Key Principles for Building a Robust Data Integration Strategy

Key Principles for Building a Robust Data Integration Strategy

Organizations must maintain all current safeguards as they integrate while ensuring that data privacy and security measures are implemented in every system and at every point of connection.

As the amount of data organizations keeps growing, so does the need for data integration. Data security issues can arise in data integration projects involving a variety of data sources. Although most security teams implement data security measures, they frequently fail to provide adequate security for integration processes.

To ensure security across all integrations, organizations must reevaluate their approaches to data integration. This strategy might call for revising current procedures and implementing a more comprehensive data integration strategy. In all but the smallest of organizations, data and system integration have become a core responsibility for IT managers.

Any business cannot function without integrating into various external systems, such as hosted IT services, cloud computing platforms for distributed computing, online marketplaces for retail, web advertising services, credit card transaction systems, and many more. The proliferation of mobile apps and remote working options affects every industry.

Modern security techniques for cloud integration

It takes much planning to implement integration successfully. The number of technical problems can seem interminable. It takes overcoming data type and format incompatibilities, bridging OS and application differences, managing data sharing, and many other issues to get on premise systems and cloud computing platforms connected and playing nicely together.

As if managing all those elements wasn’t challenging enough, data privacy and security is another requirement for the entire project.

Organizations must maintain all current safeguards as they integrate while ensuring that data privacy and security measures are in place in every system and at every point of connection.

When businesses decide to integrate, they open a classic Pandora’s Box of detailed security requirements and problems enough to turn any security expert’s head.

The preparation of a secure integration

To identify and address the security challenges the integration project will unavoidably face, CISOs must first engage with their organization’s security professionals. There is no denying that adding numerous connections between systems and applications creates new potential security flaws.

It is dangerous to move forward without professional advice because of the increasingly strict regulatory requirements and the constant innovation in the techniques and vectors bad actors use to attack. This becomes even more crucial when older legacy environments are included in the integration objectives.

Before cross-platform or cloud integration existed, some legacy platform operating systems, data storage, and system security techniques were designed. Such systems still have an integration-resistant core DNA throughout their OS and security structures, even in their most recent, updated state. Therefore, specialized knowledge and abilities are needed.

For secure data integration, there are three rules

Organizations must stop sensitive data from being sent unnecessarily to systems further down the line. The security teams need to ensure that it is kept safe in case it needs to be shared. Moreover, it is also necessary to minimize all damage in case of a potential security incident.

Here are three guidelines that can help organizations with smooth and secure data integration:

Separate concerns

Organizations can minimize the threat of data breaches by separating the functions of data storage, processing, and visualization. Giving the data scientist direct access to the primary database is impractical. Even if they mean well, they might export private customer information from that database to a dashboard only authorized users can see.

Running analytics queries on a production database can also cause it to become so slow that it becomes unusable. The answer to this issue is to specify the specific types of data that must be analyzed and, using various data replication techniques, copy the data into a secondary warehouse made just for analytics workloads.

As a result, organizations give data scientists a secure sandbox environment entirely separate from the production database while preventing sensitive data from flowing downstream to them.

Utilize data masking and exclusion techniques

Because they completely stop the flow of sensitive information to downstream systems, these two processes also aid in separating concerns. Most data security and compliance issues can be resolved when data is removed from apps.

Data exclusion is straightforward: Businesses can choose not to select the subsets of sensitive data if they have a system that enables them to do so, such as an ETL tool. Of course, there are times when it’s necessary to extract and share sensitive data. Data hashing and masking are helpful in this situation.

With an ETL tool (Extract, Transform, and Load), data exclusion and masking/hashing can be accomplished. In addition, since ETL tools permit data to be hashed or masked before being loaded into the target system, they are typically regarded as more secure than ELT tools. Consult this in-depth analysis of ETL and ELT tools for more details.

Also Read: Strategies to Develop a Cyber-Resilient Security Posture

Keep a strict auditing and logging system in place

The last thing to do is to ensure that systems are in place that allow security teams to comprehend who is accessing data, how it is accessed, and where it is sent. Naturally, this is crucial for compliance as many regulations demand businesses to show they are monitoring access to sensitive data. The ability to quickly recognize and respond to any suspicious behavior is also crucial.

Internal auditing and logging are the businesses’ responsibility and the vendors of data tools like analytics platforms, data warehouses, and pipeline solutions. Therefore, when assessing them for inclusion in the data stack, it’s crucial to consider these tools’ role-based access controls, logging capabilities, and other security features like multi-factor authentication (MFA).

Because SOC 2 Type 2 certification is the industry standard for how digital businesses should manage customer data, it is also a good thing to look for. Security teams can conduct a forensic investigation and limit the damage this way should a potential security incident occur. In the future, businesses will be forced to share data more frequently while maintaining security.

Fortunately, attending to the requirements of one need not entail ignoring the other. Sharing is not always caring when it comes to data. Yes, better decision-making, improved customer experiences, and improved business outcomes are all aided by increased data flow across departments.

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