5 Steps to Effective Cloud Data Governance

Cloud data governance is getting a lot of attention these days, and for good reason. For most organizations, their cloud data resembles the American Wild West. Data sprawls naturally and is often unruled and ungoverned. It is copied, moved, and stored in various repositories and accessed by virtually anyone. There are no guardrails or policies to ensure that it is protected and stays in the right hands. As data continues to proliferate in the cloud and data breaches increase, the industry is recognizing the need for automated cloud data governance.

What is cloud data governance?

Cloud data governance collectively refers to the set of principles, processes, policies, and tools used to manage data in the cloud to ensure that risk is properly mitigated and privacy is managed in accordance with regulatory compliance requirements. Cloud data governance also ensures that data is accurate, available, and usable across the organization.

Challenges of cloud data governance

Cloud data governance is very different from traditional data governance. The cloud itself creates a new set of expectations and ways of working with data that organizations have come to embrace as a means to innovate, improve agility, fail faster, and reduce the time to market. In this way, the cloud delivers faster business value that directly impacts the bottom line. Cloud data governance cannot hinder the organization’s ability to realize any of these business benefits. In fact, cloud data governance must preserve the value organizations get from the cloud data and that requires solving the problem of data governance from a new perspective.

When it comes to cloud data governance, it is important to understand the unofficial processes by which data is accessed and used in the cloud. Previously, when developers spun up a new application, they had to request a server to store data. That meant getting a DBA involved and, since the developer had to ask permission, it was common to involve security in that process as well.

In the cloud, developers don’t need to request a server. It’s right at their fingertips. And so is the data. Developers can spin up or copy entire data stores in minutes without ever involving a DBA, security, or anyone else for that matter. This is one of the many ways the cloud facilitates data democratization and reduces time to market – benefits that businesses aren’t willing to sacrifice.

Another factor unique to cloud data governance is the sheer volume of data that resides in data stores managed by the cloud service provider. Across a multi-cloud environment, developers and data scientists have access to hundreds of data technologies, which continue to proliferate. Developers can also easily add or embed their own data storage technology on top of a compute instance. Organizations lack any formal process for keeping track of which technologies are used and the data elements stored in them. And even if they did have such a process, data governance use cases change and data moves so frequently, it would be impossible to maintain an accurate accounting.

Why do I need cloud data governance?

The lack of data governance in the cloud has several significant implications on cybersecurity and regulatory compliance.

  1. First and foremost, organizations don’t know what data they have or where it resides. This is shadow data, which by definition is cloud data that is ungoverned, has no oversight, and is not kept up to date. Data stores containing shadow data are more likely to be misconfigured, unmonitored, and violate data policies, making them particularly vulnerable to attackers who know to look for these easy targets.
  2. The lack of data governance also means that data can be copied and moved from a well-protected environment to a less secure one. For example, sensitive data may be moved from a secured production environment to a developer environment with a lower security posture. There is also the risk of regulated data, such as card holder data, being moved from a protected environment (considered in-scope for the PCI DSS) to a less secure environment where the data is now at a greater risk of exposure and is in violation of cloud compliance requirements.
  3. When cloud data is ungoverned, the risk of sensitive data exposure increases. The data attack surface itself grows as both known and shadow data proliferates. Data assets are accidentally left exposed to the internet. Users are granted excessive privileges and can access sensitive data that they don’t need. Data stores that include sensitive data are shared with third parties. The list goes on. . . and because the data is ungoverned, it’s also often underprotected.
  4. Finally, cloud data that is ungoverned is also at risk of regulatory compliance violations. By definition, the ability to implement controls to meet regulatory requirements is a form of governance. In other words, you can’t have compliance without some semblance of governance.

What are the benefits of cloud data governance?

When done properly, cloud data governance enables security teams to implement protective controls around data without impacting existing workflows. But it also offers several other benefits.

  • First, cloud data governance helps facilitate data democratization by making more data available to more people so they can do more analytics as quickly as possible. Cloud data governance also enables users to optimize the value they get out of the data while reducing the complexity and risk typically associated with data democratization.
  • Cloud data governance helps improve the security posture of the cloud environment by making it easier to manage risk. Continuous data visibility, monitoring and automation enable organizations to finally understand what data they have, where it’s stored, who is accessing it, and how it’s being protected — without having to understand the underlying cloud data technologies.
  • Similarly, cloud data governance reduces the overhead associated with meeting and demonstrating compliance with regulatory requirements. This saves time and effort associated with implementing controls and accelerates the audit process.
  • Finally, cloud data governance can help reduce cloud costs by eliminating the creation of new shadow data and enabling teams to delete old shadow data.

5 Steps to achieving cloud data governance

Cloud data governance consists of five main processes that should run continuously and simultaneously.

Cycle Graphic representing the Five Steps to Effective Cloud Data Governance

  1. Data discovery

    You can’t govern what you can’t see, so the first step to achieving cloud data governance is to obtain visibility. This requires a centralized application that automatically and continuously discovers all data across the entirety of your multi-cloud environment. This includes data in managed and unmanaged assets, data embedded in virtual instances, shadow data, data caches, data pipelines, and big data.

  2. Classify and catalog data

    Next, define the type of data discovered so that it can be properly classified and cataloged. For example, sensitive data such as PII, PHI, and PCI should be identified and classified accordingly. This information is used to build a comprehensive, consistent data catalog across clouds.

  3. Define and enforce policies

    Once you understand what data you have, define and enforce data security policies and remediate issues for:

    • Compliance and audit management
    • Encryption at rest and in motion
    • Retention, archiving, and purging
    • Who is allowed to access what data
  4. Data ownership and usage

    Strive to associate all data with its owner. Continuously monitor who uses the data and where your data is going, especially with regards to third parties. Empower data consumers with self-service access, while retaining control and governance over data. Understand how the data is processed to ensure it can be used appropriately.

  5. Continuously monitor

    Continuously monitor for policy violations and anomalous behavior so you can proactively mitigate security risks. Address policy violations, block unauthorized access, and delete unused assets in a timely manner.

Repeat. Since cloud environments are highly dynamic, all these steps must be done continuously and constantly.

How does Laminar help with cloud data governance?

In the American Wild West, governance came in the form of a sheriff from out of town who was inevitably met with discontent and open resistance. Thankfully, times have changed – and technology has evolved. Laminar makes cloud data governance seamless. Security teams get the control they need without disrupting innovation, existing workflows, agility, or time to market.

Laminar’s cloud data security platform provides centralized cloud data governance and security across Azure, AWS, Google Cloud, and Snowflake. It enables teams to continuously and autonomously discover, classify and catalog all known and unknown “shadow” data. The platform then assesses the security posture of sensitive data against data-centric security policies, including encryption, activity logging, and retention, and alerts on security policy violations and prioritizes based on data sensitivity and risk. It also determines data ownership and offers clear guidance for remediation.

All of this from a solution that is embedded in your cloud environment and scans your assets using serverless functions that make use of the CSP’s APIs. Laminar is easy to install using cloud-native tools, is agent-less, and has no performance impact. And data never leaves your environment.

Want to see Laminar’s Cloud Data Security Platform in action? Request a demo today.

Author bio:

Mor Gozani (also goes by Mora) is the VP of Product Marketing at Laminar. She’s got the credentials to build a long list and has been working in cloud security for the past 7 years. Most recently with Orca Security, where she built and managed the product marking and technical evangelism team and prior to that she ran marketing for CloudKnox Security that was just acquired by Microsoft!