It’s easiest to grasp the concept of unstructured data by first defining structured data. IBM defines structured data as data that “has a fixed schema and fits neatly into rows and columns, such as names and phone numbers.”1 Unstructured data “has no fixed scheme” and—when compared to structured data—has a more complex format.1 Unstructured data may include audio files, web pages, emails, social media posts, images files, and more, and its datasets are typically high-volume—comprising “90% of all enterprise-generated data.”1The nature of unstructured data makes it difficult to analyze and even more difficult to secure. Organizations lack visibility into it, and it can sprawl as users generate more of it. It introduces security risks like compliance violations and breaches if a business has no proactive approach to managing it. That’s where data security posture management (DSPM) comes in. Read on to learn how to secure unstructured data across your environment for a stronger security posture.Understanding Core Differences: Structured Data vs Unstructured Data Structured Data Unstructured DataDefinition Data that is organized and formatted in specific way following pre-defined model or schema Data that lacks structure or format, typically unorganized and rawData Format Predefined Variable Storage Database Data stores, data warehouses ExampleCustomer info, financial records or inventory Email, multimedia files, resumes, financial forms, medical reports and social postUsecase Data analytics, BI, reporting, MLSentiment analysis, ML, text mining, NLP, GenAISecurity challengeData schema changes, integrating different structured data sourcesStorage volume. analysis issueComplexity Easy to manipulate and analyzeComplex, requires special tools to analyze What makes unstructured data a security risk?There are four main aspects to unstructured data that organizations need to consider in their security strategies:Unmanaged and invisible data sprawlUnstructured data can sprawl across an organization—containing sensitive information and foiling typical data management practices. Consider a situation where a customer plugs their personally identifiable information (PII) into a chatbot conversation, and that conversation gets saved in the form of textual unstructured data. The business then may store it in a data lake and fail to apply consistent access controls. Now replicate this scenario every hour. It’s a recipe for sprawling unstructured data that puts a business at risk.Overprivileged access and insider threatsIt’s common for organizations to unintentionally give employees more access than they need to unstructured data. Perhaps a business has an open access policy where employees can broadly access data, or maybe there is a lack of proper data governance. The more unstructured data you accrue, the harder it is to apply governance and the right access controls. With loose access controls, organizations risk unauthorized access to unstructured data and compliance violations from the inside out.Compliance challenges and regulatory risksFailure to secure data that contains PII or financial data may lead to regulatory fines from multiple bodies. If your organization has to comply with the General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), the Payment Card Industry Data Security Standard (PCI DSS), the California Consumer Privacy Act (CCPA) or the myriad other applicable regulations, you need strong protections for unstructured data.Data oversharing with AI toolsAI tools are undeniably powerful tools that enhance business productivity. The more high quality data it processes and trains on, the better it performs. But, the risks associated with oversharing of data with AI cannot be overlooked. The consequences of uncontrolled sharing of data with AI ecosystems can be dire, with the risk ranging from data exposure, data breaches to the creation of malicious content and compliance violation leading to financial and reputational damage. To learn more about AI driven data risk, read blog: Uncovering AI Toxic Combinations – How to defend your data from Agentic AI and RAG Common attack vectors targeting unstructured dataCybercriminals are increasingly targeting unstructured data and finding it lucrative to exploit through the following vectors:Ransomware and data exfiltrationsAttackers may steal files stored in cloud drives, emails, or collaboration platforms. They lock you out of your files with ransomware and perform data exfiltration to transfer sensitive data to other servers.Phishing and business email compromise (BEC)Successful phishing schemes trick employees into revealing credentials—granting cybercriminals access to unstructured data. BEC attacks often involve bad actors impersonating trusted contacts or business leaders to get employees to transfer funds or share confidential information.Shadow IT and unauthorized data sharingWhen employees use personal cloud storage or unapproved apps to interact with unstructured data, they put that data at risk. Unauthorized collaboration tools operate outside a company’s security policies and expose unstructured data to potential breaches. How DSPM helps secure unstructured data?DSPM is the modern approach capable of securing unstructured data. Here’s how it works:Unified, automated data discovery, classification and inventoryThe right DSPM solution safeguards both structured and unstructured data across multiple locations, cloud environments, and private applications. It starts by automating the data discovery and classification process—continuously scanning cloud storage, email and collaboration tools to locate unstructured data. Once located, it classifies the data, which reduces blind spots caused by shadow IT and data sprawl. With an automated AI powered data