Certified: The Security+ Prepcast

In this episode, we are continuing our exploration of data classification strategies, focusing specifically on private data and public data, along with how to build a truly effective data classification policy. These concepts are essential not only for passing the Security Plus exam but also for understanding the foundation of data protection in any organization.
Let us start with private data. Private data refers to any information that can be used to identify, contact, or locate an individual. This includes names, addresses, social security numbers, health information, financial account numbers, and other forms of personally identifiable information. In many cases, private data is protected by privacy laws and regulations. For example, in the United States, the Health Insurance Portability and Accountability Act requires organizations to protect patient health records. In Europe, the General Data Protection Regulation places strict rules on how personal data is collected and used. Because of the sensitivity and legal obligations surrounding private data, organizations must apply strict controls to prevent unauthorized access, loss, or misuse. This can include encryption, access controls, employee training, and regular audits to ensure compliance.
Now let us talk about public data. Public data is information that is intended to be shared openly and does not require protection. Examples include marketing brochures, published research, or job postings. Although public data is not sensitive, it still requires proper handling. Poor management of public data can lead to misinformation, outdated documents, or unintended leaks if something classified as private is mistakenly published. For instance, imagine a public-facing company directory accidentally including employee identification numbers. While the data was meant to be public, an oversight turned it into a risk. Therefore, even public data must go through classification checks to ensure it is truly safe to publish. Security practices such as peer review, approval workflows, and regular updates help reduce the risks associated with public data.
Understanding the difference between private and public data is only the beginning. To implement effective data classification, organizations need a structured policy that guides how data is labeled, handled, and reviewed throughout its lifecycle. A strong classification policy starts with defining clear categories such as private, confidential, internal use, and public. Each category should have associated handling procedures, storage requirements, and access controls. For example, data labeled as confidential may require encryption and limited user access, while internal-use data might be stored in a secure company server but shared more freely among departments.
Creating the classification policy is only the first step. Equally important is making sure everyone understands and follows it. Best practices include training employees during onboarding, requiring periodic refresher courses, and incorporating data classification into everyday workflows. Some companies embed classification labels directly into email systems and file templates so that users are reminded to tag documents correctly. Others use automated tools that scan content for keywords and apply classification labels based on preset rules. These automated tools can be a great supplement to human awareness, helping to catch errors and enforce consistency.
To make these ideas more concrete, let us look at a few real-world examples. In one financial institution, employees were trained to classify documents based on whether they contained customer account numbers, transaction details, or investment data. Files marked as confidential were stored in an encrypted cloud environment with multifactor authentication required for access. Public brochures, on the other hand, were reviewed quarterly to ensure that contact details and interest rates were current. This structured approach helped the organization maintain regulatory compliance while avoiding accidental data leaks.
In another case, a healthcare provider implemented a simple yet effective classification system with three tiers: public, internal, and protected health information. Public information included clinic hours and general health tips. Internal data included meeting agendas and operational updates. Protected health information was handled with the strictest security controls, including end-to-end encryption and audit logs. This clarity helped reduce confusion among staff and ensured that sensitive patient information was always treated appropriately.
These examples highlight the importance of aligning data classification strategies with the type of information your organization handles. They also show how thoughtful policy design, combined with ongoing training and practical tools, can turn classification from a paperwork exercise into a real-world security benefit.
As you prepare for the Security Plus exam, remember that questions on data classification may ask you to identify appropriate categories, understand handling procedures, or evaluate policy effectiveness. Be ready to distinguish between private and public data, and to explain the security measures that should be in place for each. Also, make sure you can recognize what makes a data classification policy successful—clear categories, proper training, and a combination of manual and automated enforcement.
Here is a tip for the exam: when you see a scenario about information handling, look for clues about the sensitivity of the data. If the information includes personal identifiers, medical history, or financial details, it is almost certainly private data and requires strong protection. On the other hand, if the data is intended for external sharing with no sensitive content, it may be classified as public—but still needs a careful review before release. Pay close attention to classification categories mentioned in the scenario and how data is stored or transmitted. These are often the keys to selecting the right answer.