Understanding Dark Data and Its Role in Data Security Risks

Organizations generate and collect more data than ever, from customer interactions and server logs to sensor outputs and internal documents. While some of this data is actively analyzed and used, a large portion sits untouched and unexamined.

This overlooked information is known as Dark Data. It can include anything from outdated spreadsheets to unused log files, often stored without a clear purpose. Left unmanaged, dark data not only represents missed opportunities for insight but also increases security and compliance risks. Read this post to learn what dark data is, explore common examples, and understand why it is essential to protect it.

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What Is Dark Data?

Dark data is information that an organization collects, processes and stores during regular business activities but does not actively use for any meaningful purpose. It refers to unused, untapped or unanalyzed digital information that resides in systems, backups or storage. It is called “dark” because it remains hidden.

Key characteristics

The phenomenon of dark data accumulation happens because organizations think that all information that is possible to capture should be stored, since they might use it one day. In practice, this data is unused in most cases because, without proper metadata, it is difficult to retrieve specific information, especially if the data format is unstructured and cannot be retrieved using a query.

Dark data can lead to wasted storage space and missed opportunities. It is like digital clutter, often ignored, but potentially risky and valuable. Properly managing it can reduce security threats, save costs and unlock hidden insights.

Key characteristics of dark data

Dark data vs unstructured vs obsolete data

Let’s explain the difference between dark, unstructured, and obsolete data.

• Dark data is collected but never used.
• Unstructured data lacks a predefined structure and may or may not be used.
• Obsolete data was useful, but is now outdated.

These types of data can overlap. A large amount of dark data is unstructured, and some unstructured data may be obsolete. However, not all unstructured or obsolete data is dark.

The three data types are compared in the table below:

Why Data Goes Dark

Data becomes dark when collected but never used, analyzed or managed effectively. This situation typically occurs due to a combination of technical, organizational and strategic issues. 

• Data is generated automatically. Systems, applications, sensors and logs continuously produce vast amounts of data. Much of this data is captured passively (such as server logs or telemetry) without a plan to analyze it.
• Lack of awareness or visibility. Organizations often don’t know what data they have, where it’s stored or what it contains. Data may be scattered across departments, legacy systems or cloud platforms, invisible to decision-makers.
• Poor data management and governance. Without policies for classification, lifecycle or usage, data gets stored without purpose. This happens when no regular audits determine what’s still valuable or necessary. In this case, data can become disorganized and unusable. Some organizations lack dedicated IT specialists or expertise to work with dark data.
• Business silos and fragmentation. Data is locked in departmental silos, making it inaccessible to those who might benefit from it. This happens when departments collect and store data independently. Teams may not share information or even realize they have overlapping data needs.
• Legacy systems and storage habits. Older systems archive data “just in case” and keep it indefinitely without review. Over time, this archived data gets forgotten or becomes irrelevant. Business priorities may change and data that was actively used before may become less relevant when the priorities in an organization change.
• Lack of tools or skills to analyze data. Organizations may lack the tools, personnel or strategy to mine and process large or complex datasets. This is especially true for unstructured data like images, audio and video. If the resources are limited, an organization can prioritize data collection rather than data analysis.
• Cost or risk of analyzing data. Processing and analyzing large data volumes can be costly. In regulated industries, analyzing old data may expose compliance risks and, thus, it’s left untouched.
• Perceived lack of value. Teams may not see a clear business use for certain types of data. If the data was not collected with a specific purpose, it is often overlooked.
• Low storage price. The relatively low cost of digital storage encourages organizations to keep everything, even if unused. This “save now, decide later” approach fuels the growth of dark data.

Data goes dark when it’s easier to store than to understand. The lack of strategy, visibility and tools turns potentially valuable information into digital dead weight, increasing costs and risks while missing out on insights.

Types and Sources of Dark Data

Dark data can be structured, unstructured and semi-structured. 

• Structured data is typically stored in database fields in tables and can be retrieved using queries. Sensitive data, such as bank information, medical information and customer data, is often stored in databases, but it is difficult to categorize because of limited permissions and regulation requirements.
• Unstructured data is stored without using databases or spreadsheets and cannot be effectively analyzed without conversion. Email messages, PDF files, text documents, voice recordings and surveillance video footage are common examples of unstructured data that can become dark data.
• Semi-structured data is unstructured, but some of its information is defined in data fields. HTML pages, XML documents, tables, graphs and invoices are examples of semi-structured data. It is possible to partially search and catalog this data.

