Modern businesses generate, store and analyze vast amounts of data. Considering new technologies like the Internet of Things (IoT), the cloud, 5G, and artificial intelligence (AI), it shouldn’t be a surprise that this information is being used for various purposes.
Business applications, software, hardware, and other information technology (IT)-related assets are becoming increasingly important in today’s fast-paced digital environment. Data integration is a standard process used to manage the growing amount of information.
What Is Data Integration?
In simple terms, data integration is combining information that comes from several different sources to create more unified datasets. These datasets can then be used for analytical, operational, or other related purposes.
Integration is a core element of data management. Without it, information gathered by companies would have little use and would not meet employees’ needs.
Most companies gather data from various sources, both internal and external. Business applications and employees need to access this information to accomplish tasks or complete transactions. It would be challenging for them to combine data from different sources manually.
Data integration pulls all this information together for users to make it easier to complete relevant tasks. It’s often placed in a data warehouse, a central repository that users can easily access.
For example, a loan officer may need to verify financial information before approving a home mortgage loan, like records, property values, and credit history. Each task would be time-consuming and tedious without properly integrated data.
Data integration allows companies to access a complete picture of key performance indicators (KPIs), supply chain management, regulatory compliance measures, cybersecurity, financial risks, and other essential information regarding business operations and processes.
Cybersecurity Risks Associated With Data Integration
Any professional working with data knows cybersecurity is a top concern. It’s efficient for applications and programs to share data, but the downside is that it typically increases an organization’s security vulnerabilities.
Here are three examples of cybersecurity risks associated with data integration.
Risk #1: Data Silos
Access controls are an essential part of data governance, but they can present issues for security teams. Many organizations have a large collection of data silos that work independently. In other words, it can create uniform tracking, protection, and preservation challenges.
Each data silo has its own set of operational and access control methods that make it difficult for IT teams to secure information at every stage of its life cycle. An increased number of data silos gives threat actors more opportunities to exploit vulnerabilities and execute attacks.
Risk #2: Burden on Developers
Security teams and developers are forced to take on the extra burden of securing information spread across multiple silos at every layer. Additionally, the burden is only exacerbated with new data privacy laws, and the stakes for protection are much higher.
Development and security teams are often disconnected, making data security even more challenging to manage.
Risk #3: Insider Threats
Suppose enterprises focus all cybersecurity efforts on external threats and endpoints. In that case, they fail to acknowledge the potentially damaging insider risks within the organization.
General network security is critical, but organizations must also consider unknown and unmanaged insider threats. Some of the worst data breaches occur within the company, meaning data security must exist internally and externally.
Tips to Improve Data Integration Security
These risks can damage an organization, so companies must take active measures to manage their data integration practices. Security must be engrained into data integration for the best protection.
Here are some tips to improve data integration security in an organization.
Conduct Risk Assessments and Threat Modeling
Risk assessments and threat modeling are two essential components in cybersecurity and can help improve data integration security. Threat modeling analyzes the security of an application so problems can be mitigated and future attacks can be prevented.
Risk assessments are similar to threat modeling because they analyze and assess privacy dangers when working with information. Consider implementing these strategies to secure data integration.
Build Audit Trails
A clear audit trail can help an organization manage any integrity issues. A data audit will profile information across several repositories and assess its quality and integrity.
Audit trails assist with data integration security, but they can also help organizations adhere to regulatory requirements.
Leverage Data Integration Solutions
Many vendors offer data integration solutions to meet an organization’s unique needs considering the high demand for these tools.
Popular data integration tools include Hevo, Jitterbit, Talend, Informatica PowerCenter, and Oracle Data Integrator. It’s critical to find solutions with security and compliance features to offer the best protection for integration.
Prioritize Security in Data Integration
Data integration is beneficial for businesses, but it does require enhanced security at each layer. It should not impede sharing or access to information, but it must protect sensitive data and keep it out of the hands of threat actors. Organizations that leverage integration should prioritize security as cyberattacks become more frequent and sophisticated.