[ad_1]
The content of this post is solely the responsibility of the author. AT&T does not adopt or endorse any of the views, positions, or information provided by the author in this article.
Preventing data loss is a concern for almost every organization, regardless of size, especially organizations with sensitive data. Organizations, now more than ever before, rely on voluminous amounts of data to conduct business. When data leakage or a breach occurs, the organization is forced to deal with the negative consequences, such as the high cost associated with data breach fines and remediation and reputational harm to their company and brand.
Data loss prevention (DLP) solutions help mitigate the risk of data loss. Losses can occur as a result of insider-related incidents (e.g., employee theft of proprietary information), or due to physical damage to computers, or as a result of human error (e.g., unintentional file deletion or sharing sensitive data in an email). In addition to the various ways an organization might experience data loss, mitigating the risk of loss requires the right people, processes, and technology.
Meeting the technology requirement can be a challenge when it comes to selecting the right DLP solution. During the vendor exploration and evaluation phases, there may be questions about whether it makes sense to invest in a solution that protects the network, endpoints, or the cloud or whether it’s better to select a solution that protects the enterprise and takes into account the hybrid nature of many organizations.
Data classification and labeling
The decision to invest in a DLP solution should be informed by sufficient research and planning with key stakeholders. This blog will discuss three additional things you should consider before making such an investment. Let’s begin with the types of data an organization collects, stores, and analyzes to conduct business.
To have a successful data loss prevention program, it’s important to identify all types of data (e.g., financial data, health data, or personally identifiable information) and to classify the data according to its value and the risk to the organization if it is leaked or exfiltrated. Data classification is the process of categorizing data to easily retrieve and store it for business use. It also protects it from loss and theft and enables regulatory compliance activities. Today, systems are more dispersed, and organizations have hybrid and remote workforce models, so it is critical to protect data regardless of where it resides or with whom it is shared. This kind of protection requires properly classified and labeled data.
Automated data classification is foundational to preventing data loss. It is the best way for organizations to fully understand what types of data they have, as well as the characteristics of the data and what privacy and security requirements are necessary to protect the data. Properly classifying data also enables the organization to set policies for each data type.
Techniques to identify sensitive data
DLP solutions detect instances of either intentional or unintentional exfiltration of data. DLP policies describe what happens when a user uses sensitive data in a way the policy does not allow. For example, when a user attempts to print a document containing sensitive data to a home printer, the DLP policy might display a message stating that printing the document to a home printer violates the policy and is not permissible. How does the DLP tool know that the document includes sensitive data? Content inspection techniques and contextual analysis help identify sensitive data.
The inspection capability of the DLP solution is very important. It’s important to note that traditional DLP solutions focus on data-specific content inspection methods. These inspection methods are no longer effective for organizations that have migrated to the cloud because the techniques were developed for on-premises environments. Gartner recommends investing in a DLP solution that not only provides content inspection capabilities but also offers extra features such as data lineage for visibility and classification, user, and entity behavior analytics (UEBA), and rich context for incident response. UEBA is useful for insider-related incidents (e.g., UEBA might help identify data exfiltration by a dissatisfied employee).
What actions will the DLP solution perform
After it’s clear that the tool can classify sensitive data, a logical next question is what actions the tool will perform to prevent loss of that data. A DLP solution performs actions such as sending out alerts for DLP policy violations, warnings using pop-up messages, and blocking data entirely to prevent leakage or exfiltration. Another feature might include quarantining data. Organizations should be able to define their policies based on their policy, standards, controls, and procedures.
Traditional DLP relies heavily on content analysis and does not always accurately identify sensitive data. Sometimes traditional tools block normal activity. In contrast, a modern DLP solution minimizes false positives by combining content analysis and data lineage capabilities to more accurately understand whether the data is sensitive.
There are many DLP tools on the market. A DLP solution might also be a capability in another security tool such as an email security solution. Selecting the right tool requires knowledge of market trends, the gap between traditional and modern DLP tools, data loss prevention best practices, and the purchasing organization’s security initiatives and goals. Given the many options and variables to consider, it can be challenging to understand the nuances and distinctions among solutions on the market.
[ad_2]