In the context of derivative classification, how should revealed information be classified?

In the context of derivative classification, revealed information must be classified based on the aggregation of details because this approach considers the potential sensitivity of combined information that, on its own, might not seem classified. Derivative classification involves recognizing that even seemingly innocuous pieces of information can lead to the identification of sensitive subjects when aggregated.

When information is corroborated or combined with other known data, it may reveal a greater understanding of a classified topic. Thus, a proper derivative classification takes into account how various pieces of information can collectively affect national security or other protected interests. This requirement is essential to ensure that all aspects of sensitive information are appropriately protected, aligning with the principles of information security and classification guidelines.

The other options do not encapsulate this holistic understanding of information classification. Simply stating that information should be classified only if directly stated undermines the importance of context and synthesis of data. Likewise, stating that classification applies in three levels oversimplifies the scenario and does not account for the nuances of how information can become sensitive when pieced together. Lastly, only classifying information if it is not commonly known doesn't adequately address the risks associated with revealing sensitive details through aggregation.