Information Risk Management Blog

How to Detect and Prevent Data Loss Due to Remote Work

Written by Vivek Chaudhary | Jun 18, 2020

Rapid digitalization of businesses and operations is enabling the reality of a hyperconnected world. The digital aspirations of enterprises have been aggressive through the massive adoption of data analytics to drive more engaging digital experiences.

The new world of connectivity driven by digital value chains is leveraging large volumes of data to integrate sensitive customer data, apps, servers, workstations, and various other devices. There is also an increased dependence on data stored in Cloud, IoT, Blockchain, BigData, and mobile computing devices to drive agility and digital connectedness.

The value of data is more than ever before, making it more vulnerable due to the higher level of visibility across digital platforms. This exponential use and leverage of huge data volumes have vividly increased opportunities for theft, fraud, leaks, and accidental exposure of sensitive data.

The only way organizations can protect their data is through Data Loss Prevention (DLP).

Data Loss Prevention (DLP) is a strategy to ensure that sensitive data remains securely within the corporate network.

DLP helps you to monitor and protect data at rest, in use, or in motion across the organizational environment. A DLP system can enable in driving effective data security policies through a detailed, circumstantial analysis of data transactions to detect and prevent unauthorized use.

You can also avoid accidental data leaks and mitigate theft risks due to outside attacks and deliberate misappropriation by insiders.  DLP technologies leverage a set of rules to identity information an detect any anomalous transfer of data. It can provide a comprehensive understanding of data and helps in identifying the data assets which should be protected based on priority.

Every Endpoint activity gets monitored continuously for emails, attachments, USB drives, including any employee involved in copy/paste/print of classified data. You can even avoid data loss due to theft of laptop/mobile devices, accidental deletion, fire, or system failures.

DLP has also established its importance with the increasing demands of remote work due to unforeseen emergencies such as COVID 19 pandemic. DLP can monitor the use of corporate data by employees working from home or any remote location.

Data loss prevention tools and software constantly monitor and filter data in real-time. In addition to dealing with the data used, stored, and transmitted within the network, data loss prevention applications ensure no harmful data is entering the company network from outside sources.

 

How to detect & prevent data loss through a robust DLP strategy

A holistic approach is necessary to develop a DLP strategy that can help in implementing a strong group of controls to protect the most sensitive and classified data within the organization. The strategy should be designed based on well-defined business objectives to maintain sufficient security and provide usability, mitigate risks, and ensure compliance.

Your DLP strategy must cover the following areas, Data Governance, Data Identification, Data Protection, Incident Monitoring & Detection, Incident Response & Recovery, and Performance Review.

  1. Data Governance focuses on classifying data based on its sensitivity and where they reside (drives, databases, emails, etc.) and which of them should be secured. Data exchange across networks, including third-party data access, is also understood to know how data flows and who is using them. Reporting templates are designed based on the security incident management process followed by the business.
  2. Data Identification is driven by recognizing data located in various repositories. Rule sets are formulated in consultation with business, and IT teams to protect the identified critical data sets. The details required for DLP/ruleset configuration include lists of productive websites and applications, sensitive keywords, authorized domains & websites, and registered devices.
  3. Incident Monitoring & Detection is enabled by integrating endpoints with managed SIEM platform for continuous monitoring, while ITIL best practices drive incident monitoring and detection operations for resilience and scalability. Sensitive data is protected by controlling the data transfer through USB devices, and restricting/blocking print screen activities to prevent information leaks.
  4. Monitoring and detection activities include tracking email activities while working from home or remote location, assessing employee productivity, user behavior monitoring, block unauthorized applications at the endpoint, employee forensics by screenshot monitoring, supervise file upload activities to unauthorized URLs, monitor sharing of sensitive information across channels, live alerts of incidents, and regular monitoring of email activity from personal/BYOD devices (using Secure Email Gateway).
  5. Response & Recovery teams involve themselves in performing alert triage to identify security incidents that should be managed more effectively and work with respective teams to resolve incidents. They then devise appropriate recovery plans based on the incidents and established cadence.
  6. Reports & dashboards are generated based on weekly/monthly/quarterly security incidents and given for internal review and shared with leadership teams for taking relevant security decisions.

 

The risk landscape has even more widened due to this era of working from home or remote location. Preventing loss of data is a prudent way to avoid common threats, than recovering from a costly breach. Data Loss Prevention can help you get a comprehensive 360 degree view of your data environment by enabling you know the data types in your organization, quickly respond to data leak events, understand any gaps and risks, help you know who is accessing data, where it resides, and meet all your obligations for protecting the data.

 

Case Study 1: Design and Implementation of an Advanced DLP Solution for a Large Insurance Firm. Download here.

Case Study 2: Data Leak Prevention Solution for a Large Retail Giant to protect sensitive data from leaks and breaches. Download here.

Case Study 3: DLP Solution Transformation for a Large Multinational Bank. Download here.

 

To know more about Aujas privacy and data protection services, visit us here. or write to us at contact@aujas.com.