Identification of how sensitive the data is. Data classification informs a data security team of how to prioritize their data security efforts across their organization. Examples of how sensitive data is can include severity ratings such as low, medium, and high. Data classification is nuanced. For example, a number that resembles a US Social Security number might not be sensitive if it is test data, because it is of no value, and needs minimal security. However, intellectual property might be critically sensitive, even if it is unique to a single organization, and requires the greatest attention from the security team. Legacy data discovery and classification tools rely on predefined regular expressions that are unable to understand the nuances above. Therefore, they often arrive at incorrect sensitivity ratings. A fictionless data discovery and classification solution uses automated reasoning to categorize data accurately, using its business context.