Data Classification for your Business: Why you need to prioritize it

With increased reliance on cloud services, your data is not locked behind the walls of your company anymore. The nature of connecting users, suppliers, and business partners today with your company generates a huge amount of data. How are you ensuring that critical data is protected without having to protect ALL data?

Data classification will help you develop an understanding of your company's information assets, then categorize the assets to safeguard data and comply with the information security policies, regulations, compliance obligations, and laws. You can do this either by hiring workers to apply labels based on predefined policies manually or automate the whole process.

Say you have limited resources to invest in safeguarding your data; knowing precisely what data needs protection will let you prioritize and come up with a sound plan. Then wisely allocate your budget and other resources, minimizing compliance and security costs. In my conversations with our VP of Analytics and Security, he spoke about how data classification using Machine Learning and NLP for our clients proved to be a solid foundation for a data security strategy. It helped identify the risky areas in the IT network, both on-premises and in the cloud.

The rapid shift to remote work at the peak of the pandemic left many company's exposed and highlighted various data and network vulnerabilities. As more and more companies moved to facilitate a remote work environment, data security ecosystems have evolved to become more complex. Advanced data management and classification solutions have become a critical technology investment. A security culture needs to be embedded within your organization.

So what's the best place to start?

Data classification solutions will help you protect data by putting the right security labels in place. This is a solid foundation to build on.

How to implement an effective classification strategy?

Each business will have different data classification needs to address, so a strategy will have to be tailored based on that. We begin by understanding what your objectives, goals, and strategic intent are. In this process of organizing data by agreed-on categories, a thoroughly planned classification allows for more protection and efficient use of critical data across the company and contributes to legal discovery, risk management, and compliance processes.

  • Adding labels, classifying data, and enforcing policies will help your company meet regulatory requirements and legal compliance.

  • You can begin to understand who shouldn't or should have access to it both within and outside of your company by understanding the sensitivity of different data.

  • Data classification is a step to make sure employees become more aware of the kind of data they are working with and its value, as well as their obligation in protecting it to prevent compromise of intellectual property or data loss.

  • Data classification brings security to the forefront of your company by empowering and educating you of the data you house. Most data leaks can be avoided when a data security strategy is in place. Adding visual labels to footers and headers raises end-user awareness and assists the users in becoming more security-focused and to avoid sharing sensitive content via email, USB drives, or cloud services like Dropbox or Box.

  • Here's how data classification will help you meet common compliance standards: HIPAA - For proper data protection, knowing where all health records are stored will help you implement security controls.
    GDPR - Including satisfying data subject access requests by retrieving the set of documents with data about a given individual, data classification helps you uphold the rights of data subjects.
    ISO 27001 - Classify information based on sensitivity and value. This helps meet requirements for the prevention of unauthorized modification or disclosure.
    PCI DSS - Data classification lets you secure and identifies consumer financial information used in payment cards.
    NIST SP 800-53 - Categorizing data helps federal agencies properly manage and architect their IT systems.

Types of Data Classification

User-based classification is the manual selection of every document by an employee.
Context-based classification looks at location, application, creator tags, and other such variables as indirect indicators of sensitive information.
Content-based classification interprets and inspects files to identify sensitive information.

Examples of Data Classification Categories

Public
Personal
Confidential
Sensitive
Sensitive data is a general term meant to represent data restricted to use by specific groups or specific people. Confidential and Sensitive data are often used interchangeably. Ex's of Sensitive data include trade secrets and intellectual property.

Today you have the option for automated classification. Data classification was purely a user-driven process for years. You can set processes that enable users to classify the documents they send, create, modify or otherwise touch. If you prefer, you can leave older data to gradually be retired without being classified. You can classify the backlog of existing data, alternatively, using data discovery. The idea is that unstructured data like paragraphs of text can be categorized in a system so that you don't need data entry workers to categorize your data manually. There's only the cost of setup, and although you can only get to a certain accuracy percentage, the cost savings for our clients have been significant long-term.

1. Automated classification of data will improve accessibility

There exists heaps of valuable business information within the volumes of content your firm creates every day. And at best, lots of data that is taking up space, and skews insights, and introduces errors at worst. Due to the fact that it is obsolete, redundant, or trivial, at least a third of enterprise data is useless. It would also be quite impossible to perform the level of detail-oriented cross-checking that would be necessary to identify and eliminate ROT manually since it tends to be embedded within all organizational data.

However, with training data scouting within many data sets to find then filter ROT becomes an automatable task that can be performed with a high degree of accuracy. Machines can identify and weed out ROT as part of the progressive classification of data process and improve the accessibility of high-quality data.

2. Automated classification of data fuels productivity and ROI

Data workers spend most of their time discovering and preparing data – and even then, most companies still don't make a sizeable enough dent in their volumes of unstructured information. Considering how fast new volumes of data are being generated, you will have to account for the additional salaries to employ more people to manually keep up with the demand.

Take unstructured data in the form of text, for instance. It is everywhere: emails, chats, social media, support tickets, web pages, survey responses, and more. Extracting insights from text, although it can be an extremely rich source of information, can be time-consuming and hard due to its unstructured nature. Text classification using Machine Learning can help you automatically analyze and structure your text cost-effectively and quickly. Natural language processing (NLP) is a field within machine learning and artificial intelligence that combines computer science and linguistics to break down language, so it can be analyzed by machines.

Getting started with the classification of data requires understanding your company's security needs and data compliance. Keep these in mind when you are ready to start classifying your data:

  • Don't try to classify everything right from the start.
  • Keep the process of data classification simple for both the data custodians and users.
  • Partner with data owners to first focus on the most highly sensitive, business-critical assets and systems.

Securing data is a growing challenge. Incremental steps are key to a classified and organized data model. Data classification will provide a clear picture of the data within your company's control and an understanding of where the data is stored, how to easily access it, and how to best protect your data from potential security risks.

Today businesses of all sizes handle more data than they can keep track of. This data could include invoice records, customer payment information, order history, user data in software, email lists, and so many other pieces of information. You must keep this data secure and organized but also accessible when your employees need it. Good data classification is a solid foundation for keeping your organization’s data accessible, organized and useful. While there are various methods to classify data, most businesses classify it based on internal/confidential or external/public information. Especially once regulatory compliance comes into the picture, internal data can occupy various levels of confidentiality. Pick the classification system that works best for the data, and work to make it remain secure. The topic of data classification is part of the conversation about hackers and cybersecurity. And it addresses the external threats the data may be vulnerable to in most cases. But company data employees have access to can be just as vulnerable. And so, to lower the risk of this information falling into the wrong hands, companies must implement data-access strategies. Data classification is the foundation of all of this.

Should you wish to know more about Data Classification and how our solutions differ, you can reach us here.

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