What is Shadow Data?
In this digital era, big data is gradually driving business decisions. Big Data, which allows accurate manipulations of massive volumes of data, has revolutionized the very outlook of businesses and the way they store data over the past decade. The amount of data generated and processed each day has insane figures. No matter how big or small a business is, it generates data. This data could be about customers, employees, or even sales. Data you possess is valuable and it helps to make informed decisions.
Big data might be a knight in shining armor for your business, but at the same time, it is the most vulnerable resource. Cyberattacks on data assets are serious threats to organizations. What if I tell you that you possess data that you don’t know about? YES in the form of shadow data.
Not a lot of organizations are familiar with the concept of shadow data, but it’s a weak link that can cause security threats to your confidential data.
What is shadow data?
Shadow data refers to any data that is unknown to organizations’ data security teams due to unavailable or the fact it is not covered by a company’s data security policy.
Shadow data raises a lot of compliance and security issues for organizations. The data teams, when designing the data architecture, should keep shadow data in consideration. This unmanaged data also increases the cost of overall data infrastructure.
Let’s look at a few examples of shadow data in an IT infrastructure:
- Data used for testing purposes during development, if not removed, can become shadow data.
- Unmanaged data from a legacy database and application log files.
- A database that is part of an external tool that is unmanaged can contain shadow data.
- Data that is shared with an unknown SaaS provider.
Your biggest weakness can be shadow data. This information is no longer used in many situations, corporate data teams are either unaware of it, or it’s not visible. Shadow data often goes unnoticed, and it makes it more vulnerable.
Causes of shadow data
There are several factors that are a part of the organizational process but unintentionally become a cause of shadow data. The most common causes of shadow data include:
- Development cycles
There is increasing pressure on DevOps teams and developers to work swiftly and effectively. However, they frequently forsake security in favor of speed, which raises the risk of shadow data. Developers might launch cloud instances, for instance, and then quickly deactivate them. When this occurs, data may continue to exist in a cloud environment without the knowledge of IT or security staff.
- Third-party tools
In the world of tech, remote work is becoming more common, and in order to be productive in remote and hybrid contexts, business users nowadays need specialized tools and services. They frequently use a range of tools for texting, collaborating, screen sharing, data sharing, and other tasks to boost their performance. Employees often share sensitive data using third-party tools, and the data present on third-party services can become shadow data.
- Employee machines
Employees frequently have administrative access to local workstations and programs as well. If a hacker is able to obtain access to a device with local administrator rights, they can use that access to exfiltrate data, steal passwords, and install malware. They could even be able to increase their access levels so that they can access the wider IT environment.
- Bad practices
Usually, employees are not aware of the seriousness of shadow data. They don’t know that minor negligence can cause serious security issues.
Cloud services and risk of shadow data
Cloud services can be the biggest contributor to the accumulation of shadow data in an organization. Almost all organizations have S3 or other storage objects as a backup data store which acts as a shield against security breaches to the production system. It serves as your backup strategy and contains precise replicas of the production data. However, they are frequently overlooked and less closely watched, which increases the risk of unintentionally exposing a lot of data to the public. Moving data to the cloud requires data migration from legacy databases to cloud-based databases. During this process, original data is often neglected and left unmanaged. The databases that are not in use are often overlooked when designing security strategies and cause serious threats. Sensitive data is logged by developers and logging frameworks, which produces sensitive files that are not labeled as such, lack the necessary encryption and access control and are therefore easily accessible.
The majority of businesses have a partial clone of their production database in a testing or development environment where programmers create applications and run tests. When developers are working quickly, they frequently take a snapshot of the data without adequately deleting or securing the duplicated information.
The issue with these data stores is that they all include sensitive information, including financial data, software, and information about customers, employees, and others. And they’re probably hidden from your data protection staff. They are now uncontrolled, unsecured, and unseen.
How to detect shadow data?
