I Hate CBTs Derivative Classification
CBTs (Computer-Based Training) have become a popular method for employee training in many organizations. However, there is one aspect of CBTs that I simply cannot stand: derivative classification. In this article, I will delve into the reasons why I have developed a strong dislike for derivative classification in CBTs. From my personal experiences to the potential pitfalls it presents, I’ll explore the various aspects that have contributed to my disdain for this particular element of CBTs.
Derivative classification is a process that involves determining the appropriate level of security classification for information based on existing classified sources. While it may seem like a necessary step to ensure the protection of sensitive information, I have found that it often complicates the training process unnecessarily. The complex rules and regulations surrounding derivative classification can be overwhelming for employees, leading to confusion and potential errors. In this article, I will discuss the challenges and frustrations associated with derivative classification in CBTs, and why I believe there are better alternatives available.
One of the main reasons why I have developed a strong aversion towards derivative classification in CBTs is the potential for misinterpretation and misapplication. The intricate nature of derivative classification can easily lead to mistakes, which in turn can have serious consequences for an organization. From inadvertently mishandling classified information to unintentionally overclassifying or underclassifying data, the risks are significant. In this article, I will explore the potential pitfalls of derivative classification and why I believe it is a flawed approach in the context of CBTs.
The Basics of Derivative Classification
Derivative classification is a fundamental concept in the world of CBTs (Computer-Based Training) and information security. It involves the process of determining the appropriate security level for information based on already existing classified sources. While it may seem like a necessary step in ensuring the confidentiality and integrity of sensitive information, I have come to hate derivative classification for several reasons.
One of the main reasons for my aversion to derivative classification is the complexity it adds to the training process. CBTs are designed to make learning efficient and accessible, but when derivative classification is introduced, it can create confusion and frustration for learners. The additional steps and considerations involved in determining the security level of information can overwhelm employees and hinder their ability to grasp the core concepts being taught.
Furthermore, derivative classification opens the door for misinterpretation and misapplication of security levels. Human error is inevitable, and when employees are tasked with determining the appropriate classification based on their understanding of existing classified sources, there is a risk of mistakes being made. These mistakes can have serious consequences for organizations, as sensitive information may be mishandled or improperly protected.
Another drawback of derivative classification is its reliance on existing classified sources. While these sources may provide valuable guidance, they are not always comprehensive or up to date. New information and emerging threats may not be adequately addressed in existing sources, leading to outdated or inaccurate classifications. This can undermine the effectiveness of CBTs and compromise the security of sensitive information.
Understanding CBTs
When it comes to employee training, Computer-Based Trainings (CBTs) have gained significant popularity in recent years. CBTs provide a convenient and cost-effective way to deliver training materials to a large number of employees. However, one aspect of CBTs that I find particularly frustrating is derivative classification.
Derivative classification is the process of determining the appropriate security level for information based on existing classified sources. This means that when creating training materials, the content must be classified according to the level of sensitivity it contains. While this may seem like a necessary step to ensure the security of information, I believe that it unnecessarily complicates the training process and can lead to confusion and errors.
One of the main reasons I dislike derivative classification in CBTs is the potential for misinterpretation and misapplication. Employees who are not familiar with the intricacies of classification may struggle to accurately assign the correct security level to the information they are working with. This can have serious consequences for an organization, as misclassified information can end up in the wrong hands, jeopardizing security and potentially leading to harmful outcomes.
Furthermore, derivative classification can be time-consuming and resource-intensive. Training materials often need to go through multiple rounds of review and approval to ensure that the classification is accurate. This can slow down the training process and create unnecessary delays in getting critical information to employees. It also requires additional resources to maintain and update classification guidelines, adding to the already demanding workload of training administrators.
In my opinion, there are better alternatives to derivative classification in CBTs. By adopting a more simplified approach to training materials and focusing on the content itself rather than its classification, organizations can streamline the training process and minimize the chances of errors and confusion. This would allow employees to focus on learning and retaining the information they need to perform their jobs effectively, without getting caught up in the complexities of classification.
