8 mins to read
The global cloud computing market will grow by $461 billion by the last quarter of 2025, according to predictions. With the cloud adoption rate increasing and well over half of the organizations using multiple public clouds, it’s clear that enterprise organizations will depend on public cloud security. In this blog, you’ll read about the data governance best practices for security teams in the public cloud.
Operations teams must adopt new cloud data governance and security strategies that enable effective data stewardship, support innovation, and automate compliance while moving at the speed of the cloud. How do they keep up with security and innovation? Here are the 13 data governance best practices we recommend cloud teams follow.
Centralize control of cloud and multi-cloud data
Organizations leveraging a multi-cloud architecture may have data stored in multiple public clouds. Regardless of where they are in your organization, effective cloud data governance starts with centralizing data governance and establishing control over all data sets.
Centralizing data control provides massive efficiency benefits by empowering data engineers to apply data governance policies more consistently throughout their infrastructure. Implementing a single platform for data access control across your clouds allows engineers greater control to create and manage policies for all data sets from a single interface.
Managing data security from a centralized platform eliminates issues with security policies that can lead to non-compliance. This enables teams to move data amongst public cloud platforms without impacting data governance policies and controls.
Save time with scalable global policies
As organizations expand their ability to capture and store data, manual methods of governing data become increasingly time-consuming and inefficient. Additionally, manual processes are more prone to human error introducing higher levels of data risk.
Organizations that have adopted a centralized data security platform can save time and effort by implementing global policies that regulate the availability and usage of data throughout the organization – not just within a single database or application. This significantly simplifies maintaining consistent data governance policies across multi-cloud platforms.
Automate the discovery of sensitive data
Some data governance and security platforms can automatically discover, classify and tag sensitive data across multiple platforms. Sensitive data discovery allows operations to spend less time performing manual data classification and reduces the risk of errors associated with manual data entry. Sensitive data is automatically tagged to enable the appropriate access control policies once detected.
Streamline sensitive data workflow
Even with sensitive data discovery, and automation, data governance teams must be able to certify that it has been detected, classified, and tagged appropriately. To meet these requirements, operations should establish workflows for inspecting, reviewing, and approving the results of automated discovery and data tagging. Streamlining sensitive data workflow is essential for any organization that handles confidential or personal information. To meet these requirements, cloud operations should establish workflows for inspecting, reviewing, and approving the results of automated discovery and data tagging. These workflows will help to ensure that all sensitive data is properly identified and protected while also reducing the workload for data governance teams. Streamlining sensitive data workflow can help to improve the accuracy of automated discovery and data tagging processes.
Manage role explosion
Data teams that depend on role-based access controls face increasing complexity as the number of roles in the organization grows, sometimes to the hundreds or thousands. This phenomenon, known as role explosion, makes it exponentially more complicated to accurately and uniformly apply data governance rules across the organization.
Attribute-based access controls (ABAC) is dynamically applied data governance policies to each query based on user attributes like physical location, clearance level, and purpose. This eliminates the need for data engineers to create new roles for each new data need and allows organizations to scale data access as their size and data sources grow.
To manage these complex requirements, organizations can leverage cloud-based data governance and attribute-based access control to create location-based policies that regulate access based on the user’s location and data type.
Enable fine-grained data access controls
Data architects and engineers with fine-grained data access controls can create policies restricting access to specific rows, columns, or cells within a table for unauthorized data consumers. Fine-grain data access controls allow organizations to remain compliant with data regulations and protect sensitive data that is in a table with other frequently used data or that must be accessed for a specific purpose.
In the past, data teams would have to make a copy of the file and remove or anonymize the sensitive data before allowing access – a time-consuming and tedious process. Now, cutting-edge, dynamic data masking capabilities, like k-anonymization, randomized response, and differential privacy, automatically hide sensitive data from unauthorized users without copying or moving data.
Meet compliance requirements with purpose-based access controls
Regulations dictate that data collection must be for specific and legitimate purposes and that its use cannot be for anything other than those stated purposes. Under a purpose-based access control system, each data object is assigned a set of intended purposes, and access may only be granted if the data consumer specifies an access purpose that matches the intended purpose of the data. A predetermined and approved data access purpose helps enable regulatory compliance.
Continuously monitor data for auditing purposes
Cloud data governance policy audits must be continuous to assess the effectiveness of the existing policies, identify any security risks or deviation of your security baseline, and enable ongoing compliance with regulatory requirements.
