Why Data Monitoring is Key to Sensitive Data Protection

According to one 2022 report, 89% of companies claim that their organization is taking a multi-cloud approach to data storage and analysis. This means the data they collect lives in at least two different cloud databases, if not more. The data must be accessible to the variety of tools meant to query, protect, transform, and otherwise use it for business-driving analytics, as well as to the data consumers who require access for their own projects.

Combine these factors, and you have a modern data stack with a lot of moving parts, each of which needs to interact with your data in a unique way. While the tools and processes involved can help discern greater insights and drive company objectives, it is paramount that data is never put at risk.

How can this risk be mitigated, and if possible, completely deterred across these disparate parts of the data stack? Ultimately, organizations require oversight across each and every platform, tool, engine, and user in their ecosystem. To enforce reliable sensitive data protection and keep their data resources secure, organizations require rigorous data monitoring capabilities. But what is data monitoring, and how is it put into practice?

What is Data Monitoring?

Data monitoring refers to the processes and tools that are used to facilitate oversight of how an organization is collecting, storing, and operationalizing its data. Monitoring is meant to provide teams with a consistent, holistic view of the data they possess and how, why, and where it is being used. This real-time oversight is essential for any data security or privacy framework.

Consistent monitoring enhances the security of sensitive data by providing teams with critical information about user activity. With this constant supervision, teams can keep a record of who is doing what with their data and for which purposes. Without monitoring activity, teams will likely be blind to leaks or violations when they first occur. Lacking monitoring functions will also make it more difficult to maintain audit logs for quality and compliance assurance.

Data monitoring is also, to a degree, a quality control process. By maintaining oversight across data sources, platforms, and user instances, monitoring can assess whether data is fresh, complete, and accurate for any use case. This helps data teams guarantee that data consumers are receiving the highest quality information possible at query time.

How Does Data Monitoring Work?

Often, the first step in using a data monitoring tool is to have data teams determine which conditions are expected to be met in their data ecosystem. By consulting a range of stakeholders–cybersecurity officers, engineers, platform architects, developers, compliance managers, etc–in this early stage, expectations can be agreed upon for the security and quality of data in the ecosystem. From here, standards can be set to give the software benchmarks against which to monitor the data being used. These preset rules act as the source of truth for the monitoring tool to refer to when examining how the data is actually being leveraged.

These standard rules can be set for a variety of factors like data security, quality, accuracy, freshness, privacy, and other important characteristics. Once the rules are in place and the framework is complete, the monitoring tool will routinely scan the data environment to verify if these conditions are being met. If a rule is broken, teams will know instantly and can address the issue appropriately. If not, the tool will simply continue these routine scans and compile a running log of activity for audit purposes.

Why is Data Monitoring Key to Sensitive Data Protection?

To illustrate the importance of data monitoring for sensitive data protection, consider the following example:

You’re the head of security at a branch of a large bank, tasked with ensuring that the bank’s money is not stolen. The building the bank is located in has multiple floors, vaults, teller counters, offices, reception areas, waiting rooms, bathrooms…the list goes on. Hundreds of customers visit the bank every day to make deposits, withdrawals, and discuss their loans and mortgages with bank staff. All the while, you must guarantee the security of the premises, employees, customers, and–most critically–the money.

How might you monitor all of this activity to ensure that your resources are effectively secured and protected? Even if you were to have security guards operate on a tight patrol schedule, you could never be everywhere in the bank at once. You wouldn’t have the holistic oversight necessary to understand who is doing what, where, and for which reasons, leaving the bank vulnerable.

How do banks solve this security problem? They install a system of security cameras to monitor the entire premises on an ongoing basis. With constant surveillance, bank security can maintain an omniscient view of the building, patrons, and resources, and can therefore quickly recognize when something goes wrong.

Now apply this same logic to the modern data stack: data teams and governance stakeholders are the security guard(s), your storage platforms are the vaults, connected tools are the bank’s various functions, and your data is the money. Without “security cameras” monitoring your sensitive data, you won’t have a clear idea of how and why it is being used. Data monitoring tools provide the same protection as physical security systems, surveilling data use across all tools, platforms, and users. This provides teams with the observability they need to protect sensitive data across their data stack.

Ensuring Sensitive Data Protection with Data Monitoring

Since data monitoring plays a critical role in sensitive data protection and overall data security, how should you go about implementing it in your data stacks?

Data-driven organizations should employ a data monitoring tool that:

  • Automates Surveillance Activities: This allows data consumers to operate freely and maintain oversight without requiring manual checks. When a monitoring framework automatically observes every user action taken within the data ecosystem, teams maintain a complete view of what’s going on with their data.
  • Tracks Data Policies: Data policy creation, maintenance, and changes should all be tracked and recorded. This will develop a full history of data access policies, helping data security, governance, compliance, and user stakeholders to all easily understand how policies have evolved without requiring specialized engineering resources.
  • Sends Automatic Alerts: Teams should be instantly notified of any anomalies or issues with the system’s preset rules. This gives them the immediate ability to respond to these problems without needing to wait for downstream effects to reveal the issues once it’s already too late.
  • Compiles Compliance Reports and Audit Logs: These kinds of resources are crucial for adhering to data compliance laws and regulations, enabling teams to assess policy history and check for any non-compliant activity. Both data compliance and security are enhanced through these measures.

Scalable and secure tools that combine these functions into one data monitoring “security system” will successfully strengthen your sensitive data protection. Immuta’s Data Security Platform gives teams the tools they need to discover, secure, and–crucially–monitor their sensitive data from the moment it enters their data stack. With these capabilities, teams can strengthen their sensitive data protection and security without compromising data’s integrity, usefulness, or accessibility.

To learn more about Immuta’s data monitoring capabilities–and how they are poised to evolve–request a demo from one of our data experts.

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