As more organizations are moving their data and operations to the cloud, you should consider a cloud DLP policy to protect sensitive corporate files and prevent data leaks.
A DLP tool may use several different technologies and tools in order to protect data so that it cannot be accidentally or maliciously shared with people outside the company or uploaded to private cloud storage. Data loss prevention (DLP) software uses rules set up by the organization to determine which data should be kept confidential, and can detect activities that may risk the security of this data.
As an award-winning data security solutions provider, we’d like to share our insight into data loss protection and its benefits, as well as DLP tools and how to choose them.
Table of Contents
What is DLP Security?
Data Loss Prevention (DLP) is a strategy to prevent employees from sending sensitive data outside the corporate network. While traditional cybersecurity systems are designed to act as a barrier against outsiders from accessing sensitive data, data loss prevention technology is more concerned with insider threats.
Using DLP software is an element of a comprehensive approach to cloud security. Cloud data loss prevention services ensure that sensitive data does not end up in the cloud without sufficient encryption and protection.
Benefits of Data Loss Prevention Tools
Using DLP software helps you control the use and share of important information. The main DLP benefits include:
- Preventing data loss and data breaches
- Preventing unauthorized parties from accessing your data
- Providing you with better visibility and control over your data
- Improving compliance and avoiding compliance violations
- Helping your IT security specialists to automate their work
How Does DLP Work?
Before choosing a Data Loss Prevention software tool or service, it is vital to define a DLP strategy for the entire organization, including general security practices and responsibilities for employees at all levels within G Suite domain.
Often the first step is to classify all corporate data and regulate its access, use, and acceptable methods of sharing based on your classification.
Of course data classification is not always an easy task and is made more complicated by the fact that many different people may be responsible for classification (creators, owners, users, etc.) and the amount of data in any organization is growing all the time.
Automated data classification can help and this is often a feature of DLP tools. Staff should also be trained on how to properly classify data they create and a strict policy should be in place for how this data is used and shared both internally and outside the organization.
Once there is an appropriate method of classifying data in place and clear policies surrounding its usage, the task of how to protect it will become clearer.
A very basic DLP policy could include data loss prevention guidelines such as secure methods for sharing sensitive data, clear rules on the use of BYOD and apps that have access to company data, and tight controls on physical transfer of data via USB stick or other insecure methods.
Having these guidelines in place can greatly reduce the risk of a data leak, as many high-profile cases of data leaks were caused by careless employees or as the result of insufficient training because the employee did not realize they were putting data at risk.
However, even with well-written data loss prevention policies in place and diligent staff training, data will still frequently be put at risk by employees ignoring these guidelines either for malicious reasons or because of insufficient understanding of security threats
This is where DLP services can come in by helping to detect risky employee behaviors and prevent sensitive data from leaving the corporate network.
Key Steps of Data Loss Prevention Process
Every DLP security process will be slightly different for each organization, but there are a series of general steps that are taken to implement a DLP service:
- All data is classified. Each item of data in the organization is tagged with a meaningful description. There may be several levels of classification. A basic example would be: “public”, “internal”, or “confidential”.
- Based on the classification of the data, confidentiality markers are created. These can be specific phrases in the text that sign confidentiality such as “private and confidential” or “top secret”, a pattern-match such as a credit card number, or full snapshots of confidential documents. There may be hundreds or thousands of confidentiality markers for each organization.
- Based on the confidentiality markers, security rules are created. These rules provide guidelines of the actions that should be taken if a trigger for a specific marker occurs. Actions can be divided into 2 parts:
- Active – if a trigger occurs, confidential information transmission is stopped automatically.
- Passive – if a trigger occurs, the transmission does not stop but an incident alert is sent.
- The incidents triggered by a rule break are then processed and analyzed by the security department. False-positive incidents are investigated and algorithms are adjusted to prevent false triggers in the future.
- New data is constantly classified as needed.
DLP (Data Loss Prevention) Tools
It’s important to choose a Data Loss Prevention service provider that aligns with the needs of your organization and integrates with the current IT tools that are in use for the normal everyday functioning of your business.
While it’s important that any DLP tool has full features and capabilities, it’s equally important to choose a service that can be easily deployed and managed.
DLP tools can be split into two groups:
- Built-in―data loss prevention features within Google Workspace (G Suite) or Microsoft 365. For example, Google’s own DLP functionality.
- Third-party-managed DLP services you can use to improve built-in functionality. For example, SpinOne, Virtru, or Nightfall.
To see how you can use a professional DLP tool to reduce insider threats, check out this post where we use our tool as an example.
9 Steps to Evaluate Data Loss (DLP) Solutions:
- Where is sensitive data detected – just on local client machines, or also in transfer across the network and in the cloud?
- How easily can DLP rules be created and edited?
- How does the software deal with a DLP rule break – exactly what data is reported?
- How are admins notified of DLP rule breaks?
- What reporting and analysis capabilities are included?
- Are risky activities blocked or just reported?
- What platforms are supported?
- Does the service protect against internal threats, external threats, or both?
- How often is the service updated and new features added?
Data Loss Prevention and Data Leak Prevention are important components of the data security policy for every organization and should be treated with accuracy and thorough analysis by organizations of all sizes.