Understanding Data Lineage and Data Provenance

Understanding Data Lineage and Data Provenance

Understanding Data Lineage and Data Provenance


Data lineage and data provenance are related terms, but different. Lineage focuses on the origins and movements of data over time, while provenance focuses on the transformations and derivations of data from original sources. Provenance helps teams to follow the source of data and verify its authenticity, surfacing any potential risks or vulnerabilities. In other words, lineage is more about “where” data travels, and provenance is more about the “what” of data history. Both are important from the security standpoint. 
In this article, we’re going to dive into the differences between data lineage vs data provenance and how they both help a company’s security and privacy efforts to track, protect, and understand the data within their systems. 

Data Provenance vs Data Lineage: The Differences 

What is Data Provenance? 

Data Provenance, in regards to security, focuses on the historical record of data, including the transformations and derivations from original sources. Think of it as an auditing log keeping track of records like: 

  • Who created this piece of data and when? 

  • How has this piece of data been altered over time?  

  • Who has access to this data? 

  • What are the components of this data? 


Provenance is a key layer in security, particularly in systems where 3rd parties are involved in handling sensitive data. 

What is Data Lineage? 

Data Lineage is the map, it captures the lifecycle of data, including where it originated, and follows it as it moves over time. Data lineage provides a snapshot of how product data gets from its source to an end-user: 

  • Where is this specific piece of data going (endpoints like 3rd parties and LLMs)? 

  • How has it changed as it moves through data pipelines?  

  • Where is it available to end users? 

  • What is the original source of this data? 

  • Which teams or stakeholders rely on this data? 

  • What data is being sent (code and product level)? 

When created and maintained effectively, data lineage is not only a superpower for security teams when handling an incident, but it is also a secret weapon for data engineering teams.  

Why Data Provenance and Lineage are Important‍ 

For security teams, data lineage is critical to expediting the incident resolution process. Data Lineage enables teams with the mapping needed to understand the upstream and downstream dependencies of data, simplifying how they understand the potential impact and identify stakeholders. Data lineage introduces a reliable incident response process if a data break should arise. From the lens of a business's bottom line, data lineage surfaces which datasets are outdated and unused, lowering maintenance and storage costs. 

The focus of data provenance in security is supporting the discovery of anomalies and potential threats. Security teams can use provenance to discover trends that indicate a breach or threats coming from within the company. In the case of a data breach, reliable provenance information can not only help trace back the attack to its source, but it can help minimize the blast radius of a breach. 

A well-defined data lineage and provenance system provides priceless insights and enables efficient decision-making. Data lineage and data provenance are both integral to maintaining a comprehensive data flow security strategy. 

The Privacy and Security Angles‍ 

Data lineage and provenance are critical for all companies that handle sensitive data. In Europe, GDPR requires organizations to know exactly where all personal data of EU residents reside. By 2026 13 states in the US will have enacted privacy laws requiring similar GDPR efforts be taken. 

If implemented properly, they can accurately detect data tampering and provide proof of compliance with new and existing guidelines, and reduce legal risk. In privacy, provenance and lineage are both required to prove confidentiality, integrity, and availability. 


This images highlights the outcome when provenance and lineage are both implemented to prove data confidentiality, integrity, and availability. 

Using both data lineage and data provenance creates and amplifies the trust between end users and companies. 

Integrating Data Provenance and Lineage into Existing Workflows‍ 

More data leads to better insights, but it also can introduce liabilities and noise to sift through. The goal for data-driven teams should always be quality over quantity. Leveraging tools to implement and automate data lineage and provenance efforts can be a great start to integrating data provenance and lineage into existing workflows. Tools help by: 

  • Accurately depict all data that is being exchanged with vendors or internal users 

  • Ensuring regulatory compliance 

  • Removing shadow IT blindspots 

  • Creating and maintaining comprehensive data logs 

There are plenty of available solutions both in the realm of open-source tools (implementation can be challenging) and managed solutions. The jackpot would be adopting a solution that combines lineage, provenance, and monitoring to comprehensively support the detection, remediation, and prevention of data issues. Tools like Informatica or Octopai could be strong options for companies wanting to start their focus on lineage. But If you’re looking for a tool that provides full visibility into data in transit going to third parties or internal tools, a tool like Riscosity will be an optimal solution. 


Data provenance and data lineage have an important place in any security and privacy strategy. By implementing them into privacy and security efforts, companies can ensure that they are maintaining applicable compliance practices, data integrity standards, and data quality efforts effectively. 

Are you ready to implement a data lineage and data provenance layer to your company’s strategy? We’d love to show you how Riscosity protects and maintains accurate logs of all data in transit – making sure you remain compliant and in control of your data. 

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