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Jan 30, 2025
The average company creates 2.5 quintillion bytes of data daily. Hidden within: Social Security numbers in forgotten spreadsheets, credit card details in legacy databases, and patient records in unsecured folders.
Modern data breaches exploit what you don’t know exists. While security teams focus on known assets, unidentified sensitive data sits vulnerable in cloud drives, email attachments, and dormant servers.
As data grows exponentially in volume and complexity, safeguarding it becomes an increasingly daunting task. Manual processes can’t keep up, and traditional tools often fall short. Automated discovery tools find these blind spots before attackers do, scanning much faster than human analysts can.
Let’s explore how to use automated data discovery not as a tool, but as a strategy — as the foundation for maintaining compliance, reducing risk, and ensuring that sensitive data doesn’t fall into the wrong hands.
Data discovery has come a long way. In the past, organizations relied on manual processes to locate and classify sensitive information. IT teams would comb through files, databases, and storage systems, often relying on spreadsheets and outdated tools to track their findings.
This approach was not only time-consuming but also prone to human error. As data volumes grew and regulatory requirements became more stringent, it became clear that manual methods were no longer sustainable.
The rise of unstructured data further complicated the picture. Unlike structured data, which fits neatly into rows and columns, unstructured data includes everything from emails and PDFs to images and videos. It’s messy, scattered, and difficult to manage.
Yet, it often contains some of the most sensitive information an organization holds. Without the right tools, identifying and protecting this data is like searching for a needle in a haystack. Automated data discovery emerged as the solution to these challenges.
Automated data discovery is a process that uses AI and machine learning algorithms to automatically scan, catalog, and analyze data across an organization’s systems to:
This evolution has transformed data security, enabling organizations to stay ahead of threats and maintain compliance in an increasingly complex environment.
Related: What is Data Augmentation and Why Should Security Teams Care?
At the heart of automated data discovery is pattern recognition. This technology enables tools to identify sensitive data based on specific patterns, such as Social Security numbers, credit card details, or medical records.
Unlike manual methods, which rely on humans to spot these patterns, automated tools use algorithms to scan data at scale. They can analyze hundreds of file types, from spreadsheets to PDFs, and detect sensitive information with pinpoint accuracy.
This sensitive data scanning allows organizations to detect and secure critical information using financial records and Personally Identifiable Information (PII) detection or in real-time.
Pattern recognition ensures that no sensitive information slips through the cracks, even in the most complex data environments.
Did you know? Qohash’s Qostodian Recon can process data at an impressive rate of 50 GB per hour, scanning over 350 file extensions. This speed and versatility make it an invaluable tool for organizations dealing with large volumes of unstructured data — request a demo today!
Once sensitive data is identified, it needs to be classified. Classification engines and data classification tools categorize data based on its sensitivity, regulatory requirements, or business relevance.
This step is crucial in automated data discovery for prioritizing protection efforts and ensuring compliance with industry standards.
Specifically automated classification engines eliminate the guesswork and inconsistency of manual processes. They apply predefined rules and policies to categorize data, ensuring that everything is handled appropriately.
This especially helps organizations in regulated industries like finance or healthcare, as it not only streamlines compliance but also reduces the workload on IT teams.
Related: How to Cut Your Incident Response Time in Half
Data doesn’t stay in one place. It moves, it changes, and it’s constantly at risk of exposure. That’s why real-time monitoring is a critical component of automated data discovery.
Real-time data tracking ensures that organizations can monitor sensitive information as it moves and take immediate action to prevent breaches. Similarly, real-time monitoring provides organizations with proactive notifications, enabling them to respond quickly to threats.
For example, if sensitive data is accessed by an unauthorized user or transferred to an insecure location, the system can alert the appropriate teams immediately.
Qohash’s solutions offer 24/7 monitoring, ensuring that organizations are always one step ahead of potential breaches.
Not all data is created equal.
Some information may appear sensitive at first glance but is harmless in context.
Conversely, seemingly innocuous data can pose significant risks when combined with other information. Context analysis helps organizations understand the relationships, usage, and intent behind their data.
Metadata discovery is a crucial aspect of context analysis, helping organizations uncover hidden relationships and risks within their data.
Automated tools analyze data in context, providing deeper insights into potential risks. This capability is particularly valuable for identifying hidden vulnerabilities, such as data that could be exploited in a phishing attack or used to bypass security controls.
Context analysis ensures that organizations make informed decisions about how to protect their data.
Automated data discovery doesn’t operate in a vacuum. To be effective, it must integrate seamlessly with an organization’s existing security policies and frameworks. Policy integration ensures that data discovery tools enforce rules dynamically, reducing the need for manual intervention.
Compliance scanning is an essential feature of automated data discovery, helping organizations meet regulatory standards with ease. For example, if a policy requires that all sensitive data be encrypted, the automated tool can identify unencrypted files and take action to secure them.
This integration not only enhances security but also simplifies compliance with regulatory requirements. (Qohash’s solutions are designed to align with industry standards, making policy integration a seamless process.)
Identifying risks is only half the battle. To truly protect sensitive data, organizations need to address those risks immediately. Automated remediation capabilities enable tools to take action, such as encrypting, quarantining, or deleting sensitive data.
This automation saves time and resources compared to manual remediation efforts. It also ensures that risks are addressed consistently and effectively. For organizations dealing with high volumes of sensitive data, automated remediation is essential for maintaining continuous protection.
Speed is critical when it comes to data security. The longer sensitive data remains unprotected, the greater the risk of exposure. Automated data discovery drastically reduces the time required to identify sensitive information, enabling organizations to act quickly.
For instance, our Recon tool is able to process 50 GB of data per hour, ensuring that even the largest datasets can be scanned in a timely manner.
Human error is one of the leading causes of data breaches. Whether it’s misclassifying data, overlooking risks, or failing to enforce policies, manual processes are inherently prone to mistakes.
Automated data discovery eliminates these vulnerabilities by providing consistent, reliable results.
Removing this human element from data discovery can help organizations can significantly reduce their risk of exposure.
Data security isn’t a one-time effort — it’s an ongoing process. Automated data discovery enables continuous protection by monitoring data in real time and addressing risks as they arise. This proactive approach ensures that organizations stay ahead of evolving threats and maintain compliance with regulatory requirements.
Before implementing automated data discovery, organizations need to conduct an initial assessment of their data. This step helps identify high-risk areas and prioritize efforts during deployment.
Data mapping automation simplifies the process of visualizing where sensitive data resides across an organization’s systems. This initial assessment lays the foundation for a successful implementation.
Implementing automated data discovery doesn’t have to be an all-or-nothing effort. A phased deployment approach allows organizations to start with high-priority areas and gradually expand across the organization. This strategy minimizes disruptions and ensures that the tools are working effectively before scaling up.
Even the best tools need regular validation to ensure they’re functioning as expected. Continuous validation involves ongoing audits, updates, and adjustments to keep automated tools aligned with an organization’s needs.
In a world where sensitive data is constantly at risk, organizations need automated data discovery that can identify, classify, and protect their information with speed and precision.
Our Qostodian platform and Qostodian Recon are designed to meet these challenges head-on.
With features like real-time monitoring, automated remediation, and support for 350+ file extensions, Qohash empowers organizations to safeguard their most critical assets.
Whether you’re in financial services, healthcare, or the public sector, our solutions provide the confidence and control you need to navigate today’s complex data landscape. Request a demo to see the difference in secure data in your organization.
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