Organizations often set up security rules to help reduce cybersecurity vulnerabilities and risks. The 2024 Cost of a Data Breach Report discovered that 40% of all data breaches involved data distributed across multiple environments, meaning that these best-laid plans often fail in the cloud environment.

Not surprisingly, many organizations find keeping a robust security posture in the cloud to be exceptionally challenging, especially with the need to enforce security policies consistently across dynamic and expansive cloud infrastructures. The recently released X-Force Cloud Threat Landscape 2024 Report delved into which specific rules are most commonly failing. By understanding key vulnerabilities, organizations can then figure out the best approach for reducing their risks.

“Regulations are increasing, requiring organizations to implement more compliance policies with security top of mind, which puts a lot of overhead on these organizations,” says Mohit Goyal, Product Management at Red Hat Insights. “The Compliance service within Red Hat Insights provides a more elegant way to manage and deploy these policies on systems to get ahead of any gaps.”

Environment influences failure of security rules

During the research, X-Force analyzed two sets of data across the cloud — one set operating in 100% cloud-only environments and the other with a hybrid of 50% to 99% of their Red Hat Enterprise Linux (RHEL) systems in the cloud. Interestingly, researchers found a different set of most failed rules for each of the two different groups.

Goyal says that the team intentionally looked at both environments because Red Hat caters to customers across the hybrid cloud. During the research, the team discovered that in the 100% cloud group, security rules often failed due to misconfiguring assets, meaning that organizations should focus on configuration guidelines. Meanwhile, in the hybrid environment, most failed rules revolved around authentication and cryptography policies.

When asked who is often responsible for the configurations, Goyal says it varies at different organizations. At smaller companies, a single employee often wears multiple hats. However, at larger organizations, the roles are typically well defined with multiple people involved — for example, a system administrator, a security/risk administrator and a compliance administrator.

Top failed rules in organizations with 100% cloud systems

Researchers found that in situations where all data was stored in the public cloud, the most commonly failed rule was configuration and security guidelines for Linux systems. Researchers described this rule as focusing on configuring essential security and management settings in Linux systems. Examples include setting the default zone for the firewall and isolating the /tmp directory on a separate partition to enhance security and manage disk space effectively. The mitigation is configuring the default zone for the firewall service to make sure the network security is properly configured in Red Hat-based systems.

Other top failed rules include:

• Secure mount options for critical directories
• User home directory management
• Service management
• NFS service management

Top failed rules in organizations with hybrid environments

After analyzing data within a hybrid environment, researchers found that authentication and cryptography policies often failed. These rules focus on standardizing and securing authentication mechanisms and cryptographic requirements in a given policy. Organizations set these rules to ensure consistent and strong security practices across the system. The mitigation involves authselect to standardize and simplify the management of authentication settings.

Other commonly failed rules in hybrid environments include:

• Account and SSH configuration
• SSH security measures
• Umask configuration
• Process debugging restrictions

Why mitigation commonly fails

Because each rule contains mitigation, a common question from the report was why mitigations so often fail. But the answer is not a simple one. The reasons can include a wide range of factors, including misconfiguration, lack of training and different environments.

“Security, in general, is a complex area, and with the threat landscape constantly changing and evolving, it’s hard to maintain the status quo,” Goyal says. “As new technologies and new requirements come into play and the footprint increases, it ultimately leads to a lot of complexity.”

Goyal predicts that the policies are going to increase in number and only become more complex. Organizations need solutions to keep their head wrapped around the complexities in a way that reduces the burden of operational overhead. By highlighting the gaps, leaders can understand where the risk lies and create a plan to close those gaps.

Reducing rule failures

Confirming that all rules are followed and the mitigation is used correctly when a rule fails is time-consuming, explains Goyal. At large enterprises, cybersecurity professionals bear a lot of burden with complex processes. Team members must constantly optimize and check for security while also completing other tasks. Organizations are increasingly turning to Ansible automation, such as with Red Hat Insights, for more effective and efficient remediation.

With Red Hat Insights, an organization can deploy its compliance policies (i.e.: a PCI or HIPAA data governance policy, etc.) on RHEL systems. After analyzing these systems, Insights then displays the level of compliance/non-compliance of the systems to the organization’s policies; it also recommends actions to address the non-compliance. Organizations can select to deploy the Ansible playbook on the systems with just a few clicks to become compliant again. Because the process is automated, it’s more effective and efficient than manually identifying and remediating each system separately.

“Large enterprises need this ability to help keep their costs in control and prevent security gaps from being exploited by bad actors,” says Goyal.

