Moving data from an organization’s systems into data warehouses and data lakes are essential to fuel business intelligence and analytics tools. These insights guide businesses into making decisions backed by data, allowing them to choose actions that have the best chance of positive growth. 

However, getting data from the source systems to these data stores can be a harrowing process. Once the data is in transit, it may be vulnerable to attackers who are looking for opportunities to disrupt the data transfers, steal sensitive information, or take other damaging actions. In 2020, the average cost of a data breach was $3.86 million, according to IBM

Properly protecting this data while meeting an organization’s data usage needs is a delicate balance, but tools such as Extract, Transform, Load platforms can help. 

Table of Contents

  1. The Importance of Moving Data Securely
  2. The Challenges of Moving Data Securely Between Systems and Data Stores
  3. Security Considerations for Data in Motion
  4. Using ETL Solutions to Improve Data Transfer Security

The Importance of Moving Data Securely 

Some organizations place significant value in keeping data secure when it is in use and at rest, but they may not consider the security risks of in-transit data. Some of the threats impacting this data type include: 

  • Man-in-the-middle: A malicious actor inserts themselves in between the systems sending and receiving the moving data in order to intercept it. The goal of this type of attack is to access sensitive information or acquire login credentials. Man-in-the-middle attackers may also use this tactic as part of a larger operation targeting an organization. 
  • Side-channel: Someone with a deep understanding of an organization’s systems and vulnerabilities can use this information for the basis of their attack. They know about exploits, bugs, and other security flaws with the underlying infrastructure and use that to their advantage. 
  • Sniffing: The attacker uses a type of malware called a packet sniffer to target and intercept the data in transit. They may use this approach to steal sensitive data, disrupt the data transfer process, or cause other network instability. 
  • Spoofing: The malicious actor appears to be the legitimate destination for the data transfer. In reality, they have spoofed this information to trick the network or application. This tactic may be used as part of a man-in-the-middle attack. 

Protecting data in transit against these intrusions and emerging attack methods is essential to keeping it secure. Organizations need to be particularly vigilant when working with cloud-based systems since the data moves between networks more frequently than on-premises configurations. 

The benefits of adopting stringent security methods for data transfers include avoiding data loss and breaches, maintaining compliance with regulations, minimizing delays in accessing data, and maximizing the accessibility of the information. 

The Challenges of Moving Data Securely Between Systems and Data Stores

Organizations face several challenges with improving security during data integration processes. A few common roadblocks that occur are: 

  • Staying compliant with data security regulations: Many industries and sensitive data types fall under one or more regulations that dictate cybersecurity requirements. If organizations fail at meeting these requirements, they may face penalties and fines. 
  • Handling sensitive data at scale: A common data transfer use case is integrating data from applications into a data lake or data warehouse for analytics and business intelligence. The massive data volumes involved may include sensitive data, which needs to be handled differently from standard data and introduces complexity into the process. 
  • Maintaining data governance with big data: Another big data-related challenge is implementing and adhering to data governance requirements in a complex, high data volume environment. If the data governance plan didn’t account for working with this much data, it could fall short in keeping the data appropriately managed. 
  • Working with third-party providers: Data integration tools, data lake and data warehouse providers, SaaS applications, and other third parties touch this data and need the proper security in place to avoid exposing it. 
  • Using business processes and platforms with unsecured transfer methods: The current systems may have minimal security measures in place, putting the organization’s data at risk. 

Security Considerations for Data In Motion

An organization must keep all aspects of cybersecurity in mind to protect moving data. By using a holistic approach to data security, they’re able to have the right security measures in place for each step in the data journey from source to destination. 


The number of endpoints organizations work with has grown as remote work becomes commonplace. Vulnerabilities with any of these systems could lead to issues with data as it moves between systems. 


How well protected is the network as a whole? Advanced threat detection offers a more proactive and comprehensive approach to cybersecurity, as these solutions can recognize potential attack activity as well as threats with an established definition. 


Datacenter and workstation security measures help organizations reduce vulnerabilities that stem from social engineering and poor access control. By keeping an eye on physical security as well as digital, attackers have fewer opportunities to get into the systems. 

Data Categorization 

Data security is not one-size-fits-all. Organizations can categorize data based on its sensitivity and other criteria. This approach allows organizations to have granular control over the protective measures for their data. 

Using ETL Solutions to Improve Data Transfer Security


ETL solutions offer several features and benefits for protecting data during the integration process. 

  • Encrypting data as it moves: The ETL tool encrypts the data in the pipeline so attackers can’t get any useful information out of it. It’s decrypted once it’s loaded into the destination system. 
  • Masking and removing sensitive data from the pipeline: Data cleansing occurs after the data gets extracted from the source system. Sensitive data may be masked or deleted entirely, depending on regulatory requirements and the use case for the information. 
  • Creating standardized processes for moving data between systems: Data goes through the same procedure with the same systems every time it moves. This standardization streamlines the process and provides full visibility into data as it goes through the pipeline. 
  • Strictly controlling access to the data: Organizations can limit user access to the data by configuring ETL tools with role-appropriate permissions. 
  • Being able to scale data governance programs: Data governance requirements can be implemented through the data pipeline configurations themselves. 

Use’s Highly Secure ETL Platform to Keep Your Data Safe’s top priority is keeping data secure as it moves through data pipelines. The platform accomplishes this through multiple IT security measures, including: 

  • SSL/TLS encryption
  • Full compliance with many regulations, including HIPAA, FISMA, GPG13, SOX, PCI-DSS, GDPR, CCPA, and SOC 2
  • Leveraging Amazon Web Service (AWS) infrastructure for industry-leading data center security
  • Customizable firewall-based access control provides organizations with fine-tuned control over who can access the data

Discover’s data security measures first-hand with a 14-day demo