurate and useful information.” Getting business executives and users involved in data lineage is vital—executive backing is needed for approval and funding, and user understanding and cooperation is required for verification of the accuracy of the data and effective use of the information provided. It is also recommended that organizations keep track of business and technical data lineage, both the high-level information about the data’s origins and business use and the details about how the data is modified and integrated.
In order to make the most use of data lineage, it should be tied to actual business needs; rather than simply an abstract idea, it should be treated as a tool and used to improve business strategies and solve problems. It should also be used holistically, keeping track of all of the company’s data in a single metadata repository. Finally, organizations are suggested to keep a data catalog where data lineage information is embedded. This allows data management teams to provide the tools necessary for business intelligence and analytics users to find, comprehend, and make use of the relevant data.
Conclusion
The benefits of data lineage are plentiful, from bolstering data loss prevention tactics to increasing customer confidence in an organization’s data security. Data lineage is a useful method that solves many issues in data security, integrity, and visibility. A company can make use of data lineage using any of a number of techniques, or a combination of more than one, in order to make it work for the specific needs being addressed. By following best practices and utilizing the right technologies, data lineage can be an extremely helpful tool in understanding and navigating several different areas relating to data.
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