Cloud security: A shared responsibility

Because multiple organizations are involved in a cloud environment, a key question is often about who bears the responsibility for security — the organization or the vendor. Goyal says that security is a dual responsibility.

“As a vendor to our customer, there is a responsibility to make sure they have a product that is built with its security posture front-and-center and has feature-rich functionality that allows organizations to effectively manage their organizational IT security strategy. However, they have to also configure and deploy the product correctly,” says Goyal. “Additionally, organizations need to make sure that their cloud provider emphasizes operational security. At the same time, organizations also need to take ownership for the security of the configurable components of their environment.”

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Practicing good data hygiene is critical for today’s businesses. With everything from operational efficiency to cybersecurity readiness relying on the integrity of stored data, having confidence in your organization’s data cleanliness policy is essential.

But what does this involve, and how can you ensure your data cleanliness policy checks the right boxes? Luckily, there are practical steps you can follow to ensure data accuracy while mitigating the security and compliance risks that come with poor data hygiene.

Understanding the 6 dimensions of data cleanliness

It doesn’t matter where your company data is sourced — without addressing its quality and accuracy, you won’t be able to rely on it. To create the right data cleanliness policy, you’ll need to understand its different dimensions. These include:

• Accuracy: Identifies to what extent data can be trusted and is free from errors. This requires specific validation protocols and compliance with data collection standards.
• Completeness: Signifies whether or not collected data provides clear answers to certain questions. It involves evaluating any missing data attributes and recognizing any apparent gaps.
• Consistency: Checks that data is properly mirrored when stored in multiple databases and represented by a percentage of matched values.
• Validity: Refers to data adherence against predefined rules or formats. It helps eliminate the violation of logical constraints or data type restrictions.
• Uniqueness: Makes sure all data types reference the same units of measure or support formats to remove the possibility of information overlapping or duplication across data sets.
• Timeliness: Represents the degree to which data remains up-to-date. This ensures data is accessible when it’s required so it can be used properly.

Once you have a grasp on these six core elements, you’re ready to move forward with crafting your data cleanliness policy.

Step 1: Define policy scope and objectives

The first step to take when creating a data cleanliness policy is to define all appropriate business objectives. Any specific data sets or systems and the intended use of the information within them should be clearly outlined.

This step also involves considering often-overlooked data, including unused software logs, outdated emails and former customer records. If this information is forgotten about, it can lead to security issues down the road when they are left in unsecured locations.

Step 2: Classify data assets

With your policy scope defined, you’ll need to take inventory of all relevant data sources. Data assets can include various databases spread across multi-cloud environments, locally stored spreadsheets or any other areas where data is stored.

Classifying all data assets is another way to minimize forgotten data from compiling and creating high-value targets for cyber criminals. During this process, you’ll also want to categorize data based on its relative sensitivity or regulatory requirements. This will make it easier to implement the right access controls and data retention policies.

Step 3: Establish data quality standards

The data quality standards you develop for your policy should be measurable and easy to understand. To achieve this, you’ll need to lay out specific criteria for each data type, including the acceptable formats data should be in and any validation rules you have in place.

With your metrics in place, you’ll be able to regularly monitor their performance over time. Many times, regulatory requirements will stipulate that data needs to meet certain accuracy and completeness benchmarks. Having these trackable metrics in place provides the transparency needed to ensure these regulations are continuously being met.

Step 4: Assign roles and responsibilities

Establishing clear accountabilities is essential when managing organizational data. Your data cleanliness policy should define the various roles in your organization, including specifying who can access data and what levels of permission they have.

Controlling the amount of individuals who can access, modify or delete data is one of the most important elements of ensuring data integrity over the long term. It helps you to mitigate the danger of insider threats as well as establish clear lines of accountability if and when anomalies are located in data sets.

It is also common to make use of a data governance team that can help to implement and enforce various policy initiatives. These teams can reduce the likelihood of data inconsistency and help support various data security protocols in place.

Step 5: Implement data cleansing procedures

In the event that data issues are discovered, your policy should also cover necessary data correction procedures. This can include standardization, normalization or deduplication of data stored across systems.

Another supporting element of this process is having clear data retention and disposal policies in place. This helps to reinforce best practices when it comes to data lifecycle management. It also minimizes a digital attack surface, making it less likely that sensitive information is left in a vulnerable storage state, and helps to minimize damages in the event of a successful cyberattack.

Maintain healthier organizational data

Being able to rely on the accuracy and consistency of your company data is critical. Not only does data integrity play an important factor in improving the value of your technology investments, but it also helps to strengthen your cybersecurity posture.

By following the steps above, you’ll be able to draft a data cleanliness policy that allows you to maintain healthier organizational data while extracting its full value.

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