# On Data Leakage Prevention Maturity: Adapting the C2M2 Framework
## 2 Materials and Methods
### Input Knowledge
#### 2.2.2 DLP Solutions  
Initially, DLP strategies focused on monitoring network traffic and preventing unauthorized user activities [19]. Over time, there was a shift towards more centralized monitoring. Today's DLP solutions offer comprehensive monitoring across various data channels, encompassing web traffic, print jobs, email gateways, and file transfers to USB devices. They also incorporate advanced techniques like deep packet inspection. Both endpoint clients and centralized event collection mechanisms can be utilized together [20; 21]. However, these advancements still come with challenges, notably performance limitations, e.g., when deploying sophisticated filtering rules such as advanced regular expressions to endpoint clients, which can significantly impact system performance. Figure 1: The DSR grid by [PERSON] et al. [14] served as both a tool for documenting activities within the DSR Methodology Process and the structure of this section. In addressing the diverse landscape of DLP techniques, it is essential to distinguish between the various approaches. These techniques can be broadly classified into three categories: content-based analysis, context-based analysis, and hybrid approaches. The following provides a summary of the various approaches to DLP. For comprehensive evaluations and detailed discussions of the advantages and disadvantages associated with each DLP approach, refer to the extensive analyses in the literature, e.g., [22, 23]. From the realm of data science and data mining, **content-based analysis** focuses on methods such as data fingerprinting, lexical content analysis (including rule-based systems and regular expressions), and statistical analysis. This technique [24] is adept at detecting sensitive information across various platforms, including laptops, servers, cloud storage, and outbound network traffic. Its approach is reminiscent of early spam filter technology [25]. It aims to prevent the accidental exposure of sensitive data, whether the data are static (at rest), being used (in use), or being transmitted (in transit) ([18], D-9). While effective in scenarios where sensitive data, along with potential senders and recipients, are clearly identifiable, this approach faces challenges. It often results in a high false positive rate due to several identifiable factors: the difficulty in crafting filters that account for all edge cases, the dynamic nature of DLP rules where yesterday's valid rule might flag false positives today, inconsistent data classification leading to misidentification, and evolving language and communication patterns that surpass the DLP system's rule update frequency. These limitations diminish the practicality of this method for routine business operations. Furthermore, it is vulnerable to obfuscation tactics employed by attackers, as sophisticated data alteration techniques can limit its effectiveness. Contrastingly, **context-based analysis** eschews direct identification of sensitive content, instead analyzing the meta information associated with data or the context surrounding them [26]. This method models normal user behaviors and detects anomalous patterns, using machine learning to identify outliers. However, a significant limitation of this approach is its potential ineffectiveness in practical, everyday scenarios. For instance, it may fail to classify an email as a data leak risk based solely on metadata, without examining the content of attachments. Such scenarios include sending emails with attachments to personal addresses, where the actual content of the attachments is crucial for an accurate assessment of data leakage risk. Additionally, the effectiveness of this approach is often hampered by a lack of comprehensive training data, which are essential for accurately identifying false positive and false negative events and for refining the detection process. **Hybrid Approaches** combine both content and context analysis [27, 28, 29]. This method leverages the strengths of both techniques to provide a more comprehensive solution.