Cloud DLP: Data Security for the Remote Workforce
What is Cloud Data Loss Prevention (DLP)?
Data loss prevention (DLP) secures sensitive data and prevents accidental exposure or malicious data exfiltration. It protects data in transit, data at rest, and data on endpoints. DLP solutions encrypt data to prevent its exposure, and monitor and control data transfers to make sure they are legitimate.
As organizations everywhere transition to remote work, Cloud DLP is becoming critical to data security. Cloud DLP solutions are used by organizations that store sensitive data in cloud storage (commonly accessed by remote employees, but also used from office locations). They encrypt data at rest while stored in cloud systems, and ensure data is only sent to authorized applications. Some cloud DLP products anonymize or obfuscate sensitive data to reduce the impact of data exposure.
This is part of our series of articles about endpoint security.
How Does Cloud DLP Help Secure Remote Workforce Environments?
Multi-cloud environments using tools like Slack, Salesforce, Box, Google G-Suite, and Office 365, promote collaboration and productivity. Employees access these applications from a variety of devices, both corporate and personal, as well as from mobile devices. This raises the need for security policies and controls to safeguard sensitive data.
Different cloud applications employ different security management interfaces and each requires an administration effort. Managing this patchwork of applications and policies can become very complex.
Cloud DLP enables consistent data security and management across different software as a service (SaaS) applications and infrastructure as a service (IaaS) resources, by extending a company’s data security controls to the cloud.
Centralized policies enable cloud DLPs to classify and monitor sensitive data access and protect it, whether in the cloud, in emails and apps, in motion, and at rest. Real-time data protection includes data encryption, masking, and deletion of unnecessary data.
Related content: read our in-depth guides to other solutions that can help secure a remote workforce:
Key Features of Cloud DLP Solutions
A comprehensive cloud data loss prevention solution should contain the following key features.
Pre-Built and Customizable DLP Policies
Cloud DLP should provide out-of-the-box policy templates built around security best practices. However, these templates should be easily customizable, and the solution should let you build new policies from scratch if needed.
Content and Context-Aware DLP
Content-aware DLP technology is now a standard. It constantly scans data for known alphanumeric strings and key terms that indicate sensitive data (these may be defined as policy rules). Content-aware DLP, for example, should be able to identify a 9-digit string as a social security or ID number, and even recognize whether the string is legitimate, and thus requires protection, or not.
Machine learning techniques have become a critical part of data protection strategies. Machine learning enables DLP systems to progressively learn what should be flagged as a policy violation or security risk and what should not. Cloud DLP-based machine learning technology reduces false positives significantly, ensuring that only anomalies that have a real security impact are raised as alerts.
Alerts and Notifications
A cloud DLP should enable user notifications and administrative alerts, indicating policy violations that require remediation or investigation. Notifications for users are especially important, because they inform users that they have violated policies, and can instruct them how to handle data safely and reduce future incidents.
Cloud DLP solutions should support automation of monitoring, auditing, and security controls for cloud-based data. They should not only identify policy violations, but also automatically react to them. You should be able to set policies for deleting, quarantining, or unsharing data or an entire data source.
Cloud Data Loss Prevention Best Practices
Here are some best practices you should adopt to make the best use of a cloud DLP product.
Identify which types of data the organization defines as critical for business purposes, and which data is sensitive for security or compliance reasons. A possible parameter for determining how “critical” or sensitive data is, is the level of damage caused by its loss or compromise.
Apply DLP to the most sensitive or valuable data, which may attract an attacker and may result in the biggest risks to the business.
Classify the Data
To manage data more easily, you should classify it based on context. Associate each unit of information by its creator and data store, associated application, etc. Consistent classification tags enable easy tracking.
Leverage the DLP solution’s content inspection to automatically classify data by keywords such as ‘secret’ or ‘confidential’, or by patterns such as credit card or social security numbers. The content inspection feature will usually have configurations suitable for specific compliance standards, such as PCI DSS and GDPR.
Identify Risky Data Flows
Data is at the biggest risk when it is distributed to user devices, customers, partners, or a supply chain. Data is at risk when transferred to a storage device or endpoint, attached to an email, or transmitted in any other way. Map out your data flows, identify the ones that carry the most risk, and set the appropriate security policies via the DLP solution to minimize risk.
Monitor Data in Motion
Sensitive data in motion requires monitoring and a high degree of visibility. A DLP solution should be able to identify behavior that puts data at risk, generate alerts, and allow security teams to easily identify what is happening and whether the incident requires intervention.
Progressively Develop DLP Controls
Business line managers should be aware of DLP procedures. These may be simple, to begin with, targeting common but obviously risky behavior around data. As the program progresses, controls may be fine-tuned and made more granular to target more specific risks.
Train Employees and Leverage Automated Prompting
Unless trained on data security practices, employees will continue to exhibit risky behavior. Training reduces risk by explaining the reasons that can lead to data loss, and sharing best practices.
Automated user prompting is a simple and effective approach to user education, provided by DLP solutions. In addition to blocking some activity, the solution should notify about company policy or potential risk, and this can often be enough to suppress an activity.
Starting small is often an excellent way to deploy a DLP solution. Repeating similar steps on gradually expanding sets of data of data identification and classifications enables fine tuning controls. Begin by focusing on a subset of the most critical data. Then expand outward from the pilot, covering more and more sensitive information. This approach will also enable minimal disruption to business processes.
Enhancing Endpoint Security and Reducing Data Leakage with Hysolate
Hysolate provides your team with a fully isolated and secured and managed VM on their Windows10 endpoint device, to keep access to sensitive systems and data secure, so you can work productively on your host operating system.
Hysolate offers security features to reduce data leakage, including anti-screenshotting, anti keylogging and adding a unique watermark to your Workspace. The Hysolate Workspace can be set up to be non persistent, and can be remotely wiped in seconds if a threat is detected.
Hysolate improves endpoint security as enterprise access is done exclusively from a corporate OS while risky activities happen on the main OS, without the need to monitor personal/private user activity or to fully manage the user’s device.
Request a demo to find out more how Hysolate can help secure your endpoint devices.