Implementing cloud-based data loss prevention to SolarPower company

• Main Author: Inocencio, Michael Angelo P.
• Format: text
• Language: English
• Published: Animo Repository 2022
• Subjects: Data protection; Computer security; Computer networks—Security measures; Computer Sciences; Information Security
• Online Access: https://animorepository.dlsu.edu.ph/etdm_comtech/14
• Institution: De La Salle University

Organizations often have security protections and controls for threats, malware, vulnerabilities, malicious network activities and detections from indication of attacks but little focus on data protections. The leakage and exfiltration of sensitive data whether financial records, personal identifiable information, company records or state secrets represents one of the most damaging threats that can be carried out by malicious users inside or outside. Especially the rising and evolving of technologies from mobile to cloud infrastructure and applications. Any leakage could seriously diminish the confidentiality, integrity and availability of any organization's data. That is why the need to implement Data Loss Prevention systems becoming a must in any Organizations, not just for a traditional protection but with the capability to complement and implement in cloud environment. Cloud Data Loss Prevention is the solution that can help protect sensitive data from any leakages, detect, block any unauthorized data transfers thru any applications or technologies. This project aimed at designing, configuring, implementing a cloud-based data loss prevention system for SolarPower company. Using a framework-based approach in selecting the right solutions in accordance with international standards, best practices on what to consider in a data loss prevention system. The project also aimed to mapped out what are the important technologies and techniques, architecture setup and network configuration to mitigate any potential threat for data leakages. Keywords: Data Loss Prevention, Data Leakage, Cloud-based Data Loss Prevention, Data Protection, Data Loss