I’ll provide a comprehensive security framework addressing all three focus areas:
Role-Based Security Implementation:
Leverage JDE’s native security model as your foundation. Create security groups that align with your pricing access requirements: PRICING_ADMIN (full access), PRICING_ANALYST (filtered competitive data), SALES_MGR (territory-level access), and SALES_REP (customer-level access). In BI Publisher, design your data models to incorporate user context through bind variables that query the user’s security profile.
Implement dynamic row-level security using SQL predicates that filter based on the current user’s role. For example, your pricing report query should include conditions like: WHERE (user_role = ‘PRICING_ADMIN’) OR (user_role = ‘SALES_REP’ AND customer_id IN (SELECT customer_id FROM user_customer_assignments WHERE user_id = :current_user)). This ensures that the database only returns data the user is authorized to see, regardless of how they access the report.
Create a centralized security view in your JDE database that consolidates all pricing security rules. This view should join user profiles, territory assignments, customer relationships, and security groups. Your BI Publisher reports query this view rather than implementing security logic in multiple places. When organizational changes occur - territory reassignments, role changes - you update the security view, and all reports automatically reflect the new security model.
Data Masking Strategies:
Implement field-level masking based on user security context. For competitive pricing data, use conditional logic in your BI Publisher templates to display different formats based on role. High-sensitivity fields like exact competitor prices should show as percentage ranges for sales users: <?if:$USER_ROLE='SALES_REP'?>Competitor price: <?round(($COMP_PRICE / $OUR_PRICE - 1) * 100, 0)?>% below standard<?else?>Competitor price: $<?COMP_PRICE?><?end if?>.
For customer-specific pricing agreements, implement progressive disclosure. Sales reps see only their assigned customers’ pricing. Pricing analysts see aggregate customer pricing trends without customer identifiers. Only pricing directors see the complete customer-to-pricing mapping. Use BI Publisher’s data model to create separate queries for each security level, with the template automatically selecting the appropriate query based on user role.
Consider implementing time-based data decay for highly sensitive competitive intelligence. Historical competitive pricing data (older than 90 days) might be shown in aggregate form even to pricing analysts, while recent data remains restricted to pricing directors. This balances analytical needs with security concerns.
Audit Logging and Compliance:
Enable comprehensive audit logging for all pricing report access. Configure BI Publisher to log every report execution, including user ID, timestamp, report parameters, and data scope accessed. Store these logs in a separate audit table that’s write-only for report users - even administrators shouldn’t be able to modify audit history.
Implement report watermarking that embeds user identification and access timestamp directly in the report output. For PDF reports, use BI Publisher’s PDF security features to prevent editing and add a visible watermark with user ID and “CONFIDENTIAL - [User Name] - [Timestamp]” on every page. This creates accountability and discourages unauthorized sharing.
For the most sensitive pricing analytics, implement session recording. When users access competitive pricing dashboards, log not just that they accessed the report, but which specific data elements they viewed or filtered on. This detailed audit trail is critical for compliance and investigating potential data leaks.
Create automated compliance monitoring. Schedule regular reports that analyze the audit logs for anomalous access patterns - users accessing data outside their normal scope, bulk exports of competitive data, access from unusual IP addresses, or access during non-business hours. Alert the security team when suspicious patterns are detected.
Finally, implement a data classification framework. Tag all pricing reports with sensitivity levels (Public, Internal, Confidential, Restricted) and enforce controls based on classification. Restricted reports should require secondary authentication, have export disabled, and include automatic expiration of delivered copies. This systematic approach ensures consistent security treatment across all pricing analytics.