Understanding the Four Objectives of Data Classification

Data Classification
5 min read
James Mignacca
August 16, 2023
James Mignacca
August 16, 2023
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Taking (and maintaining) control of your organization’s attack surface boils down to a basic truth: you can’t protect what you don’t know about. If you don’t have confidence in your organization’s current data classification software or practices, then it’s likely that your organization uses, stores, and shares volumes of sensitive data that it’s not aware of.

Sensitive data is high-value currency to malicious actors and career hackers. As sensitive data stores build over time, attackers improve their odds of breach success and overall profitability. That’s because every piece of data can be used to complete a puzzle that grants threat actors greater access to individuals and organizations.

Even the most innocuous data is valuable

Every organization has data—and lots of it. Almost every organization also has some level of classification of data in place; however, many of the systems and structures they use don’t or can’t account for unique, duplicate, or orphaned data, otherwise known as dark data.

The most vulnerable types of dark data classify as personal identifiable information (PII); according to the 2023 Verizon Data Breach Investigations Report, customer, partner, and/or employee PII accounted for more than 50% of confidential data varieties in reported breaches.

Dark data represents unstructured information, and it’s found everywhere, including databases, emails, documents, and even social media accounts. IDC estimates that by 2025, the world’s 175 zettabytes of data will contain mostly unstructured data types, at a whopping 80 per cent.

Workplace reliance on hybrid and remote systems, cloud applications, and endpoints continuously expand data stores. Data is often siloed across these systems, making it hard to track and manage. The first step to managing both unstructured and structured data types across your organization is to understand where the data lives.

Sensitive data lives everywhere

Sensitive data can be found on email servers, cloud storage systems, file sharing services like Dropbox or GoogleDrive, and customer relationship management (CRM) tools like Salesforce or Office365. But how do you know what data types are sensitive?

Data classification best practices involve understanding the types of data that need to be protected, like customer information or financial records, and then determining how that data should be classified based on its sensitivity level. This helps organizations identify which digital assets or areas of the business could be more vulnerable to attacks and prioritize their security efforts accordingly. Additionally, this can help ensure compliance with regulations related to protecting sensitive information.

Classification of data can vary depending on the industry your business operates in and the kinds of data it handles. Understanding the specific data types your business uses and how those data types affect your organization’s risk benchmarks and overall attack surface risk is key to developing an effective data classification strategy for your business.

Aligning data types to core data classification objectives

Data classification is essential to protect data from unauthorized access, misuse, and disclosure. The four key objectives of data classification are: confidentiality, integrity, availability, and accountability—let’s look at how they work together to help secure sensitive data:

1. Confidentiality

The first objective of data classification is to keep information confidential or private. This means ensuring that only authorized users can access it and view its contents, while preventing it from being shared with those who do not have permission to view it. Confidentiality also ensures that individuals’ personal information remains protected from unauthorized access or disclosure.

2. Integrity

Data integrity is the second data classification objective and an important concept when it comes to attack surface management. It refers to the accuracy and completeness of data, ensuring that all information stored in a system remains consistent over time, identified and authenticated through verifiable methods like automated checksums which detect content changes over time without manual intervention. Data integrity prevents modification made during transit between an initiating system(s) and receiving systems (the source and destination). This means that any changes made are tracked and recorded so that they can be undone if necessary. By maintaining data integrity, organizations can reduce their risk of being attacked by malicious actors as they have better control over what kind of access different users have to sensitive systems or files.

3. Availability

Data availability refers to the ability for data to be accessed and used by authorized users, while preventing unauthorized access or use. By ensuring data availability, organizations can reduce their risk of being targeted by attackers and guard sensitive data from exploit.

4. Accountability

Data accountability requires that organizations keep track of who has accessed organizational data. It’s the process of ensuring that data remains secure and protected from unauthorized access or manipulation by identifying potential vulnerabilities, implementing security measures such as encryption and authentication protocols, and monitoring for suspicious activity. Data accountability helps businesses maintain trust with customers and stakeholders; the activities supporting data accountability help demonstrate the measures that your organization has in place to protect sensitive data. It also ensures compliance with regulations such as GDPR and HIPAA while providing an audit trail in case a breach occurs.

Understanding these four objectives is fundamental to data classification—and essential to attack surface management. Download the Data Protection Solutions Guide to access new and emerging use cases and industry best practice frameworks to help evolve your organization’s data classification strategy.

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