Shadow Data Security: Strategies for Optimal Protection

Businesses have long struggled to manage their databases. While digital transformation has helped streamline many processes, an increase in remote and hybrid work—and a proliferation of personal devices and cloud services—have made it more challenging than ever to control sensitive information. It has also led to an explosion of shadow data that requires innovative, robust security strategies to manage effectively.

Developing shadow security policies differs from traditional data management in several key ways:

It often lacks clear ownership.
It exists in unknown locations.
It might not adhere to standard formats or classifications.

These ambiguities require a more flexible and comprehensive approach that emphasizes discovering shadow data, educating employees, and creating secure alternatives, rather than just relying on perimeter defenses and access controls.

Shadow Data: The Threat You Don’t Know About

Shadow IT is the unauthorized use of applications, internet services, and third-party software that’s used to share files and send messages. Shadow data pertains to unauthorized or unmanaged data storage and handling. Both present significant challenges and risks for organizations, primarily regarding security, compliance, and data integrity.

Organizations seeking advanced solutions for workplace challenges sometimes turn to unauthorized software, cloud services, or personal devices, inadvertently creating vulnerabilities in organizational defenses. This is particularly concerning in sectors dealing with sensitive information, such as healthcare, finance, and government, where risks extend beyond mere breaches and encompass a range of issues, including regulatory violations and inefficient resource allocation.

Where Does Shadow Data Come From?

Sometimes referred to as dark data, shadow data originates from various sources within an organization. Common sources include:

Employee-Generated Data. Employees often create data that isn’t officially tracked or managed by the organization’s IT department. This can include personal notes, drafts, and documents stored on local drives, personal cloud services, or external devices. While this data can be helpful for individual productivity, it often bypasses standard data management and security protocols, creating potential vulnerabilities.
Shadow IT. While many apps and services employees use can enhance productivity, some also introduce significant risks as the data stored and processed by them is typically outside the purview of the organization’s security measures.
Unstructured Data. Organizations generate vast amounts of unstructured data, such as emails, instant messages, social media posts, and multimedia files. This data often resides in disparate locations and formats, making it difficult to track and manage. Unstructured data can contain sensitive information that, if not properly secured, can lead to breaches and compliance issues.
Log Files and System Records. System logs and records generated by various apps, servers, and network devices can accumulate rapidly. These log files often contain critical information about system performance, user activities, and potential security incidents. However, they’re frequently overlooked in data management strategies, resulting in shadow data that can be exploited if accessed by unauthorized individuals.
Backup and Archive Data. Businesses routinely create backups and archives to ensure data recovery in case of system failures or data loss. Over time, these backups can accumulate redundant or outdated information that, if not properly managed, can become a source of shadow data.
Third-Party Integrations. Many companies integrate third-party applications and services into their operations that involve data exchanges that aren’t always monitored or secured. As a result, data shared with or processed by third-party services can become shadow data, particularly if those services do not adhere to the organization’s security standards.
IoT Devices. The proliferation of Internet of Things devices in the workplace has introduced new data sources. These devices generate and transmit large volumes of data, often without adequate security measures in place. IoT-generated data can easily become shadow data if not properly integrated into the organization’s data management framework.
Orphaned Data. Data can become “orphaned” when projects are completed, employees leave the organization, or systems are decommissioned without proper data disposal procedures. This data can remain on servers, local drives, or cloud storage indefinitely, becoming part of an organization’s shadow data landscape and posing a security risk if not properly managed.

Security Risks of Shadow Data

Shadow data’s primary risk is its invisibility. Close behind are shadow data breaches due to improper security measures that make sensitive information vulnerable to misuse or theft. For instance, in healthcare, patient records shared via unsecured messaging apps could lead to widespread exposure of confidential medical information. Regulatory compliance violations and their accompanying legal consequences and fines are another significant risk, as some shadow data systems might not meet industry-specific compliance requirements.

