Learn what dark data is, why it’s a security risk, and how to discover, classify, and secure it across your environment.
Every day, organizations collect, process, and store vast amounts of data ⸺ from transaction logs and emails, to video files, documents, and machine telemetry.
Yet, much of this information never sees the light of day. It remains unused, unclassified, and ungoverned. This is what experts call dark data.
According to Gartner, dark data refers to “information assets organizations collect, process, and store during regular business activities, but generally fail to use for other purposes.”
It’s the digital exhaust of modern business: i.e. data that’s been gathered but never analyzed or integrated into decision-making systems.
Dark data exists in every enterprise: log files; archived emails; chat histories; obsolete CRM exports; and shared folders that have not been accessed in years. Left unchecked, it becomes both a security liability and a missed opportunity.
Dark data doesn’t just consume digital space ⸺ it consumes real cost and energy. In fact, IDC estimates that enterprises spend an average of over $650,000 each year maintaining data they no longer use.
Storage, replication, and backup of this unused data inflate cloud and on-premises costs while increasing an organization’s carbon footprint.
Without classification or lifecycle management, this data continues to grow silently, filling up expensive storage tiers. Over time, what starts as an IT nuisance becomes a budget and governance problem.
Hidden among these unused datasets are sensitive information assets ⸺ eg. personal identifiers, payment details, or confidential business records. Because they aren’t visible to governance systems, they escape encryption, masking, or access control policies.
This makes dark data a compliance and breach risk. It can violate privacy laws such as GDPR, HIPAA, and PCI DSS; or expose organizations to regulatory fines when unprotected records are discovered post-incident.
(Read more in The Leading Data Breach Risks for Enterprises.)
Dark data isn’t inherently useless. It’s just untapped. Buried within could be operational insights, customer behavior patterns, or AI training opportunities.
However, to unlock all that value safely, companies must first discover what data they have, classify it accurately, and apply governance controls.
The largest share of dark data is unstructured: text documents; images; PDFs; videos; chat logs; and emails. These lack predefined schema and are difficult to query or tag; which means they often escape traditional data management and security systems.
Dark data also exists in semi-structured formats such as JSON, XML, and, or CSV files ⸺ i.e. data that is machine-readable but rarely integrated into core analytics.
(Explore more on Unstructured Data Discovery.)
Old backups, legacy systems, and shadow IT repositories ⸺ like unsanctioned cloud drives or SaaS apps ⸺ are other dark data sources. These silos accumulate redundant, obsolete, or trivial (ROT) information, much of it containing sensitive data that has outlived its business purpose.
Every overlooked backup or forgotten share widens the perimeter that attackers can exploit. These hidden repositories often lack modern security controls or visibility within security monitoring systems, making them low-hanging targets for exfiltration activity.
Unmonitored repositories frequently contain sensitive exports, legacy credentials, or configuration files that expose internal processes. Because these assets are rarely audited or encrypted, they can be inadvertently shared or accessed; leading to silent data leaks long before detection by traditional DLP or SIEM tools.
If data isn’t inventoried, it can’t be governed, retained, or deleted in accordance with privacy laws like GDPR, HIPAA, or PCI DSS. Dark data undermines audit readiness and creates regulatory exposure, particularly when organizations cannot prove how or where sensitive information is stored.
As enterprises integrate generative AI and machine learning, unclassified or ungoverned datasets risk contaminating AI models with sensitive or regulated information.
Feeding unvetted dark data into training pipelines can result in data leakage through model outputs, raising ethical, legal, and reputational concerns.
Managing dark data isn’t just about reducing storage costs – it’s a cornerstone of data integrity, privacy, and organizational trust.
Without systematic discovery, classification, and governance, dark data becomes the invisible weak link in otherwise mature cybersecurity and compliance programs.
Modern data protection starts with visibility. You can’t protect what you don’t know exists. That’s why data discovery and classification are foundational to handling dark data.
Traditional tools scan specific databases or file systems, but they miss data moving between systems. A modern approach involves network-layer discovery, which is to identify sensitive information in transit and at rest, across on-premise, cloud, and SaaS environments.
DataStealth’s data discovery engine operates at this layer. It can identify data flowing through applications, APIs, and files – i.e., without agents or code changes – allowing organizations to find dark data where it lives.
Discovery is only half the solution. Once found, data must be classified to understand its type, purpose, and sensitivity.
Classification engines use pattern recognition, AI, and custom rulesets to determine if information includes personal identifiers (PII), payment data (PCI), or protected health information (PHI).
DataStealth’s data classification engine automates this process, classifying data based on both content and context so that policies can be applied consistently across hybrid environments.
After the classification phase, organizations can protect sensitive data via encryption, tokenization, masking, or redact data based on its risk profile.
At this stage, solutions like DataStealth’s proprietary Platform can tokenize sensitive data in-line, ensuring that private information never enters unprotected systems in the first place.
This approach reduces compliance scope while maintaining operational transparency.
Governance isn’t just policy… it’s process.
After carrying out sensitive data discovery flows, organizations should maintain a central data inventory, define ownership, and automate retention or deletion based on business and legal requirements.
Combining automated discovery, classification, and policy enforcement ensures dark data is continually surfaced, managed, and secured.
(For a broader framework, explore the Zero Trust Checklist and Zero Trust Best Practices).
Not all dark data can be deleted. Some must be secured in place. Tokenization, masking, and encryption render data unreadable to unauthorized users, mitigating compromisation risk even if storage locations are accessed.
DataStealth’s data protection suite automates these controls without disrupting applications, ensuring continuous protection of both structured and unstructured data.
Dark data is dynamic. As new systems, SaaS apps, and APIs emerge, previously visible data can become invisible again. Continuous monitoring, classification updates, and data type handlers are essential to stay ahead of this drift.
Once discovered and governed, dark data can safely contribute to analytics, AI development, and data-driven innovation.
By combining discovery and protection, organizations transform what was once a liability, into a strategic asset: ie. fueling predictive models, customer insights, and process optimization; all without breaching compliance.
This balance of visibility and control underpins data-centric security (a key theme in our guide to multi-cloud security).
For related strategies, see What Is Data Sprawl and How to Regain Control
Overall, dark data isn’t new. It’s simply growing faster than most organizations can manage. The challenge isn’t just identifying it, but ensuring it’s governed, secured, and usable in a world that’s now defined by AI, SaaS, and continuous integration.
By combining discovery, classification, and protection, enterprises can turn their dark data into a controlled and compliant asset; thereby illuminating the parts of their digital environment they’ve long overlooked.
Explore how in-line structured/unstructured data discovery and data-centric security can help you do all this and more, at DataStealth, today.
Bilal is the Content Strategist at DataStealth. He's a recognized defence and security analyst who's researching the growing importance of cybersecurity and data protection in enterprise-sized organizations.
