Organizations today generate, store, process and manage more data than ever before, since data is the backbone of the modern organization. As the volume of on-premises and cloud data continues to skyrocket, the risk and challenges of protecting it are also rising. Data needing protection can be broadly classified as structured and unstructured data, each with its own set of challenges and security requirements.
Structured vs Unstructured Data
On the one hand, structured data is highly organized, easily searchable, and often stored in databases. This data type follows a specific structured format, such as rows and columns in a database. Customer information, transaction records and inventory data are all examples of structured data, and it is easier to manage and analyze than unstructured data due to its predefined structure.
On the other hand, unstructured data lacks a specific format, schema or structure, making it harder to identify and more challenging to analyze, store and manage than structured data. Much of unstructured data includes emails, message text, attachments, metadata and communication threads. Unstructured data also includes text found in Word documents, PDFs or plain text files containing unorganized information, such as articles, reports and contracts. In addition, multimedia files including images, videos and audio files often contain vast amounts of information without a consistent format to protect. Finally, social media posts are another class of unstructured data, including text, image, video and metadata content from platforms like LinkedIn, Twitter, TikTok, Facebook and Instagram.
Unstructured Data Characteristics
The traits of unstructured data can be characterized by four “V” words. First there is volume, as unstructured data is exponentially growing due to digital communication, internet-connected devices and social media. Next the variety of unstructured data takes on numerous formats and types, making it difficult to manage and analyze. The rapid generation and sharing velocity of this data cause significant storage, processing and security challenges for unstructured data. Finally, the veracity of unstructured data with varying quality and accuracy requires investment in data validation and cleanup.
Risks and Challenges of Protecting Unstructured Data
The massive volumes of unstructured data create significant risks for organizations. Some of the main risks and challenges associated with unstructured data include:
- Data breaches – Unprotected or poorly managed unstructured data is vulnerable to cyber-attacks, which can result in data breaches and unauthorized disclosure of sensitive information. The lack of a consistent structure makes it difficult to apply uniform security measures to avoid these breaches.
- Compliance issues and risks – Compliance with data protection regulations, such as GDPR and CCPA, requires proper management, protection and auditing of unstructured data, including personal data.
- Storage and management concerns – The sheer volume and variety of unstructured data mentioned earlier can be taxing on an organization’s resources, requiring adequate storage, processing power, and efficient secure management practices.
- Identification and categorization challenges – Identifying and classifying sensitive unstructured data is difficult, labor-intensive and time-consuming.
- Limited access controls – Unstructured data often has minimal or inconsistent access controls, significantly increasing the risk of unauthorized access.
Due to many of the challenges discussed, unstructured data has become an attractive target for cybercriminals. Given the importance and potential risks associated with unstructured data, it is critical for organizations to invest in effective strategies and solutions to safeguard it.
Unstructured Data Protection Strategies
Whether structured or unstructured, there are three key components to a successful data protection strategy – identifying the data, classifying it, and remediating the risk.
Organizations need to be able to identify sources of unstructured data and classify and categorize them based on sensitivity. To reduce risk, organizations need to use role-based access controls and the least privilege access principles (for example, zero trust) to limit access to sensitive data. To protect data from unauthorized access, organizations should encrypt data in transit and at rest. And regularly monitoring and reviewing access logs and proactively addressing suspicious activities helps improve data security hygiene.
The best solutions for protecting unstructured data leverage AI and machine learning. AI-driven data classification speeds the process and accuracy of identifying and categorizing sensitive data, while AI-powered threat prevention and anomaly detection tools can detect and prevent threats in real-time, reducing the risk of data loss. In addition, machine learning algorithms are equipped to analyze user behavior and suggest appropriate access controls.
Protecting Both Types of Data With DSPM
To achieve comprehensive data protection across the board, organizations must adopt a unified approach that covers both structured and unstructured data. Effective data protection solutions should provide a holistic view of all data types, enabling organizations to implement consistent security policies and practices across their entire data landscape.
One popular, proven approach to this challenge is advanced data security posture management (DSPM). DSPM empowers organizations to discover structured and unstructured data and gain comprehensive visibility into where sensitive data resides and the types of sensitive data that exist. It also classifies data by tagging and labeling it. In addition, DSPM monitors and identifies risks by proactively detecting and assessing behavior and usage of business-critical data, preventing potential breaches before they occur. Finally, DSPM remediates and protects sensitive information against unauthorized access and data loss.
With DSPM, as sensitive structured and unstructured data moves through the network and across data stores, it is labeled appropriately no matter where it resides. It is then monitored for risks, such as inappropriate permissions, risky sharing, inaccurate entitlements, and wrong location. If any risks are detected, they can be remediated.
Understanding the differences between structured and unstructured data is crucial for implementing effective holistic data protection strategies. Organizations must recognize the unique challenges posed by unstructured data and adopt advanced solutions that leverage AI and machine learning to safeguard all types of data, such as advanced DSPM. By doing so, they can mitigate risks, ensure compliance and derive valuable insights to drive growth and innovation.