To detect shadow data design procedures to continuously monitor data assets. These data assets include your active data pipelines, legacy databases, cloud-based storage objects, and idle data that is not in use anymore. Define a standard procedure and document all the data assets. Flow security provides solutions to detect shadow data. It maintains the data infrastructure and automates the whole process. You can catalog the complete application environment, including all assets, data stores, data flows, shadow DBs, third parties, and external services. For security and regulatory purposes, it lists all the services and databases that hold sensitive data, including PII, PHI, and financial data. This helps to store and back up data. Using flow security, users can automatically detect data-centric security risks and get relevant solutions. By maintaining the proper data infrastructure, the chances of having shadow data can be minimized.
Strategies to minimize shadow data
We have developed the understanding that shadow data is inevitable, and there’s no algorithm that can make sure that there’s no shadow data. Does it mean shadow data can’t be controlled? NO shadow data can be identified and treated properly in order to eliminate security risks associated with it. Let’s discuss a few strategies that can be used to minimize shadow data:
- The software development lifecycle (SDLC) should include data security as a fundamental component, with the right security and compliance rules being implemented from the very beginning of planning and design.
- All the questions related to data architecture must be documented by development and data teams. These documents must be integrated to agree on the same guidelines and shared across teams. This will help to keep all teams informed. Continuously scan your workloads to build and maintain data documents that classify data assets according to their sensitivity and criticality. These data documents need to be extensive and open to all stakeholders, and searchable using a variety of criteria, including owner, sensitivity, utilized by, version, and other categories.
- State-of-the-art data documentation should also offer visuals that point out links, flows, and dependencies among data repositories, given the magnitude and complexity of data in a typical mid-sized to a big firm. You can find shadow data that nobody is actually using if you can build outflows and determine who is engaging with what data. In addition to using expensive storage space, this idle data presents cybersecurity vulnerabilities, including data theft.
- Schedule frequent health checks for data assets. When a developer duplicates a data store for testing or a database is mirrored by an operations staff member before an update, they should remove the copies as soon as the testing environment is shut down or the upgrade is complete. But in reality, there are lots of duplicates and insufficient or low-value data stores all around your environment. Put procedures in place to periodically delete this shadow data. A complete health check helps to identify such data assets and helps to identify shadow data.
- Shadow data must be taken into account in your organization’s risk assessment policies and procedures. The type of shadow data, where it is located, and any pertinent compliance requirements, based on how sensitive it is, will all be included in the risk assessment. Then you should implement the necessary procedures, such as access limits, low privileges, monitoring for suspicious behavior, warning of threats, and fixing glitches.
- There should be continuous employee training to create awareness about shadow data. Employees must be informed about the best practices to avoid contributing to shadow data.
Shadow data in the real world
Let’s look at some real-world scenarios where shadow data has occurred and caused some serious problems.
- Twitter reported a bug that started storing users’ personal data like usernames and passwords into a readable text file on their system. The user information is usually protected using hashing algorithms, and this bug resulted in compromising 330 million users’ data. Twitter then asked its users to change their passwords and use a two-factor authentication process to secure their accounts.
- In 2016 a social networking platform Adult FriendFinder Networks reported a data breach. In this incident, twenty years old data from six different databases were leaked. This data was protected by weak hashing algorithms. The data was pretty old, and its security architecture was not updated.
Can we eliminate shadow data? No. Mismanaged data repositories will always exist. Shadow data is something that can’t be completely eliminated. It’s a typical result of a company growing at a fast speed and is rarely intended. However, there are ways to make sure you’re safe and have the right level of access to every location where your data may reside. Data monitoring tools in the cloud can help data security and data quality teams fast-track their monitoring of cloud-based data assets. You can identify where your shadow data stores are and who owns them by having comprehensive data monitoring. By doing this, an organization can create a secure environment, make decisions more quickly and intelligently across the board, and succeed in a world that is dominated by the cloud. Identifying shadow data as an issue and a possible threat to your organization in the future is the first step you can take to secure your data. Once shadow data is part of the data security policy, it’s easier to do risk assessments and monitor data assets. These best practices can make sure you are properly discarding unused data assets and leaving no room for hackers to access your data.