Data Classification: How SaaSOps Managers can Secure Organizational Data


Data classification is the method of categorizing data according to its sensitivity levels in order to protect it efficiently and effectively.

Data classification is a critical step in ensuring the effective management of information security and risk management systems.

The primary goal of data classification is to regulate the classification, use, disclosure, and security of the company's data, as well as that of its data subjects, in accordance with the needs, contractual as well as regulatory requirements.

It is critical to have a data classification scheme in place to ensure that sensitive information is handled appropriately by the employees. Maintaining data management throughout its entire lifecycle – from the time it is created until it is properly destroyed at the end of its life – is essential for the success of any business. 

The levels of confidentiality, sensitivity, or secrecy required for data determines its classification. 

Once data is classified, you can then implement appropriate levels of information security controls based on the data's value and risk levels. Classification of data distinguishes low impact data from high impact data.

The different levels determine whenever you share data, whether within or outside an organization; you must consider the following factors: what type of data you are sharing, what classification the data has, and the impact of data sharing on the business.

An appropriate method for protecting data can be devised properly once the data has been properly classified.

Data Classification helps Organizations understand: 

  • Different types of data that is available

  • When and where specific data is stored 

  • Access, integrity, and protection levels that are required 

  • If current privacy and security measures are in compliance with the applicable laws and regulations 

4 Major Categories of Data Classification

4 Major Categories of Data Classification

1. Restricted Data (High Risk)

Leakage, modification, or loss of restricted data poses a serious risk to the business. Restricted data should be subjected to the strictest security controls possible. 

Examples include trade secrets, sensitive personal information, health records, etc.

2. Confidential Data (High Risk)

Disclosure, modification, or loss of confidential data could pose a significant risk to the business. Confidential data should be subjected to stringent security controls. Confidential data is accessible to CXOs and top-level management.

By default, all customer data should be classified as confidential unless the data owner specifies otherwise.

High-risk data necessitates the implementation of stringent access controls and protections, not only because it is frequently protected by regulations such as GDPR, HIPAA, and CCPA, but also because it has the potential to cause significant harm to an individual or organization it belongs to if compromised. 

Examples include client information, audit reports, passwords, payroll records, and personal information, to name a few.

3. Private or Internal Data (Medium Risk)

Disclosure, modification, or loss of internal data poses a significantly lesser risk to the business as compared to high-risk data. This type of data should be subjected to a moderate level of security controls. 

For instance, non-identifiable employee data, internal telephone directories, policies, and so forth. 

4. Public Data (Low risk/ No Risk)

Disclosure, modification, or loss of public data could pose few or no risks to the business. As a result, public data is subjected to minimal security controls. Low-risk data is information that is available to the public and does not require any access restrictions, such as job postings, information on public web pages, and contact information for businesses.

Media releases published reports, and business cards are some examples of public data.  

Approaches to Data Classification


This approach delves deeper and analyses data based on the file's content to identify sensitive, personal, and confidential data and then assigns the appropriate classification label.


This approach considers variables such as application, location of data creation, or creator of the data as metrics of sensitive data. 


The user determines how the data should be classified manually. User-based classification is highly dependent on the employee's judgment and knowledge of the data.

How To Classify Sensitive Data

Data classification can not only help businesses improve security but also their efficiency and customer service. To start with, businesses should determine the categories and criteria for data classification, understand and define their goals, outline the roles and responsibilities of the employees in maintaining proper data classification strategies, and implement security protocols that correspond to data categories and labels in order to streamline the process. The proper implementation and execution of data classification will provide an operational scheme for data processors (employees as well as third-party contractors).

Let’s have a look at the step by step process to go about data classification:

  1. Discover and Identify data. Discover location and accessibility and identify data owners and their responsibilities. The security team must define the appropriate access controls for each classification level. 

  2. Specify the evaluation criteria for data classification and label how data will be classified and labeled. The protocols for each level of data will be different; for example, you would be much more careful with confidential data than public data. Data policy must define the labels that will be used for data, how the data will be stored, and how it will be disposed of. 

  3. Classify and label your data. Data should be labeled appropriately prior to its sharing in order to determine the sensitivity level of the data. 

Each data level will have its own set of controls defined by the IT security team, including public, internal, confidential, and restricted labels. 

It is recommended that when classifying data, you create different levels, with each level allowing end-users or internal employees to have access to different methods of control and requirements.

Data must be labeled as public, internal use, confidential, or restricted prior to sharing. Data can be grouped by how valuable it is to the organization, how sensitive it is, or how valuable it is. 