It is important to remember that these policies must be a continuous audit to be effective. By regularly assessing the effectiveness of existing policies, organizations can identify any security risks or deviations from their security baseline. This also enables them to stay compliant with any applicable regulatory requirements. In short, continuous monitoring of cloud data governance policies is essential for ensuring the safety and security of an organization’s data.
Enforce transparency with automated reporting
The combination of centralized data access and automated reporting ensures full transparency amongst data consumers, data architects, data engineers, and compliance teams when it comes to understanding the who, what, when, why, and how of data access. With auditing and reporting capabilities, data teams can quickly auto-generate reports that reveal who is accessing data, why they are accessing it and how they are using the data.
Enforcing transparency with automated reporting is a great way to keep your cloud team organized and efficient. When everyone has access to the same data and can see the utilization of the data, it’s much easier to keep track of progress and identify potential issues. This transparency ensures that everyone is on the same page and that the data is used effectively. Automated reporting is a valuable tool for any cloud team, and enforcing transparency is a great way to ensure that it meets its fullest potential.
Conduct an assessment
Conduct an assessment of your data collection practices. Whether you operate locally, nationally, or globally, understand which privacy laws and regulations apply to your business. Follow reasonable security measures to keep sensitive information safe from inappropriate and unauthorized access. Therefore, make sure the data you collect is processed in a fair manner and only collected for relevant and legitimate purposes.
Prioritize third-party security
Don’t forget to maintain oversight of partners and vendors as well. Consequently, if someone provides services on your behalf, you are responsible for how they collect and use your consumers’ personal information. Prioritizing third-party security should be atop every company’s priorities. Too many organizations take a hands-off approach when it comes to overseeing their partners and vendors. Unfortunately, this can come back to bite them later on. If someone provides services on your behalf, you are responsible for how they collect and use your customers’ personal information. As a result, it’s so important to have a rigorous checklist in place to ensure that your partners are taking cybersecurity and data privacy as seriously as your business is. By taking these precautions, you can help protect your consumers’ information – and your company’s reputation.
Adopt a data security and privacy framework
Adopting a data security and privacy framework is crucial for any organization that wants to keep its information safe. With so many different frameworks available, it can be difficult to know where to start. The NIST Privacy Framework is a great place for organizations to get a sense of what types of risks their data privacy and security face. The AICPA Privacy Management Framework is another excellent resource that can help organizations develop a plan for keeping their data safe. Finally, the ISO/IEC 27701 – International Standard for Privacy Information Management is an invaluable tool for organizations that want to ensure that their data is properly protected. Adopting one or more of these frameworks is essential for any organization that wants to keep its data safe and secure.
Educate employees
Data privacy success hinges on a business’s ability to create a culture that prioritizes data security and privacy within the organization. And educating your employees about their role and your organization’s obligations to protect personal information is central to establishing this type of environment.
The public cloud will be a part of all our futures – our society is largely running on it. Many enterprises will use a multi-cloud approach to running their business. The cloud must implement security checks and controls as a result.
Data Governance Conclusion
The 13 best data governance best practices that we’ve listed are a great starting point. They are by no means the only areas you need to worry about when it comes to protecting your data. While we can’t guarantee that these data governance best practices will keep you safe from every possible risk, they will go a long way in helping to protect your company’s crown-jewel data.
Put data and identity at the center of your strategy
The Sonrai Dig delivers a complete risk model of all identities (people and non-people) and data relationships, activity, and movement across cloud accounts, cloud providers, and third-party data stores, helping teams with data governance best practices. Sonrai implements controls around what has access to data is fundamental to any data security and compliance program. Each unique cloud provider delivers services and APIs to manage identity and access to data for their stack. However, they are not standard across all the stacks available (e.g., Amazon, Google, and Microsoft), do not address third-party data stores, and often require using low-level tools and APIs. Sonrai Dig resolves this problem through normalized views and control of cloud identity and data access.
Sonrai Security offers multiple solutions touching each facet of a secure cloud perimeter. This includes complete end-to-end automation for security workflows, remediation, and prevention. Using Sonrai Dig and leveraging CIEM, CSPM, Cloud DLP, and automation, an enterprise just starting their cloud migration or one already there can harness the efficiency of the cloud safely.
To learn more about Sonrai Dig and how we can help your organization with data governance best practices, request a demo today.
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