Team Overview: 

The Digital Insider Risk (DInR) Department protects The Jones Financial Companies, and its subsidiaries (collectively, "the Firm") against risk stemming from user digital activity.  The Data Loss Prevention Platform Engineer will be responsible for developing, testing, and optimizing Data Loss Prevention processes, across various DLP tools, ensuring the protection of client and Firm data. You will work closely with security analysts, engineers, and other IT professionals to enhance our security posture through the development of advanced detection and enforcement rules, and the integration of endpoint DLP for cohesive monitoring, alerting, and investigation capabilities.  
 
What You'll Do: 

Sensitive Information Types and Label Policies 

Creation and Refinement: Develop sensitive information types within Microsoft Purview and other technologies to align with the firm’s data security objectives and compliance standards. Label Management: Build, test, and deploy sensitivity labels and label policies to protect sensitive information effectively. 

System Prerequisites 

Platform Readiness: Ensure all systems enforcing DLP meet platform prerequisites, including software updates and configuration requirements. 

Monitoring and Policy Adjustment 

System Impact Monitoring: Continuously monitor the impact of DLP policies on systems and applications, making adjustments to rules or deployment strategies as necessary. Policy Optimization: Regularly tune detection rules and DLP enforcement policies based on DLP alert analysis to minimize false positives and enhance effectiveness. 

Integration and Standardization 

Cross-Platform Integration: Integrate various DLP tools with other insider risk platforms for cohesive monitoring, alerting, and investigation capabilities. Sensitive Information Type Standardization: Align sensitive information type libraries across various DLP tools, including endpoint DLP, Zscaler, MS Purview, and Proofpoint, to create a unified approach. Data Protection: Develop alternative protection actions for sensitive data with deep experience with message encryption and redaction mechanisms. AI Governance: Work with cross functional teams to develop protections for the safe usage of AI platforms through the use of DLP to limit sensitive data exposure to LLM data sets. 

Exact Data Match and Rule Development 

Exact Data Match Catalog: Develop and maintain an exact data match (EDM) catalog and integrate it with DLP policies to refine data detection accuracy. Rule Refinement: Collaborate with stakeholders to develop advanced detection and enforcement rules tailored to our various needs. Trainable Classifier: Create and maintain positive and negative trainable classifier libraries to integrate machine learning into DLP detection logic. 

Deployment Leadership 

Endpoint DLP Deployment: Lead the implementation of Endpoint DLP, including coordinating patch management, software deployment, and exclusions to maintain system readiness. Automation Preparation: Prepare for automated maintenance of patch levels and system compliance checks. 

User Impact Management 

Impact Mitigation: Act as the primary resource to address user impacts arising from DLP implementation, providing clear communication and support. Training and Guidance: Offer training and best practices with documentation to helpdesk and cross functional teams to ensure smooth adoption and operation of DLP tools.