classification DSPM capability, security teams can identify and categorize their unstructured data (What type of data is stored, where it is stored, who has access to the data and how it is managed) with high accuracy and then devise strategies to protect it. Access governance and least privilege enforcementDSPM provides granular, risk-based, user-centric view of all access paths to mission-critical data and configurations. DSPM detects unnecessary permissions, flags high-risk users, and integrates with zero trust to automatically restrict access based on the principle of least-privilege. Only the users who need access to data get that access, and it is not granted in a blanketed way—revoking permissions to limit data exposure.Proactive risk remediationDSPM minimizes risk using advanced risk correlation that determines hidden attack paths and access to data. It helps to filter out the noise and prioritize incidents by risk and severity through in-depth analysis of all risky access to sensitive data. With near to real-time alerts flagging unauthorized access, security teams can take proactive steps to remediate overprivileged users and risky access paths to sensitive data, thus preventing breaches and enforcing compliance. Steps to reduce unstructured data risks in your organizationHere are five sequential steps you can take today to reduce the risk posed by unstructured data in your business:Identify and classify unstructured data: Use DSPM to automate the process of locating sensitive data. In addition leverage AI powered data classification and custom tools to identify and classify specific sensitive data important from your industry perspective across all your environments. Enforce strict access controls: Choose a DSPM offering that integrates with zero trust models to apply least privilege access.Implement real-time monitoring and anomaly detection: Use DSPM to detect suspicious data movement or access attempts.Choose secure collaboration tools and cloud storage: Restrict unauthorized data sharing and shadow IT use by preventing employees from using personal cloud storage and apps.Automate compliance enforcement: Implement policies that align unstructured data security with GDPR, HIPAA, PCI DSS, and CCPA mandates. Why you should take action to secure unstructured data now?Unstructured data is becoming a more attractive source for exploitation as it sprawls across business environments. Chances are, your business is dealing with significant volumes of unstructured data. Implement DSPM today to get control over this blind spot in your data landscape. DSPM from Zscaler provides a scalable, automated way to identify, secure, and manage unstructured data. Request a demo of our DSPM offering today.Sources:IBM, “Structured vs. unstructured data: What’s the difference?,” February 7, 2025, https://www.ibm.com/think/topics/structured-vs-unstructured-data
[#item_full_content] It’s easiest to grasp the concept of unstructured data by first defining structured data. IBM defines structured data as data that “has a fixed schema and fits neatly into rows and columns, such as names and phone numbers.”1 Unstructured data “has no fixed scheme” and—when compared to structured data—has a more complex format.1 Unstructured data may include audio files, web pages, emails, social media posts, images files, and more, and its datasets are typically high-volume—comprising “90% of all enterprise-generated data.”1The nature of unstructured data makes it difficult to analyze and even more difficult to secure. Organizations lack visibility into it, and it can sprawl as users generate more of it. It introduces security risks like compliance violations and breaches if a business has no proactive approach to managing it. That’s where data security posture management (DSPM) comes in. Read on to learn how to secure unstructured data across your environment for a stronger security posture.Understanding Core Differences: Structured Data vs Unstructured Data Structured Data Unstructured DataDefinition Data that is organized and formatted in specific way following pre-defined model or schema Data that lacks structure or format, typically unorganized and rawData Format Predefined Variable Storage Database Data stores, data warehouses ExampleCustomer info, financial records or inventory Email, multimedia files, resumes, financial forms, medical reports and social postUsecase Data analytics, BI, reporting, MLSentiment analysis, ML, text mining, NLP, GenAISecurity challengeData schema changes, integrating different structured data sourcesStorage volume. analysis issueComplexity Easy to manipulate and analyzeComplex, requires special tools to analyze What makes unstructured data a security risk?There are four main aspects to unstructured data that organizations need to consider in their security strategies:Unmanaged and invisible data sprawlUnstructured data can sprawl across an organization—containing sensitive information and foiling typical data management practices. Consider a situation where a customer plugs their personally identifiable information (PII) into a chatbot conversation, and that conversation gets saved in the form of textual unstructured data. The business then may store it in a data lake and fail to apply consistent access controls. Now replicate this scenario every hour. It’s a recipe for sprawling unstructured data that puts a business at risk.Overprivileged access and insider threatsIt’s common for organizations to unintentionally give employees more access than they need to unstructured data. Perhaps a business has an open access policy where employees can broadly access data, or maybe there is a lack of proper data governance. The more unstructured data you accrue, the harder it is to apply governance and the right access