How Data Classification Improves Incident Response

Most cybersecurity teams are under immense pressure to contain threats and recover from attacks as swiftly as possible. Fortunately, if they leverage classification methods, they can relieve some of that strain. How can data classification improve their incident response?

What Is Data Classification?

Data classification is the practice of organizing information into categories based on its characteristics, value or sensitivity. The goal is to make it easier to store, search for and retrieve while increasing its resiliency to unauthorized access attempts and cyber attacks. 

Most organizations collect and store a combination of public, private and sensitive information. Common examples are census details, human resources documents, sales figures, financial records and proprietary reports. Without classification, figuring out which is which at first glance can be time-consuming and confusing. 

This issue worsens as the number of information assets increases. Considering the average enterprise had over 2 petabytes in 2022 — up from 1 petabyte in 2020 — it’s more common than people might assume. How can businesses respond to incidents effectively when they have to sort through so many unorganized files?

In the information security sector, an incident is a service interruption or disruption affecting systems or files. It almost always impacts security, meaning cybersecurity professionals must assess, respond and report it as quickly as possible to prevent financial losses, regulatory measures, customer backlash or legal action. 

How Does It Work?

Preprocessing is the first step of classification. Data scientists must remove duplicates and correctly format everything to streamline the search and retrieval processes. From there, they consider technical, privacy, compliance and security to decide how to place each asset into a predefined category. 

Decision-makers often use sensitivity levels to determine placements. While public data like mailing lists are low-risk and easy to replace, proprietary information like trade secrets are irreplaceable. They must also consider the consequences of a breach — if hackers stole employees’ financial details or contact information, they’d face legal or regulatory action.

Every information asset must be tagged with preset labels to make things searchable and traceable. From there, businesses place them in specific storage systems depending on category. They also often apply certain security or access controls as necessary, using sensitivity and risk levels to determine which to prioritize. 

Types of Data Classification

Three main types of data classification exist. 

1. Content-Based

Content-based classification focuses on the information itself. The contents of a file can determine its sensitivity level. For example, health records and financial details are typically more risk-prone than account passwords or GPS data.

2. Context-Based

Companies often must consider the circumstances in which data is used, shared and stored. Context-based classification determines sensitivity and value by using metadata and status details. Source, location and usage are common examples. 

3. User-Based

User-based classification relies on an individual’s knowledge, judgement and awareness to determine how information should be categorized. It’s often necessary because complex factors like compliance, vendor relationships and cybercrime can affect systematization. 

The Benefits of Classifying Information Assets

Security is one of the most significant benefits of data classification. It lessens data breach severity by substantially lowering organizational risk. In other words, firms can use it to minimize their financial and reputational losses. 

Another significant benefit involves compliance. Cybersecurity teams with accelerated incident response times have a greater chance of containing threats before they can compromise files, substantially lowering the risk of non-compliance-related regulatory action. 

Ways Data Classification Improves Incident Response

Data classification can help improve cybersecurity incident response in multiple ways. 

  1. Improves Risk Assessments

When decision-makers classify information based on sensitivity, they better assess risk. Once they determine how valuable each category is to threat actors, how likely incidents are and the potential consequences of a breach, they can prioritize their assets.

Moreover, they may realize they have a disproportionate amount of proprietary, confidential or financial documents, enabling them to make data-driven decisions about storage system location, access controls and security measures. 

  1. Accelerates Response Speed

Classification lets IT professionals know precisely where every information asset is, where they get sent to and who has access to them. This increased visibility makes tracing the source of an incident much more straightforward, accelerating response time. 

Considering the average breach’s incident response time is about 277 days — 207 for detection and 70 for containment — acceleration is critical. This way, businesses can minimize the financial, reputation and loyalty losses they incur.  

  1. Improves Response Efficiency

Companies often separate files into different storage systems based on classification. Consequently, they can identify a security incident’s impact with greater accuracy, accelerating containment and recovery. 

In some cases, businesses are legally required to inform people of an incident if it compromises their privacy or security. The reporting timeframe is typically a few months. Considering they must first recover, investigate with law enforcement and identify those affected, every day they save because of response efficiency is crucial. 

The Importance of Classifying Data 

Cybersecurity teams can use data classification to improve their incident response, strengthening their security posture substantially. On top of streamlining employees’ everyday responsibilities, it can help protect employers from cyber threats.

As the Features Editor at ReHack, Zac Amos writes about cybersecurity, artificial intelligence, and other tech topics. He is a frequent contributor to Brilliance Security Magazine.

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