Data Integration’s Hidden Cybersecurity Power

Data integration – essentially, the practice of bringing data from multiple sources into a single consolidated view – has become vital to the broader discipline of data management. Among other reasons, all of the work associated with data management, from collection to storage and everything in between, is typically harder and more labor-intensive when dealing with multiple data silos.

There’s another, less visible reason: Strong data integration and data management can enhance your organization’s cybersecurity posture.

“Many often think of securing applications, but it's even more critical to secure data,” says Krishna Subramanian, co-founder and COO of Komprise. “Since data is the lifeblood of any business, data leaks are the greatest security risks.”

Data breaches are especially problematic when you’re not even aware of them – a scenario that is far more likely when data is stored in disparate silos.

“Data management is all about analyzing data and mobilizing data to feed the right data to the right place at the right time,” Subramanian says. “To apply the best security to your data across the business, it is firstly imperative to understand the data across all silos – from on-premises to edge to cloud. That is where data management comes into play.”

In this article, we’ll consider data integration and data management as your “secret” security power and provide tips for using them to bolster your cybersecurity posture.

5 Security Pillars Where Data Integration & Management Help

Before we get to tips and best practices, let’s connect the dots between cohesive data integration/data management and security. Scott Wheeler, Cloud Practice Lead at Asperitas, notes that data integration and data management play significant roles in five key areas of security:

1. Strengthening Protection: “Data management can help prevent breaches by implementing encryption, masking, anonymization, authentication, and zero trust,” Wheeler says. 

Encryption and masking, for example, help protect data in transit and at rest, while anonymization can limit the fallout when a breach does occur. Effective data management is also key to enabling the principle of least privilege and zero trust strategies, limiting data access to people and systems that actually need it to do their jobs. 

2. Bolstering Breach Response: There’s a security adage that essentially declares: “Assume breach.” The most secure organizations are the ones that treat breaches as inevitable or already underway and act accordingly to strengthen and speed up their response and mitigation strategies – and shore up their prevention and overall security posture in the process. That’s much harder (if not impossible) to do when you don’t have a complete picture of your data – you might not even know where to start looking.

“Data management can significantly accelerate responding to a data breach by facilitating the identification of where the breach occurred and its impact on the organization,” Wheeler says. “It can also reduce the data recovery time after a security incident by having well-defined processes and procedures in place for data recovery.”

3. Reducing Data Silos and Duplication: Data integration can have one of the biggest impacts on cybersecurity by reducing data silos and duplicate data, both of which pose potential security risks. For example, silos – which Wheeler defines as “isolated pools of data that are not shared or connected with other data sources” – can lead to sensitive or outdated data being stored somewhere that is not protected by the same security policies and processes that are applied elsewhere in the organization.

“Data integration can help reduce data silos and duplication by consolidating and standardizing the data across different systems and platforms, ensuring that the data is accurate, complete, and secure,” Wheeler says.

4. Enhancing Data Governance and Compliance: Data governance and compliance go hand in hand with security. A lack of good governance—which Wheeler describes as “the set of rules and processes that define how data is collected, stored, accessed, used, and protected within an organization”—almost certainly will increase an organization’s exposure to security risks and make it virtually impossible to meet regulatory requirements. Data integration is a bedrock for both good governance and robust compliance.

“Data integration can help enhance data governance and compliance by enabling a centralized and consistent view of the data, facilitating data quality checks, audits, and reports, and enforcing data access and usage policies and permissions,” Wheeler says.

5. Improving Data Visibility and Monitoring: Here’s another security truth: You can’t protect what you can’t see. That’s again where things like fragmented data silos cause challenges and increase risks. If you have blind spots in the data visibility, it’s more difficult to ensure consistent, comprehensive security protection, detection, and response.

“Data integration can help improve data visibility and monitoring by creating a unified and comprehensive data landscape, allowing for real-time data tracking and alerting, and applying data security measures and controls at every stage of the data lifecycle,” Wheeler says.

8 Tips For Bolstering Cybersecurity

With that context in mind, Wheeler and Subramanian shared a slew of expert tips and practices for leveraging data integration and data management to bolster your cybersecurity posture.

1. Start with the right data integration strategy: Wheeler advises doing your homework on selecting a data integration strategy and tools that suit your business and its goals. Three key examples of strategy include batch integration, real-time integration, or hybrid integration. Then, evaluate tools and processes accordingly.

2. Data inventory and classification are essential. Both Wheeler and Subramanian emphasize the importance of data inventory and classification as fundamental to strong data security. Wheeler points out that they are an absolute necessity for enforcing the practice of least privilege, meaning users (and code—or machine-based systems) can only access data necessary to perform their jobs, and nothing more.

Subramanian says classification is also vital when working with unstructured data: “Tagging features within unstructured data management solutions, along with AI file content scanners, can further identify data which may require industrial-strength security,” she explains. “This could be PII data, IP data, R&D data, HR data, and so on. The process of classifying data with additional metadata is critical to help apply the correct security to different data sets.”

There’s an added benefit, too: Effective tagging and classification can also help IT teams more readily identify deleted or otherwise unneeded data that is still being saved in a high-cost cloud storage platform, reducing data management costs over time.

3. Monitor data access: Data integration can also help facilitate tracking data access – as in, who accessed what data and when. 

“This is invaluable in researching data breaches or ensuring the organization complies with any regulations or internal policies,” Wheeler says. He also advises establishing data stewardship roles and responsibilities to ensure data quality and integrity, as well as data accountability and ownership.

4. Make full use of alerting: Many IT and data management applications can deliver automated alerts about a wide range of metrics. Subramanian recommends using these alerts to proactively identify threats and anomalies in your environments, such as excessive file retrievals from one user account or excessive writes to a storage location which could be the work of a hacker. 

5. Use data integration patterns and frameworks: There are multiple options here, such as extract-transform-load (ETL), change data capture (CDC), or data virtualization. Wheeler notes that any of them can potentially simplify and standardize the data integration process.

6. Leverage security-centric data management practices: At their core, data management and data integration are security-minded and include a variety of tactics for strengthening your posture. Use them.

“Apply data encryption, masking, anonymization, and access control techniques and follow the relevant data protection regulations and standards,” Wheeler says.

He also advises implementing data quality checks and validations at every stage of the data integration process and then monitoring and measuring data quality accordingly.

7. Automate policies for data movement: Subramanian suggests looking for solutions that can bring greater automation to data governance and management. This can help reduce the risks inherent in things like outdated data.

“For instance, you may want to automate that customer files move to immutable cloud object storage for 12 months once an account is closed or inactive, and then automate the deletion of the files,” Subramanian says.

8. Watch out for generative AI and LLMs: It’s 2024, so we are required by universal law to mention AI.

Seriously, though, generative AI applications (such as ChatGPT or Bard) will impact data management and security, too. As such, your policies and processes need to cover them.

“[IT leaders] will need to develop employee guidelines concerning which data, applications, and use cases are sanctioned for generative AI tools,” Subramanian says. “IT must have tools which can prevent the leakage of sensitive data (such as software code, proprietary information, customer information, HR data) into AI tools that are accessible by the general public.”

The Bottom Line

Data Integration and data management done right are vital catalysts for data security – and for an organization’s overall security posture. Ignore them at your own peril.

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