  1. Document any exceptions to the classification policy that are discovered and integrate them into the evaluation criteria.

  2. Select security control on the basis of classification level to provide the necessary level of protection. Once the data has been properly organized, you can determine the most likely threats and risks associated with that data. If you do business in the United States, you are required by law to protect personally identifiable information (PII) about employees and customers. Employ security control and protection measures (right security and protective measures throughout data's life cycle).

It is important to place controls appropriately based on the sensitivity or the criticality of the data in order to secure data that is sensitive and not waste valuable resources on securing non-sensitive data.

  1. Specify the procedures for declassifying resources and the procedures for transferring custody of a resource to an external entity (3rd party access and security level for that)

  2. Create an enterprise-wide awareness program to instruct all personnel about the classification system (and what will happen if they don’t follow the procedure)

  3. Continuous monitoring is critical to ensure that you are auditing access to sensitive data on a continuous basis to ensure that only authorized individuals are gaining access to the sensitive data. For the purpose of avoiding "creep," which occurs when an individual is able to elevate their privileges over time to the point where they have access to more privileges than they should have access to. Ideal monitoring involves appropriate measures and evolving security practices.

Securing Data Throughout its Lifecycle
Securing data throughout its lifecycle has numerous benefits, including:  

Data Classification Best Practices -1
  • Risk assessment and governance 

  • Assisting in the fulfillment of legal discovery requirements 

  • Facilitating the achievement of regulatory compliance requirements 

  • Increased efficiency in security investments

Data Classification Best Practices:

Data Classification Best Practices

Setup a Data governance Council

The data governance council develops procedures and guidelines for the collection, storage, use, and safekeeping of data and also reviews and updates the data policy as needed. Additionally, the data governance council is responsible for the appropriate response actions in the event of a violation of the data policy or a security breach. 

Make a Data Catalog or inventory.

The data catalog or inventory is a record of data processing activities or, in simple terms, what the data processor has done with the data. 

Other things to look for in a data inventory are information about the types of data that are processed, where and how the data is processed (such as making changes or deleting them), as well as why these actions are done.

Do not forget to re-evaluate the data.

Data should be re-evaluated on a regular basis. The data profile is not constant and can vary over its lifecycle, depending on business requirements or regulatory changes. Data that was once classified public can become confidential if policy changes. 

Data Classification is Not Optional in the SaaS World

  • It is critical that all data is classified so that it can be appropriately protected.

  • In order to establish a baseline for the minimum level of security for the information and the information systems on which data resides, it is necessary to categorize the data. 

  • Classifying data will help you determine how much time and money you should devote to protecting and controlling access to it. For example, you will spend much more time and effort protecting data classified as high risk than you would protect data classified as low risk.

  • It is not a good idea to treat all data as equal when designing a security system. Sensitive data becomes easily accessible if all data is classified and protected as 'low-risk' data. Whereas classifying all data as 'high-risk' would not only be expensive, it also restricts easy access to non-critical data.

Data classification is essential in order to secure data and comply with different regulations. A solid foundation for your data security strategy can be built by determining where your sensitive and regulated data is stored, both on-premises and in the cloud. 

Furthermore, data classification improves user productivity and decision-making while simultaneously lowering storage and maintenance costs by allowing you to eliminate redundant and unnecessary data.


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SaaS Management: 3 Key Challenges

A Framework to Eliminate SaaS Wastage

SaaS Vendor Management in 2022: The Definitive Guide

Symptoms of an Unoptimized SaaS Stack (+ Solutions)


The Ideal Cost Optimization Playbook to Control SaaS Spend

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SaaS Management: 3 Key Challenges

With this explosion of SaaS at companies, there arise SaaS challenges caused by apps getting out of your control. These SaaS challenges varies in three dimension: spend management, security and complance risks, and various SaaS operations tasks like automating SaaS procurments, renewals, employees onboarding and offboarding.

A Framework to Eliminate SaaS Wastage

‘Muda’ is used to describe any activity that uses resources but doesn't generate value. It is the Toyota system for identifying and eliminating waste in all forms. It is the same thing that helps Toyota sell more cars than Ford, General Motors, and Honda at a higher margin.

SaaS Vendor Management in 2022: The Definitive Guide

An obese SaaS stack leads to SaaS wastage. It's a disease! It not only causes financial issues but also gives you security and compliance problems. That's why you must keep tight control on your SaaS stack. And it begins with managing your SaaS vendors. 

Symptoms of an Unoptimized SaaS Stack (+ Solutions)

In this post, we've discussed 7 symptoms of an unoptimized SaaS stack and solutions to optimize the same.

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