Data Loss Prevention (DLP) is the practice of detecting and preventing data breaches, exfiltration, or unwanted destruction of sensitive data. Organizations use DLP to protect and secure their data and comply with regulations.  Organizations typically use DLP to:

• Protect Personally Identifiable Information (PII) and comply with relevant regulations
• Protect Intellectual Property critical for the organization
• Achieve data visibility in large organizations
• Secure mobile workforce and enforce security in Bring Your Own Device (BYOD) environments
• Secure data on remote cloud systems

The DLP term refers to defending organizations against both data loss and data leak prevention. Data leak refers to data that’s unintentionally pushed out, accessible to theft, easily exposed to download via web apps or USB devices and others. It is an event in which important data is lost to the enterprise, such as in a ransomware attack. DLP focuses on preventing illicit transfer of data outside organizational boundaries.

For organizations concerned with the loss or public exposure of important and often confidential data, Connectis will provide a Summary of Findings for DLP.  Our service includes identifying and classifying information that is Confidential,

Public and Private.  Consultants meet with and interview key stakeholders, along with our technologies, gather information on how data at rest and data in motion are stored, how internal systems that push data categorize the classification of the data, recognizing the security implications that must be maintained based on the classification.  Our report identifies and makes recommendations to how your policies should control the data flow.

Connectis DLP services help organizations recognize which assets require protection and how to protect them from data leak.

• Insider threats — a malicious insider, or an attacker who has compromised a privileged user account, abuses their permissions and attempts to move data outside the organization.
• Extrusion by attackers — many cyber attacks have sensitive data as their target. Attackers penetrate the security perimeter using techniques like phishing, malware or code injection, and gain access to sensitive data.
• Unintentional or negligent data exposure — many data leaks occur as a result of employees who lose sensitive data in public, provide open Internet access to data, or fail to restrict access per organizational policies.

• Securing data in motion: technology installed at the network edge can analyze traffic to detect sensitive data sent in violation of security policies.
• Securing endpoints: endpoint-based agents can control information transfer between users, groups of users, and external parties. Some endpoint-based systems can block attempted communications in real time and provide user feedback.
• Securing data at rest: access control, encryption and data retention policies can protect archived organizational data.
• Securing data in use: some DLP systems can monitor and flag unauthorized activities that users may intentionally or unintentionally perform in their interactions with data.
• Data identification: it is crucial to determine if data needs to be protected or not. Data can be defined as sensitive either done manually by applying rules and metadata, or automatically via techniques like machine learning.