Sanitizing Unstructured Data In Motion—and Why It’s Important

Data is in perpetual motion within organizations, continuously crossing various networks and systems. This data drives decision-making and fuels innovation, shaping strategic initiatives across departments. However, many organizations lack a deep understanding of their data’s contents at any given moment, partially due to the massive volume they handle. 

According to recent research, 41% of organizations manage at least 500 petabytes of data, equivalent to almost 5 million full-length movies in 4K quality. With that number only continuing to grow (and much of it being stored within company data lakes) there’s a real need to keep it all safe. 

Understanding Unstructured Data

Unstructured data is characterized by its lack of a predefined format or organization. It encompasses various information types such as text, images, videos, and social media content. This data comprises most data used in daily business operations, accounting for 80% of the data stored. Unlike structured data, it’s not easily sorted into traditional database fields, making it harder to sort and analyze. 

To get a better idea on structured versus unstructured data, we have a blog on that very topic.

For most organizations, unstructured data does not exist as videos but as a mix of documents, emails, multimedia, and other unstructured formats that are difficult to search and index. Some of the data is routine information, but it may also include sensitive data, which could be harmful if lost. Even worse, hidden threats like malware may lurk in seemingly safe files, such as simple documents.

It should be noted that artificial intelligence (AI) and machine learning (ML) algorithms ingest unstructured data as part of their learning. With generative AI programs being fed an internet’s worth of this data, it’s easy to see how unstructured data can be leveraged for harm if not adequately protected from sensitive data collection, exposure, and hidden malware. 

Overseeing how this data is utilized as it is shared across the organization or with external parties may feel like an impossible task. Companies face the challenge of facilitating employee and partner collaboration with the risk of their work spreading sensitive data or hidden threats.

Unstructured Data is More Common than Not

Unstructured data would be easy to assess if it didn’t come in a wide range of formats and exist across every facet of organizational operations. Then, there’s the need to manually classify data so that it can be properly siphoned and stored. Emails may include attachments with business plans, and presentations may have live data examples that are not meant for everyone’s eyes. File upload portals are open to the public and without a process to ensure the safety of protected information, organizations are left with retroactive, manually-intensive intervention to ensure compliance. 

With unstructured data flowing between employees, contractors, and even back to customers, the risk of accidental dissemination of data is exponential. This also doesn’t account for non-accidental incidents such as insider threats. 

Risks in Unstructured Data

The presence of malware, ransomware, and other embedded threats, typically overlooked by conventional security tools, underscores the vulnerabilities associated with unstructured data. Furthermore, the extensive use and diverse nature of unstructured data exponentially increases the organization’s attack surface, creating more opportunities for cyber-attacks.

This expansion not only multiplies the potential entry points for these attacks but also raises concerns regarding the exposure of sensitive information such as personally identifiable information (PII), payment card industry information (PCI), and protected health information (PHI). If the data falls into the wrong hands, such exposures can lead to severe compliance and regulatory repercussions, highlighting the need for stringent data management and protection policies.

Moreover, the challenges extend to detecting these risks, as the variable formats and the lack of structured metadata complicate the implementation of effective security measures.

Eliminating Threats While Data is Still In Motion

Organizations must prioritize sanitizing unstructured data as it moves across organizational boundaries to safeguard sensitive information from potential threats. This process protects against data breaches and cyber threats and maintains compliance with data protection regulations.

Obfuscating Sensitive Data

Modern Data Detection and Response (DDR) technologies typically offer robust solutions for detecting and obfuscating sensitive data within unstructured files. By leveraging advanced scanning technology, DDR systems can identify sensitive information swiftly and accurately. Once detected, these systems employ automated redaction mechanisms to remove or mask sensitive data, thereby maintaining privacy and ensuring compliance with regulatory requirements.

DDR’s real-time processing capability is crucial in managing data securely and efficiently. It enables organizations to handle sensitive data immediately, as identified, minimizing the window for potential data breaches or compliance lapses.

Many modern DDR systems also provide threat and privacy analytics to further enhance security measures, which are indispensable for regulatory adherence and transparency in data management practices. This type of retroactive reporting is essential for organizations to demonstrate compliance and streamline their data governance processes effectively.

Eliminating Hidden Malware in Files

Beyond sensitive data, DDR is crucial in eliminating hidden threats from unstructured data through sophisticated detection and proactive mitigation strategies. Advanced DDR technologies utilize Content Disarm and Reconstruction (CDR) to identify and neutralize zero-day malware by deconstructing and safely reconstructing files. Some DDR solutions also employ traditional antivirus (AV) mechanisms to remove known malware to ensure comprehensive threat coverage.

The Solution: Active Data Protection

Votiro’s Zero Trust DDR capabilities help organizations take control of unstructured data as it flows through the organization. Votiro seamlessly integrates into your existing infrastructure, protecting sensitive data and reducing the risk of hidden threats.

Votiro’s approach to DDR is rooted in the Zero Trust security model, treating every file as a potential threat and rigorously scrutinizing all incoming data. This methodology extends the protection perimeter by integrating seamlessly with security frameworks, enhancing an organization’s overall data protection landscape. By preemptively addressing threats and fortifying existing defenses, DDR ensures that potential vulnerabilities are managed before they can inflict harm.

Votiro also employs advanced analytics to monitor data flows in real time. This ensures that sensitive data such as PII is identified and classified by DDR as it moves through the organization. Automated data handling and protection policies ensure private data types are managed according to business and regulatory standards, preventing unauthorized access and potential data breaches in the process. 

Additionally, Votiro DDR provides customizable filters, allowing organizations to define what constitutes sensitive data according to their specific compliance needs and operational standards.

Founded on our proprietary, advanced CDR solution, Votiro proactively prevents malicious threats from unstructured data as it traverses organizational boundaries. Combined with AV, Votiro DDR preemptively eliminates known and zero-day threats to ensure a multi-layered approach to data security and privacy.

You can learn more about our Zero Trust Data Detection and Response capabilities by signing up for a one-on-one demo of the platform, or you can try it free for 30 days and see how Votiro can defend your unstructured data without adding unnecessary complexities.