The different types of dark data can be industry-specific. Below, you can see examples of dark data.

System logs and machine-generated data

This type of dark data includes:

• Server and application logs
• Security logs (including failed login attempts)
• Firewall and network activity logs
• Device telemetry
• Sensor data from industrial or smart devices
• Geolocation data
• Debugging and error logs

Customer interactions

Customer interactions are another type of unstructured data that includes:

• Emails (inboxes, archives, specific platforms)
• Chat logs from customer support or bots
• Call recordings (contact centers, sales teams)
• Voicemail messages and voice record transcripts
• CRM notes and history
• Social media interactions

Legacy backups and old archives

This data category is a common type of unstructured data that includes:

• Outdated file server backups, VM backups, etc.
• Old database dumps
• Archived emails and mailboxes
• Tape cartridges or legacy storage media
• Obsolete application data

Document versions and unmanaged files

In some cases, there are multiple versions of documents and files. They also represent dark data:

• Duplicated or outdated versions of spreadsheets, presentations and documents
• Local desktop files that never reach the cloud or centralized data storage
• Temporary files, autosaves or drafts
• Files on shared drives with no naming convention or version control

Hidden Risks and Costs of Dark Data

Dark data poses hidden and often underestimated risks and costs to organizations. While sitting idle in servers may seem harmless, it can create serious financial, legal, security and operational consequences. Let’s explain the hidden risks and costs of storing dark data without proper administration.

Cybersecurity threats and breaches

Dark data often contains sensitive information (personally identifiable information, credentials, IP addresses, financial data and others) that is unprotected or unmonitored. Thus, this data can be an easy target for cybercriminals. Hackers can exploit unpatched archives, exposed file shares or outdated backups. If compromised, it can lead to data breaches, identity theft or cyber extortion (this data can also be sold/published on dark web data leak sites). Since dark data is overlooked, no alerts are triggered if it’s accessed or stolen. Organizations often don’t know what’s been compromised until it’s too late.

Sensitive information like passwords, customer data or internal documents stored in dark data can be leaked or ransomed.

Examples of negative consequences:

• Legacy email backups containing team member credentials get exposed in a ransomware attack.
• Archived customer emails containing personally identifiable information are exposed in a phishing attack, resulting in identity theft and reputational damage.

Regulatory compliance risks

Storing dark data unnecessarily may violate data retention or privacy laws (like GDPR, HIPAA, CCPA). These regulations require data to be classified, secured and retained only as long as necessary. Dark data often contains sensitive personal or health-related information that violates retention or encryption requirements.

The risks include:

• Regulators may fine organizations for keeping data longer than allowed or failing to secure it adequately.
• The discovery of dark data during legal proceedings (eDiscovery) can expose organizations to unexpected legal risks.
• Keeping unclassified old customer data may result in non-compliance penalties if not encrypted or properly documented.

The negative consequences are:

• Heavy fines, lawsuits and audit failures.
• Difficulty in executing legal rights like the “right to be forgotten” (GDPR) when dark data isn’t even mapped.

Unnecessary storage and infrastructure costs

Accumulating dark data scales up costs for:

• Storage hardware and datacenter space
• Cloud subscriptions, including cloud storage and egress fees
• Backup, replication and disaster recovery systems (disaster recovery infrastructure)
• Cooling and energy usage (for on-premises file servers and database servers)

An organization is paying to store, back up and secure data that provides no value. In large enterprises, dark data can consume 50-80% of total storage.

Impact on analytics and business decisions

Dark data clutters data lakes, warehouses and dashboards with redundant or irrelevant information. It leads to data inconsistency, duplication and analysis paralysis. Valuable insights remain buried, while business decisions are based on partial or misleading data.

Dark data impacts analytics by:

• Making data environments cluttered and harder to navigate.
• Slowing down searches, data access and migration projects.
• Causing confusion over what data can be trusted.
• Wasting the time of analysts who sift through irrelevant or outdated information.

The negative business impact of dark data:

• Poor product strategies and customer targeting
• Missed trends in customer behavior
• Slower decision-making due to noise in data systems

Over time, unmaintained data may become corrupted, unreadable or incompatible with modern systems. In a disaster recovery scenario, restoring old, dark data might fail or introduce errors into active systems. Dark data may seem invisible, but it silently multiplies risk and costs.

How Dark Data Affects Data Security

Without proper management, dark data can lead to negative security consequences. This data can be vulnerable to cyber-criminals due to a lack of administrative attention, including encryption and protection.