Other risks include:

Loss of data control due to information scattered across various unapproved platforms, making shadow data difficult to track, manage, and protect. For example, storing and sharing sensitive data on personal devices outside secure networks increases the risk of misuse or breach.
Inefficient resource allocation is a frequent problem, with duplicate or redundant systems wasting financial resources and creating administrative overhead.
Integration and compatibility issues can cause workflow disruptions, as unauthorized tools often don’t integrate well with existing systems.
Increased IT support complexity makes it challenging for IT teams to manage and troubleshoot a vaster array of unauthorized tools, reducing overall efficiency.
Inconsistent user experience, where different teams use various unauthorized tools, can lead to inconsistent processes and outputs.
Impaired decision-making can result from fragmented data across official and shadow systems, leading to incomplete analysis and flawed strategic choices.
Security vulnerabilities can sometimes arise from unpatched or improperly configured shadow IT systems, enabling unauthorized access or data misuse.
Data loss is also a risk, as information stored on unauthorized systems might not be included in regular backups, risking permanent loss.

Developing effective strategies to mitigate these and other risks requires a multifaceted approach that balances security needs with employee productivity and innovation. By understanding these risks and developing security policies to manage shadow IT, organizations can protect their assets while fostering a culture of responsible technology use.

Shadow Data Security Strategies for Optimal Protection

Organizations everywhere are awash in data from countless sources. This data tsunami often results in the inadvertent or accidental accumulation of shadow data—unregulated and potentially risky data repositories. While this data can provide valuable insights, it also presents significant security challenges.

To ensure optimal protection and mitigate risks, organizations must adopt comprehensive shadow data security strategies, including artificial intelligence (AI) and generative AI (GenAI) to enhance data security.

1. Comprehensive Data Discovery and Classification

The first step in addressing shadow data is identifying where it resides. A data security platform with advanced data discovery tools can scan all potential data repositories. AI-powered solutions automatically classify data based on its sensitivity and relevance, enabling businesses to prioritize protection efforts. By understanding what data is stored, where it’s located, and how sensitive it is, companies can tailor their security measures accordingly.

2. Implementing Robust Access Controls

Access controls are critical in preventing unauthorized access to sensitive data. Implementing intelligent and sophisticated policy based access control (PBAC) ensures that only authorized personnel can access certain data types. AI enhances these systems by monitoring access patterns and identifying anomalies that may indicate a security breach. This proactive approach mitigates shadow data risks by ensuring that access is strictly regulated.

3. Continuous Monitoring and Threat Detection

Round-the-clock monitoring of data environments helps businesses identify and respond to potential threats in real-time. AI and machine learning algorithms analyze vast amounts of data to detect unusual activities or patterns indicating security issues. This real-time analysis allows for rapid response to potential threats, minimizing the risk of data breaches. Incorporating GenAI further enhances these systems by predicting future threats based on historical data and trends.

4. Data Encryption and Masking

Encrypting sensitive data ensures that even if unauthorized access occurs, the data remains unreadable. Implementing robust encryption protocols for data at rest and in transit is essential. Data masking can also be used to obfuscate sensitive information in non-production environments, reducing the risk of exposure. AI-powered solutions help manage encryption keys and automate data masking processes, making these strategies more efficient and effective.

5. Regular Audits and Compliance Checks

Conducting routine audits ensures shadow data is identified and appropriately managed. Simplifying compliance with data protection regulations such as GDPR, CCPA, and HIPAA can be accomplished with AI-driven audit tools that streamline the process by automatically checking for compliance and identifying areas that require attention.

6. Employee Training and Awareness

Human error is a frequent and significant factor in data security breaches. Regular employee training and awareness programs help mitigate this risk. Staff should be educated on the importance of data security, best practices for managing sensitive information, and the risks associated with shadow data. Incorporating AI-driven training platforms that provide personalized learning experiences and real-time feedback ensures employees stay informed and vigilant.

7. Leveraging GenAI for Data Management

GenAI can be a powerful tool in managing and securing shadow data. By generating synthetic data that mimics real data, organizations can safely test and develop their systems without exposing actual sensitive information. The technology also assists in automating data classification, anomaly detection, and threat response, making the overall data management process more efficient and secure.

Detect & Minimize The Risks Associated With Shadow Data

As we’ve seen, shadow data comes from a variety of sources, often emerging from well-intentioned efforts to enhance productivity and operational efficiency. However, without proper oversight and management, this data can create significant security vulnerabilities. By identifying the origins of shadow data, organizations can take proactive steps to incorporate these data sources into their overall data management and security strategies, thereby mitigating risks and ensuring comprehensive data protection.

Velotix provides organizations with proactive data security solutions that transform how they manage and protect sensitive information, minimize unstructured data exposure risks, and effectively address the challenges posed by shadow data.