controls. With loose access controls, organizations risk unauthorized access to unstructured data and compliance violations from the inside out.Compliance challenges and regulatory risksFailure to secure data that contains PII or financial data may lead to regulatory fines from multiple bodies. If your organization has to comply with the General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), the Payment Card Industry Data Security Standard (PCI DSS), the California Consumer Privacy Act (CCPA) or the myriad other applicable regulations, you need strong protections for unstructured data.Data oversharing with AI toolsAI tools are undeniably powerful tools that enhance business productivity. The more high quality data it processes and trains on, the better it performs. But, the risks associated with oversharing of data with AI cannot be overlooked. The consequences of uncontrolled sharing of data with AI ecosystems can be dire, with the risk ranging from data exposure, data breaches to the creation of malicious content and compliance violation leading to financial and reputational damage. To learn more about AI driven data risk, read blog: Uncovering AI Toxic Combinations – How to defend your data from Agentic AI and RAG Common attack vectors targeting unstructured dataCybercriminals are increasingly targeting unstructured data and finding it lucrative to exploit through the following vectors:Ransomware and data exfiltrationsAttackers may steal files stored in cloud drives, emails, or collaboration platforms. They lock you out of your files with ransomware and perform data exfiltration to transfer sensitive data to other servers.Phishing and business email compromise (BEC)Successful phishing schemes trick employees into revealing credentials—granting cybercriminals access to unstructured data. BEC attacks often involve bad actors impersonating trusted contacts or business leaders to get employees to transfer funds or share confidential information.Shadow IT and unauthorized data sharingWhen employees use personal cloud storage or unapproved apps to interact with unstructured data, they put that data at risk. Unauthorized collaboration tools operate outside a company’s security policies and expose unstructured data to potential breaches. How DSPM helps secure unstructured data?DSPM is the modern approach capable of securing unstructured data. Here’s how it works:Unified, automated data discovery, classification and inventoryThe right DSPM solution safeguards both structured and unstructured data across multiple locations, cloud environments, and private applications. It starts by automating the data discovery and classification process—continuously scanning cloud storage, email and collaboration tools to locate unstructured data. Once located, it classifies the data, which reduces blind spots caused by shadow IT and data sprawl. With an automated AI powered data classification DSPM capability, security teams can identify and categorize their unstructured data (What type of data is stored, where it is stored, who has access to the data and how it is managed) with high accuracy and then devise strategies to protect it. Access governance and least privilege enforcementDSPM provides granular, risk-based, user-centric view of all access paths to mission-critical data and configurations. DSPM detects unnecessary permissions, flags high-risk users, and integrates with zero trust to automatically restrict access based on the principle of least-privilege. Only the users who need access to data get that access, and it is not granted in a blanketed way—revoking permissions to limit data exposure.Proactive risk remediationDSPM minimizes risk using advanced risk correlation that determines hidden attack paths and access to data. It helps to filter out the noise and prioritize incidents by risk and severity through in-depth analysis of all risky access to sensitive data. With near to real-time alerts flagging unauthorized access, security teams can take proactive steps to remediate overprivileged users and risky access paths to sensitive data, thus preventing breaches and enforcing compliance. Steps to reduce unstructured data risks in your organizationHere are five sequential steps you can take today to reduce the risk posed by unstructured data in your business:Identify and classify unstructured data: Use DSPM to automate the process of locating sensitive data. In addition leverage AI powered data classification and custom tools to identify and classify specific sensitive data important from your industry perspective across all your environments. Enforce strict access controls: Choose a DSPM offering that integrates with zero trust models to apply least privilege access.Implement real-time monitoring and anomaly detection: Use DSPM to detect suspicious data movement or access attempts.Choose secure collaboration tools and cloud storage: Restrict unauthorized data sharing and shadow IT use by preventing employees from using personal cloud storage and apps.Automate compliance enforcement: Implement policies that align unstructured data security with GDPR, HIPAA, PCI DSS, and CCPA mandates. Why you should take action to secure unstructured data now?Unstructured data is becoming a more attractive source for exploitation as it sprawls across business environments. Chances are, your business is dealing with significant volumes of unstructured data. Implement DSPM today to get control over this blind spot in your data landscape. DSPM from Zscaler provides a scalable, automated way to identify, secure, and manage unstructured data. Request a demo of our DSPM offering today.Sources:IBM, “Structured vs. unstructured data: What’s the difference?,” February 7, 2025, https://www.ibm.com/think/topics/structured-vs-unstructured-data
