Data Security Expert (DLP)

Skills

About the Role

You will design and implement the company-wide data security scheme under the new financial system, and build advanced detection capabilities across DLP, data encryption, and data masking. You will continuously validate data security rules and models, and handle incident identification, response, investigation, and forensics. You will improve risk identification by refining detection rules, building data asset maps, and using graph detection to trace data flows and spot sensitive data exfiltration risks. You will also explore and apply AI and LLM techniques to create AI-driven detection, triage, and automated response solutions.

Requirements

• Major in information security, network security, or computer science, with 8-10 years of relevant data security experience
• Hands-on experience with endpoint and email data loss prevention (DLP)
• Familiarity with the data security lifecycle, including its risk points and key objectives at each stage
• Familiarity with common security offense and defense techniques, experience building security operations, managing data security risks, and strong sensitivity to data exposure risks
• Familiarity with data security products and detection methods such as DLP, UEBA, sensitive data identification, data encryption/decryption, and secure data sharing
• Experience with enterprise-level endpoint data protection solutions; endpoint security experience, familiarity with macOS and Linux, and hands-on data threat modeling experience are a plus
• Experience with enterprise-level big data analysis tools such as Flink, Hive, Spark, ElasticSearch, and graph technologies; hands-on security data mining, analytics, or threat intelligence analysis experience is a plus
• Strong logical thinking and communication skills, with a solid understanding of compliance and legal considerations

Responsibilities

• Design and implement the company-wide data security scheme under the new financial system
• Develop and enhance data security detection capabilities, including DLP, data encryption, and data masking
• Continuously validate data security rules and models, and follow up on data security incident identification, response, handling, investigation, and forensics
• Improve data security risk identification by optimizing detection rules and models, building data asset maps, and using graph detection to trace data flows and identify sensitive data exfiltration risks
• Explore and apply AI and LLM techniques to enhance security detection and response, and build AI-driven detection, triage, and automated response solutions