Data is a precious resource for businesses, but losing it can also have disastrous consequences. To avoid this, it's best to implement a DLP strategy. What is DLP? What's the point? And how can it be implemented? Find out the answers.
What is DLP (Data Loss Protection)?
DLP stands for Data Loss Prevention. The idea is to prevent data loss, especially by preventing employees from sending critical information outside the company’s network. To achieve this, organizations can implement a series of measures aimed at ensuring the security of sensitive information.
Often, DLP solutions are software used by IT security managers to control what can or cannot be transferred. For this purpose, DLP solutions classify different data based on their level of confidentiality. This allows defining what can or cannot leave the network (and by whom).
In addition to enhancing control over network activity, DLP software can also filter data streams within the network itself, thereby protecting data in motion.
To optimize the use of these tools, companies have a significant interest in defining a comprehensive data leak prevention strategy.
Why use DLP tools?
Limit internal errors
43% of data breaches in an organization originate from an internal employee. Therefore, while human error is a factor, in the realm of IT security and data protection, these errors can sometimes have disastrous consequences for organizations.
Indeed, an employee who inadvertently shares a file on a public cloud and discloses confidential information can hold the company liable or tarnish its reputation. Not to mention the financial losses required to mitigate the damage.
The purpose of DLP (Data Loss Prevention) software is precisely to prevent unauthorized users from sharing information that should remain on the network (potentially harming the company).
Comply with data protection regulations
In addition to internal threats, companies across all sectors of activity are also subject to the GDPR (General Data Protection Regulation).
In this regard, organizations are considered responsible for all their data, whether it concerns the company itself, customers, partners, and so on. And when the organization holds information related to third parties, it must be doubly vigilant regarding the risk of data exfiltration.
How to implement a DLP strategy
To successfully implement your data loss prevention strategy, you need to follow a series of best practices. Here’s what you need to know:
Prioritization
First and foremost, it is essential to define critical data—those that would genuinely harm the company in the event of theft or cyberattacks.
Network administrators should focus their attention on these sensitive data sets, as they are more likely to be targeted by cyberattacks.
Typically, at-risk data includes vital information for the company, such as customer contact details, source codes, employee information, and other data necessary for its operations.
Data classification
The idea is to categorize different types of data based on the level of protection required. For example, data can be distinguished as data in motion, data in use, or data at rest.
However, it is also possible to classify information based on context, data source, creator, format, keywords, and more. This classification allows for the quick identification of confidential data. To achieve this, it is advisable to use certain standards, such as PCI or PII. These standards define a uniform format for all data to easily identify those with similar characteristics and, therefore, the same level of criticality.
Furthermore, it is essential to determine the location of data. It can be categorized as critical, secure, or open. For an effective DLP strategy, each piece of data should be stored based on its level of confidentiality. Less sensitive information may be freely accessible to employees or even third parties, while strictly confidential information should require permissions for sharing.
Risks
While data protection is a concern for all businesses, the associated risk varies from one company to another and even from one piece of data to another.
For instance, the disclosure of customer data directly implicates a company’s responsibility. On the other hand, if a company’s strategy is disclosed, the harm primarily concerns competition.
In the same vein, the risk also differs depending on the location of the data. In general, when data is within the network, the threat is lower. However, when it leaves the network, the level of criticality increases. Therefore, DLP (Data Loss Prevention) must be able to monitor all data flows, both inside and outside the organization (e.g., with clients, partners, or suppliers).
To achieve this, IT security managers must understand user behaviors for each data movement and, most importantly, identify those that pose the highest risks.
Lastly, the risk can also be associated with the channel used to share data, such as email or removable storage devices. The DLP software must adapt its strategy based on the specific threat in use.
Communication
While the IT security manager is responsible for implementing the DLP strategy, its effectiveness relies on the entire workforce. It is essential to collaborate with various departments to mitigate the risk of sensitive data leakage.
On one hand, the IT security manager must understand the habits and needs of employees to tailor the data loss prevention strategy accordingly.
On the other hand, they must educate employees on the behaviors to adopt to protect data. Many employees may not be aware that specific actions can have adverse consequences for the entire organization. Therefore, it is necessary to provide training on both the risks and the best practices to develop.
Controls
Repetition
Companies have a strong interest in repeating this process continuously. This iterative approach allows for the refinement of the DLP strategy, maximizing security and data protection. Moreover, it is crucial to repeat the process as new data emerges within the organization. This ensures that the DLP strategy remains up-to-date and effective in safeguarding data.
