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What Is Shadow Data?
5 min. read
Shadow data is a term used to describe unknown, hidden, or overlooked copies of sensitive information that exist outside the purview of an organization's IT security measures. Shadow data can reside in various forms, such as unstructured files, structured databases, or even cloud storage, often without the knowledge or control of the IT department.
Shadow Data Explained
Shadow data is data that is created, stored, or shared without being formally managed or governed by the relevant IT teams. Shadow data can be found in spreadsheets, local copies of databases, emails, and presentations. It would often find its way to personal devices, but shadow data assets can also live on cloud storage such as Amazon S3, or as overlooked tables in a database.
Shadow data can pose a security risk to organizations. In most cases, security controls and policies won't be applied to this data, which makes it more difficult to track and monitor, and more vulnerable to unauthorized access.
To mitigate the risks associated with shadow data, it’s important for organizations to have policies and procedures in place to manage and govern the creation, storage, and sharing of new datasets. In addition, organizations can use data security tools such as DSPM to identify, classify, and secure shadow data.
The Dangers of Shadow Data
Often lurking in the uncharted corners of an organization's digital landscape, shadow data poses a formidable challenge for IT security teams. This elusive data is the result of employees creating and storing files on personal devices or unauthorized cloud services, data duplication through backups or migration processes, unauthorized extraction of data by insiders, and leakage through third-party applications or partners. The nature of shadow data evades conventional security measures, leaving it exposed to threats and vulnerabilities.
As organizations grapple with the consequences of unsecured shadow data, they confront a myriad of risks. Data breaches become more likely as unauthorized access or accidental exposure becomes increasingly possible. Compliance violations emerge as businesses struggle to adhere to data protection regulations, resulting in hefty penalties. The loss of intellectual property can cripple an organization's competitive advantage and tarnish its reputation. Decision-making, as well, falters when based on incomplete or outdated information, leading to poor business outcomes.
To counter these threats and regain control over shadow data, organizations need to adopt a proactive approach. By implementing robust data discovery and classification tools, enforcing strict access controls, and promoting a culture of data security awareness, businesses can effectively address the challenges posed by shadow data and ensure the confidentiality, integrity, and availability of their sensitive information.
Mitigating Shadow Data Risks
Mitigating shadow data risks requires a multi-faceted approach that combines technology, policies, and user education. The following strategies can help organizations effectively address the challenges posed by shadow data.
Data Discovery and Classification
Implement advanced data discovery tools that support a wide range of file types and storage locations, including structured and unstructured data, on-premises systems, cloud services, and personal devices. Choose a data classification solution that offers customizable classification criteria, automated tagging, and support for industry-specific regulations.
Access Controls and Permissions
Establish strict access controls to ensure only authorized users can access sensitive data. Implement role-based access control (RBAC) and attribute-based access control (ABAC) models to enforce granular access restrictions. Implement multifactor authentication (MFA) and single sign-on (SSO) to enhance access security. Regularly audit user permissions and access logs to identify and remediate excessive or unused privileges.
Data Loss Prevention (DLP) Solutions
Select data loss prevention (DLP) solutions with features such as content-aware detection, real-time monitoring, and policy-based enforcement. Ensure the chosen solution can integrate with existing security tools, applications, and cloud services to provide comprehensive coverage across the organization's data ecosystem.
User Activity Monitoring
Implement monitoring solutions that provide detailed insights into user actions, including file access, modification, deletion, and sharing. Choose solutions that offer real-time alerts, customizable thresholds for anomalous behavior detection, and integration with security information and event management (SIEM) systems.
Encryption
Use strong encryption algorithms, such as AES-256 or RSA-2048, to protect sensitive data. Implement key management best practices, including secure key storage, rotation, and backup. Consider using encryption solutions that offer central management, policy enforcement, and compliance reporting.
Secure Collaboration and File-Sharing Tools
Evaluate and choose secure collaboration tools that offer end-to-end encryption, granular access controls, and activity tracking. Ensure these tools can integrate with existing systems and comply with industry-specific regulations.
Policy Development and Enforcement
Develop comprehensive data security policies that address data classification, data storage, sharing, retention, and disposal. Assign responsibility for policy enforcement to specific individuals or teams and establish clear consequences for non-compliance.
User Education and Training
Educate employees on the risks associated with shadow data and the importance of adhering to organizational policies. Create a comprehensive security awareness program that includes training on data security best practices, organizational policies, and industry-specific regulations. Offer regular training sessions, simulations, and assessments to reinforce learning and measure the program's effectiveness.
Incident Response Planning
Develop an incident response plan that outlines the steps to be taken in case of a data breach or security incident involving shadow data. Your plan should clearly define roles, responsibilities, and procedures for detecting, containing, eradicating, and recovering from security incidents involving shadow data. Regularly review, test, and update the plan to ensure its effectiveness and alignment with evolving threats and organizational changes.Conduct drills to ensure that the organization is prepared to handle incidents effectively.
Shadow IT FAQs
An example of shadow data is when an employee saves a copy of a sensitive company document, such as a sales report or client contract, to their personal cloud storage account for convenience. This action creates an unauthorized replica of the data, which now resides outside the organization's IT security infrastructure. As a result, the company loses visibility and control over this data, leaving it susceptible to unauthorized access, potential data breaches, and compliance violations.
A data shadow system refers to an unofficial, parallel data management system created and maintained by users outside the purview of the organization's IT department. These systems often emerge when employees perceive the official systems to be too complex, slow, or restrictive. They may develop spreadsheets, databases, or applications to manage data and processes more efficiently. Because they bypass the organization's security protocols and data management policies, data shadow systems can introduce security risks, data inconsistencies, and integration challenges.
A shadow dataset is a collection of data that exists beyond the control and oversight of an organization's IT department and security measures. These datasets can be created intentionally or unintentionally, through actions such as data duplication, unauthorized extraction, or data sharing with external parties. Shadow datasets pose significant risks to an organization's data security, as they can lead to breaches, compliance issues, and inaccurate decision-making due to a lack of visibility and control over the data.
A cloud access security broker (CASB) is a security solution that acts as an intermediary between an organization's on-premises infrastructure and cloud service providers. CASBs provide visibility and control over the usage of cloud-based applications and services, helping organizations enforce security policies, manage access, and protect sensitive data. Key features of CASBs include monitoring user activity, enforcing access controls, detecting and preventing data leakage, and ensuring compliance with regulations like GDPR.
Scope creep refers to the gradual expansion of a project or task's objectives, requirements, or features beyond its original goals, often leading to increased complexity, delays, and resource consumption. In the context of cloud security and GDPR, scope creep may occur when unauthorized applications gain excessive OAuth permissions, broadening their access to sensitive data or systems. This expansion can inadvertently expose the organization to security risks, non-compliance issues, and potential data breaches.
Application sprawl describes the uncontrolled proliferation of software applications within an organization, often resulting from the adoption of numerous tools to accomplish similar tasks. This phenomenon can lead to inefficiencies, increased costs, and security vulnerabilities. In the context of shadow IT, application sprawl occurs when employees independently adopt multiple unauthorized applications, creating a complex landscape that is difficult to manage, secure, and maintain in accordance with data protection regulations like GDPR.