• Dark data expands the attack surface. Every forgotten backup, old email archive or untracked file adds to the potential entry points for cybercriminals. The more data you store (especially unprotected), the more opportunities hackers have to exploit vulnerabilities. For example, a poorly secured FTP server with archived documents can become a weak link in an otherwise secure system.

• Dark data lacks visibility and monitoring. This data is usually not logged, scanned or audited. It doesn’t benefit from Data Loss Prevention software, antivirus or EDR solutions. As a result, breaches involving dark data often go undetected for months.
• Dark data bypasses modern security controls. Legacy formats and locations (like tape drives or old SQL dumps) may not be covered by encryption policies, access controls and multi-factor authentication. For example, an old HR database dump with plaintext passwords stored in an open share goes unencrypted and unnoticed.
• Dark data creates data retention risks. Security best practices recommend minimizing data retention, but dark data persists indefinitely. This increases the exposure window for sensitive data long after it’s needed. Even if a cyberattack happens today, old, unused data from years ago can be leaked or sold.

Dark data is a blind spot in cybersecurity. You can’t protect what you don’t know you have, and attackers are betting on that. Dark data discovery can be a starting point for proper data management.

How to Manage and Reduce Dark Data

Managing and reducing dark data is crucial for improving security, compliance, cost-efficiency and business intelligence. The recommended practices for dark data management are explained below.

• Discover and classify your data. Use data discovery tools to scan servers, cloud storage, databases and computers. Identify location, file type, age, owner and sensitivity. Tag data by business relevance or regulatory category.
• Develop a data governance strategy. Governance ensures that every piece of data has a purpose, owner and expiration. Define clear policies for data lifecycle management:
• What data to keep
• For how long (retention)
• Who owns it
• Where it should live
• Implement data ownership responsibilities across departments.

• Clean up legacy data. Audit old backups, archives and storage locations. Remove the following data:
• Redundant or outdated backups
• Obsolete versions of files
• Unused databases
• Consolidate useful legacy data into structured, accessible formats.
• Consider using data retention rules to auto-expire and purge irrelevant data.

• Secure sensitive unstructured data. Encrypt or restrict access to email archives, spreadsheets, PDF files and voice/video files. Apply access controls, version management and audit logging. Even unused data needs protection until it’s reviewed or removed.
• Establish regular data management tasks. Schedule quarterly dark data reviews, annual storage audits and regular DLP scans. Train users on proper data handling and encourage the “store with purpose” mindset. Don’t wait for a breach or audit; clean up proactively.
• Optimize cloud storage. Classify cloud data by activity level. Automate auto-deletion or move-to-archive rules. Avoid over-retention in shared drives or object stores.

Organizations can gain serious benefits by transforming dark data despite the challenges. It is recommended to provide data analysts with access to data sets and create effective automated workflows. When dark data is analyzed, performance metrics can be tracked to make more rational decisions regarding resource allocation and optimization.

How NAKIVO Can Protect Against Dark Data Risks

Backups can protect your organization from the risks associated with dark data. However, if they’re mismanaged, they can also become a source of dark data themselves.

NAKIVO Backup & Replication is a dedicated data protection solution that can help you protect your environment and reduce the amount of dark data related to backups.

Backups are essential in terms of dark data management. If dark data contains business-critical information, a secure backup can be a lifesaver during disaster recovery. Instead of letting old or unused data clutter production systems, move old data to encrypted, versioned backups or cold storage. This isolates dark data while preserving access for compliance or future insights.

• With advanced retention settings, you can implement custom retention policies and define how long data is stored in the backup repository. You can align this configuration with regulation requirements, such as GDPR, taking into account the right to be forgotten. This keeps your backups from becoming dark data warehouses.
• Backup encryption. The NAKIVO solution supports source-side and target-side encryption for backed-up data. Encrypted backups are better protected against unauthorized access, which reduces security risks.
• Log truncation. When you back up MS SQL Server databases, log truncation allows you to store only backup data without logs, reducing the amount of dark data.
• Backup immutability. Protect backups against being modified and deleted by ransomware using immutable backups. This feature reduces the risks related to losing dark data in backups.

Conclusion

Without proper management, dark data can waste storage space but can also be useful for business insights. Follow the recommended practices for data administration and remember to back up your data. Backups help ensure that even dark data is well-protected against deletion or corruption. Use NAKIVO Backup & Replication for reliable and advanced data backup and recovery.

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