Data Exfiltration

Data exfiltration, in the context of security and cybersecurity, refers to the unauthorized and often surreptitious extraction or theft of data from a computer, network, or system by an individual or entity with malicious intent. This activity typically involves transferring sensitive or confidential information from a victim's environment to an external location, which could be controlled by cybercriminals, competitors, or threat actors. Because it can lead to data breaches, intellectual property theft, or other forms of illegal data exposure, data exfiltration poses a significant security concern. Numerous routes, such as network connectivity, portable storage, email, and cloud-based services, can cause it. Data exfiltration must be recognized and stopped, often necessitating robust security controls, monitoring, and incident response strategies to minimize potential damage.

ThreatNG is a comprehensive solution encompassing External Attack Surface Management (EASM), Digital Risk Protection (DRP), and Security Ratings, with a focus on assessing "Data Leak Susceptibility," strengthens an organization's defense against Data Exfiltration by proactively identifying vulnerabilities and potential data leakage risks within its external digital presence. This proactive approach assists in reducing the risk of data exfiltration and unauthorized data access. It complements and facilitates seamless integration with existing security solutions, particularly data security tools like Data Loss Prevention (DLP) and encryption systems. For instance, when ThreatNG identifies external vulnerabilities that may lead to data exfiltration, it can facilitate a handoff to the organization's DLP system, which can then enhance monitoring and protection measures to prevent data breaches and data exfiltration. This coordinated approach enhances the organization's ability to safeguard sensitive information and prevent data exfiltration in the external digital landscape, reinforcing its overall data security posture effectively.