What Executives Need to Know About Data Risk Classification

We’ve all heard the quote, “data is the new oil.” Whether or not you agree with the sentiment is up to you, but the facts are clear: data is big business, has huge importance, and is coming in at an increasingly unfathomable rate. In fact, at the dawn of 2020, an estimated 44 zettabytes of data existed in our world. Just to give you context, on a byte-per-star basis, that’s 40 times more than the number of stars in our observable universe. And the pace at which data is collected also continues to grow, with 2.5 quintillion bytes of data created every day. That’s A LOT of data coming from a variety of sources— social media, e-commerce, content form fills, IoT devices, streaming services — and the list goes on. Because of the velocity and volume of data, executives need to understand the principles of data classification, data fusion, and adopt strategies to effectively manage and secure the information.

What is Data Risk Classification?

A simple definition of data risk classification is the process of organizing data into relevant categories so that it can be used and protected. In addition, data classification includes tagging data to make it searchable and trackable while also reducing duplication.

There are three industry-standard types of data risk classifications:

  • Content: does the content of the data contain sensitive information?
  • Context: using the data creator, location, and application to determine if the data contains sensitive information
  • User-based: the end-user reviews and edits the data, flagging sensitive information

Data risk classification is important because it helps determine who can access the data and how it may be used, and identifies the “data risk” associated with each classification. Data's risk can be classified as:

  • Low risk data - data that is intended or approved to be public information/knowledge. It is a low security risk because the data's contents do not have any adverse effects.
  • Moderate risk data - data that is not general public knowledge. It is restricted data that includes some security risk to a companies mission, safety, finances or services if disclosed.
  • High risk data - high risk information that is protected by law or regulations. Could have a significant negative impact on a companies mission, safety, finances or services. Example: protected health information (PHI) or classified financial information. 

While one might think this is an IT responsibility, it’s not. Rather, it’s an executive’s role to develop the strategy around risk classification and the rules surrounding the risk. Who can access the servers and for what applications are two questions to consider. As an example, if the data is high risk, such as protected health information, financial information, employee data, or privileged intellectual property, security and rules must be established for who can access this data, and the consequences should unauthorized access occur. Strict rules and security protocols should be defined prior to handling high risk information. The same applies to data deemed as medium sensitivity, such as email files or internal communications. While a breach here might be embarrassing or uncomfortable, it doesn’t carry the same legal consequences as highly sensitive data. And finally, low sensitivity data— the information you want the public to have, like press releases or sales collateral. Again, as an executive, it’s your responsibility to develop the strategy and policies around data classification and risk. The data owners (IT, Marketing, etc.) are accountable for the proper handling and execution.

How Data Fusion Improves Security

Obviously, data risk classification is important, but it can also be cumbersome and complex. And, when we start talking about different data owners, applications and data collection points, consistency in data risk classification becomes even more essential to business success. Enter the role of data fusion.

Data doesn’t live in a silo. Data fusion integrates data from multiple sources to produce more accurate and consistent information across an organization. Data fusion relies on entity resolution analytics to compose pre-integrated objects, providing an executive with a complete sense of the information available, thereby enabling better business decisions.

The best data fusion models are AI-based, which improves the efficiency and security of data risk classification. Sorting fused entities becomes more effective when all data features are available within more complete data objects, eliminating the problem of constructing complex queries and categorizations across multiple, disparate data sets. Unfortunately, the majority of data management services today do not implement data fusion, missing the opportunity to provide executives with a more complete and constructed view of the data landscape and placing more work on the data analyst. The task of extraction, correlation, categorization, and disambiguation across billions of records from thousands of sources is precisely what AI can solve. Additionally, AI has the ability to generate analytic models with much greater accuracy.

Historically, data fusion models have been plagued with difficulties and errors in resolving join-attributes across sources while also consolidating information into a data warehouse. The primary challenges have been accurately joining data from such diverse sources. Additionally, many of the traditional tools and services (Python and Notebooks) require coding and are not suited for business executives. As an answer to these challenges, BOSS has built a low/no code solution, to simplify data access, ingestion, and harmonization.

The bottom line is that data fusion improves risk classification accuracy by lowering the amount of data filled with synthetic values, aggregating away resolution in the data, or dropping data due to empty values. In other words, data fusion helps create more complete data objects, which leads to more accurate and complete analytic results.

Verifying Risk Classifications Strategies with Data Authentication

Once an organization has data risk classification and data fusion practices in place, the data plan doesn’t stop there. Now, it’s time to verify employees are using the data appropriately and following the policies put into place.

Data authentication is the process of ensuring the right people are using the data, and data authorization ensures they are using it in approved ways. Organizations can manage data authentication and authorization in a number of ways, including the most common method of controlling access via user permissions and logins. This is extremely important when handling high risk information. A comprehensive data classification strategy must also specify who and how users may access and use the data according to the data sensitivity. Additionally, a plan should be in place should an attack or breach occur. While a company’s executive is responsible for defining the plan, the IT department makes the plan work. By classifying information into three categories (low risk, moderate risk data, high risk) executives will have a better chance of ensuring their company's and customer's information remains secure. 

Solving Information Risk Challenges

Data is collected with a business objective in mind, and every enterprise has a quest for more meaningful data. However, with so much data coming in and at unprecedented rates, gaps are bound to occur. Often, the gaps stem from relying too heavily on manual data risk classification processes, making it difficult for analysts to keep up and opening the door to human error. AI solves this problem, and data fusion unifies data categorization from different sources for more accurate data risk classification.

By 2025, the World Economic Forum predicts the amount of data generated each day will reach 463 exabytes globally. AI solves multiple business challenges associated with data categorization and risk classification, including:

  • Reduces labor costs, as AI can classify data much faster and accurately than a human
  • Handles data inconsistencies and complexities, from missing fields to structure, semi-structured and unstructured data
  • Boosts accuracy and risk classification consistency
  • Builds in scalability, as machines can processes fluctuations in data volume easily

An organization-wide data risk classification plan that addresses core business needs, goals and objectives, and is well-communicated starts at the top. The end goal is to turn data into meaningful information and information into insight. A comprehensive data risk classification strategy is foundational to your business success.

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