August 10, 2023 | Reading time 6 minutes

Data Loss Prevention Best Practices for the Cloud: Complete Guide

Losing sensitive corporate information can have deteriorating outcomes for a business. Learn how to avoid them in our latest guide on Data Loss Prevention Best Practices for the cloud.

Understanding cloud data loss and its common causes

In 2023, about 60% of all corporate data is stored in the cloud. And judging by the trends, this percentage will continue growing. Similar to data stored on-prem, cloud data is subject to loss. 

While cloud data centers guarantee data protection against physical damages and the majority of malware attacks, they place a fair share of responsibility for data security on the data owners. And they manage to keep their side of the bargain, as the negligible percentage of cloud data loss incidents can be attributed to malfunctions or hackers’ attacks on the data centers.

The main causes of data loss in the cloud are:

  1. Human error (e.g., accidental deletions)
  2. Absence of backup
  3. Ransomware attacks
  4. Zero-day attacks
  5. Account hijacks
  6. Unauthorized access
  7. Man-in-the-middle attack
  8. Cloud misconfiguration

All these causes, including ransomware attacks, lie beyond the scope of responsibilities of cloud providers. Businesses using cloud services have to address these issues on their own.

Cloud Data Loss Prevention

Cloud data loss prevention is a system of practices and tools that prevent data loss in the cloud. Some businesses include incident response to DLP practices, and in our opinion, it makes sense. Let’s say your company lost a folder with the project specifications and managed to retrieve it from the backup. We could safely claim that the backup tool helped the company prevent permanent data loss.

Cloud data loss prevention includes:

  • Cloud security configurations (e.g., disabling outside sharing)
  • DLP policies (e.g., creating a copy of a deleted file in a secure vault)
  • Third-party tools (e.g., backup, SSPM)

There’s another important aspect to consider in DLP. In some cloud environments, the term DLP encompasses both data loss and data leak prevention. While the security practices and tools for both leak and loss prevention overlap, they are not the same. Data leak requires some extra practices (e.g., detection of PII transfer, mass down-load detection).

For the purposes of this article, we’ll only cover data loss prevention.

Benefits and significance of implementing DLP

Data loss prevention help businesses avoid many business risks:

  • Downtime
  • Legal fines due to the non-compliance
  • Lawsuits from the clients and/or employees
  • Reputational losses
  • Revenue losses
  • A surge in client churn
  • Termination of important project
  • Incident response expenditures
  • Loss of employees
  • Business termination

Cloud data loss prevention best practices 

In this section of the article, we list the practices that will help you protect your data from loss.

Data Loss Prevention Best Practices for the Cloud
Cloud DLP Best Practices

Data discovery and management

This practice is probably the most overlooked. However, in our opinion, it is critical for any efficient data loss prevention system.

Data discovery and management will enable your IT team to achieve data visibility within your cloud. Without this visibility, efficient control over data integrity is impossible.

This practice requires that you discover and catalog all the data in your cloud. Next, you need to categorize it in accordance with its sensitivity and value for the organization. It will help you create DLP policies and data retention rules. It will also enable you to set the rules for the deletion of obsolete data.

Data discovery and categorization should be regular for your team.

Data behavior monitoring

Your IT team needs to monitor data events such as mass editing or mass deletion. It will help you stop the incidents that include the loss of large data sets. Such events can happen due to human factors (errors or malicious actions), software malfunction (an error in an application), ransomware attack, or a zero-day attack.

Such monitoring is impossible to carry out without automation tools. That is unless you use dedicated human resources, which is highly unlikely to take into account the cost of such monitoring and the current cybersecurity talent shortage.

Data behavior patterns will also help you to detect covert threats within the organization if you feed the information about data behavior to SIEM.

Access Control

Access control has two aspects. First, you need to secure the user access to your cloud environment by ensuring that:

  • Users have secure passwords that they change on a regular basis.
  • Multifactor authentication is enforced.
  • Regular log-out on all devices is enforced.
  • You monitor user behavior to detect abnormalities that might imply the account hijack.

