• Mark van Vuuren

Sensitive Data Discovery and Classification Explained

Sensitive data is any type of classified information that must be protected and made inaccessible to parties without the proper authorization. This type of data includes personally identifiable information (PII) and protected health information (PHI), among others. Both ethical and legal reasons warrant the need to have strict restrictions for access to these types of data. Specifically, the inherent risk of cyber threats and breaches puts greater emphasis on identifying sensitive data and classifying the information therein.

At its core, data classification tags data based on its type, sensitivity, and value it provides to the organization in question. It helps an organization identify the value of its data, what risk said data is exposed to, and implement needed controls to mitigate identified risk. Besides, data classification helps an organization stay compliant with various relevant industry-specific regulations, including SOX, PCI DSS, GDPR, and HIPPA.

The Four Classes of Data Sensitivity

Data is classified depending on its sensitivity levels. This sensitivity can be classified into different types determined by the federal regulations as mandated by the security control units. As such, sensitive data can be classified into four types including:

Low Data Sensitivity Type

Low data sensitivity type encompasses data that poses little to no risk to the organization. Data within this class can be accessed by anyone as there are little or no restrictions to its accessibility. In hindsight, this is public information that can be discussed anywhere by any party. For an organization, data within this class include any information on the organization already in the public domain. This could consist of unpublished findings, information on founders, niches, and leadership.

Moderate Data Sensitivity Type

Data within this class is subject to contractual agreements by either party interested in the information. Notably, leakage of such data will often only cause minimal damage to the organization. Examples of data that fall under this category include IT service information, employee contact information, and documentation on intellectual property.

High Data Sensitivity Type

Data within this classification are private and should be kept confidential. A breach of this data set could cause significant damage to the organization, including exposure to criminal liability and cyber-attacks. Besides, a breach could significantly threaten business continuity. Examples of this data type include IT security information, controlled unclassified information, PHI, and PII.

Restricted Type of Sensitive Data

Data within this class is considered to be highly sensitive and will often attract an NDA. Examples of restricted, sensitive data include industry-specific data, trade secrets, and clients’ payment details. A breach of this type of sensitive data could, in effect, lead to the complete closure of business and legal ramifications with untold financial costs.

Importance of Data Indexing and Classification

As discussed, data classification involves placing data within the four data classes depending on their level of sensitivity. On the other hand, data indexing helps show the relationship between the data found and the classes available, thus structuring all the data. The importance of data indexing and classification include:

Classification Provides Context to the Data

Chances are, as an IT professional, you have questioned the need to classify data when the IT systems can fully index all the content. However, you should be aware that simply inputting the data does not provide context.

During classification, you can segregate the data and assign value to it. Note that assigning of value is subjective at best and relies significantly on the goal of the classification. This should ensure that the organization’s data is narrowed down, which improves its searchability.

Classification Stipulates Retention and Disposition of Data

Popular sentiment regarding data storage is that when storage is cheap, why to bother deleting data. This way of thinking is further propelled by the idea that portable storage is now cheap, sturdy, and reliable. Still, a clear decision is needed as it pertains to data retention or disposition.

Classification helps create an organization’s policy as it pertains to retention and disposition schedules. This should see all data types subjected to scrutiny to determine if it remains relevant or if its disposition is paramount.

Classification Is in Adherence with Data Protection Regulations

Data privacy continues to be an issue of concern for regulators and IT specialists in equal measure. The issue has given rise to data protection regulations, like the GDPR, primarily focused on privacy.

Classification can help with creating categories of data that require more robust protection. It gets rid of any sorted data that is obsolete, redundant, or trivial, thus further improving security measures in place. Besides, classification ensures that your security team focuses its energies on streamlining data security operations instead of discovering and classifying sensitive data.

Classification Makes Your Data More Accessible

Data classification determines how accessible and valuable your data truly is. With the enormous amounts of data at the disposal of your IT professionals, you can end up having lots of unstructured data. As such, you want to implement a classification strategy that should see you sort through the different data available to you, thus ensuring you can gain valuable insights from what you already have.

Organizations are growing increasingly reliant on data. As this data moves across the different levels of the organization, you want a partner that will offer you sensitive data discovery, data access and permission auditing, data activity monitoring, data file tagging, and breach response. This is particularly important with the rampant data security threats currently experienced by organizations.

The AI-based Inventa™ platform from provides a network-based perpetual, scalable, and pinpoint-accurate data discovery and classification solution with automated, near real-time discovery, classification, and cataloging of all sensitive data – both structured and unstructured – at the enterprise scale. The Inventa platform takes a zero-trust approach to data discovery and doesn’t rely on input from your organizational players.

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