Second, you need to limit access to sensitive data within a cloud environment. For example, there is no need to provide the finance department with access to all sales data and vice versa. The best principle that comes to mind is divide and conquer. Divide your data into clusters and enable access to it only for a limited number of your employees. That’s where the data categorization we mentioned before comes in handy.

Application detection and control

Application control can be achieved only through the full visibility and risk assessment of every app that has OAuth access to your cloud environment. Otherwise, you might have to deal with a zero-day attack when hackers access your sensitive data by exploiting an app’s previously unknown vulnerability.

Control over applications will also enable you to detect similar apps that are used by different departments (or even within one department). You can reduce tech stack expenditures by removing apps with duplicating features from them.

Employee Training and Awareness

Employee training can help your team correct their behavior to avoid data loss incidents. This includes:

  • detecting potential threats like phishing emails with ransomware
  • sharing files in accordance with the company security policies 
  • avoiding using unauthorized applications
  • creating complex passwords both for corporate and personal accounts
  • never using permanent deletion for files
  • securing their devices better

Obviously, training cannot eliminate all these incidents. However, it will make the chances of data loss lower.

DLP policies

DLP policies can become, in many cases, when employees fail to follow data protection rules. These are algorithms that are triggered whenever an incident with data loss potential occurs. Many cloud environments have DLP policies. In addition to that, there are third-party tools that enable companies to create additional policies if the existing ones aren’t enough.

An example of such a policy will be when a person incorrectly shares a sensitive data file. The policy might only notify the user (and, in some cases, the Admin and the user’s manager), disable such action, or even take ownership of the document from the user.

Incident Response and Recovery

Incident response and recovery are critical to minimize the impact of a data loss event and the event that caused it on the company. It includes the following activities: 

  • retrieving data from backup, 
  • revoking sharing permission, app access, etc.
  • user suspension
  • taking over the ownership
  • incident investigation.

It’s important not only to restore data but also to find the cause of its loss and take necessary actions to prevent repeating such an incident in the future. For example, more than one-third of businesses that survived a ransomware attack become the victim of a new attack. This happens because the business failed to detect all the entry points that hackers left when they infiltrated your environment for the first time.

Using third-party tools

It is impossible for IT teams to carry out all the necessary data loss prevention practices. That’s why your business needs cloud-native data protection.

Meet SpinOne platform. It was tailored specifically to address the multiple threats to data integrity in the cloud. At its core is the incremental regular backup that copies your data on a separate unlimited cloud storage.

To protect against mass data loss, SpinOne has powerful behavior-based ransomware protection. It detects and stops ransomware within minutes after the attack and also recovers all the data automatically.

SpinOne offers businesses App Risk Assessment. This functionality detects all the OAuth apps that have access to your cloud environment and gives your Admins the power to block them.

Additionally, the platform monitors and detects an abnormality in data and user behavior, helping IT security teams to detect data incidents timely, for example, improper sharing. Finally, SpinOne has DLP policies to intervene and stop risky incidents or notify your team about them.

Overall SpinOne helps your IT security team automate most of the critical data loss prevention practices and decrease the response time and the number of incidents.


What are the key components of an effective DLP policy framework?

The main elements of an effective DLP policy framework are discovery, classification, and monitoring.

How can access controls and user permissions help in preventing data loss?

Access control can prevent unauthorized users from accessing data and carrying out an attack (for example, deleting it or overwriting it with false information).

How do employee training and awareness contribute to DLP best practices?

Trained employees are less likely to take actions that can cause data loss (e.g., fall for a social engineering attack or have their accounts hijacked).

How often should DLP policies be reviewed and updated?

DLP policies should be reviewed and updated at least once every year. However, it’s best to make it at least twice a year.

Can DLP solutions be integrated with existing security infrastructure?

It depends on the solution. 

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