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Content Auto Review

Security Requirement Statement​

All user-generated listings and profile content must be auto reviewed and not allowed to list if doesn't pass review.

Control Classification​

  • Timing Control Category: Preventive
  • Nature Control Category: Technical
  • Status: Identified
  • Date Identified: 2025-10-29
  • Date First Implemented: TBD
  • Date Last Reviewed: 2025-10-29
  • Date Retired: N/A

Draft-to-Published Workflow Foundation

  • ItemListing domain entity supports 'Drafted' state for content pending automated review
  • Publishing workflow transitions listings from 'Drafted' to 'Published' only after automated validation
  • Domain validation through ItemListing.publish() method enforces permission checks and state transitions
  • CreateListing interface allows saving as draft or publishing with automatic review gates

Content Validation Architecture (To Be Implemented)

  • Text content analysis for profanity, spam, and policy violations using Azure Content Moderator or similar service
  • Image content scanning integrated with existing Azure Blob Storage malware detection infrastructure
  • Automated content scoring with configurable thresholds for auto-approve, manual review, or auto-block decisions
  • Content validation service integration with domain events for audit trails and monitoring

Listing Content Review Pipeline

  • Title, description, and category fields require automated text analysis before publication
  • Image uploads leverage existing Azure Blob Storage security with Microsoft Defender scanning
  • Location and availability information validated against platform policies and geographical restrictions
  • Profile content including user descriptions, location data, and personal information subject to same validation pipeline

Integration with Existing Infrastructure

  • Azure Blob Storage file upload system already includes malware scanning via Microsoft Defender for Storage
  • Existing domain permission system through Passport/Visa architecture supports content validation workflows
  • GraphQL mutation pipeline provides integration points for automated review services
  • Event-driven architecture enables real-time content review notifications and status updates

Manual Review Escalation System

  • Content flagged by automated systems moves to admin review queue for human evaluation
  • Admin dashboard integration displays pending content reviews alongside user and listing moderation
  • Appeal process for content blocked by automated systems follows existing listing appeal workflow
  • Administrative override capabilities for content review decisions with audit logging

Policy Enforcement Framework

  • Configurable content policies for different content types (listings, profiles, messages)
  • Machine learning model integration for evolving threat detection and content classification
  • Automated response actions: auto-approve, require manual review, auto-block with user notification
  • Content policy violation tracking for user reputation and escalating enforcement actions

Compensating Controls​

Multi-Layer Content Validation

  • Automated content analysis combined with user reporting mechanisms for community-driven moderation
  • Manual admin review queue for content flagged by automated systems or user reports
  • Appeal process allows users to contest automated content blocking decisions
  • Content policy documentation provides clear guidelines for acceptable platform content

Infrastructure Security Integration

  • Azure Blob Storage malware scanning prevents malicious file uploads regardless of content review status
  • Content validation occurs before publication, preventing policy-violating content from becoming publicly visible
  • Existing authentication and authorization systems ensure only verified users can submit content for review
  • Domain-layer validation prevents bypass of content review requirements through direct API access

Audit and Monitoring Systems

  • All automated content review decisions logged with confidence scores and reasoning
  • Content review metrics tracked for system performance monitoring and policy effectiveness
  • False positive/negative tracking enables continuous improvement of automated review accuracy
  • Administrative review actions audited for compliance and quality assurance

User Communication and Education

  • Clear content policy guidelines provided during account creation and listing submission
  • Automated content rejection includes specific policy violation explanations
  • Appeal process provides users recourse for incorrectly blocked content
  • Content policy updates communicated to users with grace periods for compliance

Escalation and Override Mechanisms

  • Administrative override capabilities for complex content review cases
  • Escalation procedures for repeated policy violations or edge cases requiring human judgment
  • Content review queue prioritization based on user reputation, content type, and violation severity
  • Emergency procedures for coordinated abuse or platform security threats

Context and Problem Statement​

Platform Safety Requirements The ShareThrift platform requires comprehensive automated content review to prevent publication of harmful, illegal, inappropriate, or policy-violating content. Without automated screening, malicious users can publish dangerous content, scams, spam, harassment, or illegal material that harms community safety and platform reputation.

Scale and Efficiency Needs Manual content review cannot scale with platform growth and user-generated content volume. Automated systems enable rapid content evaluation while preserving human judgment for complex cases, ensuring timely content publication for legitimate users while maintaining safety standards.

Legal and Regulatory Compliance Automated content review supports compliance with platform liability regulations, content moderation requirements, and digital services act provisions. The system must demonstrate proactive content screening capabilities and maintain audit trails for regulatory reporting.

User Experience Balance Content review must balance safety requirements with user experience, ensuring legitimate content publishes quickly while preventing harmful material from reaching the platform. Transparent policies and appeal processes maintain user trust while enabling effective content moderation.

Success Criteria​

SC-5.1: Content Review Coverage and Accuracy

  • 100% of user-generated content undergoes automated review before publication
  • Automated review achieves >95% accuracy for clearly violating content detection
  • False positive rate maintained <5% for legitimate content submissions
  • Content review system successfully prevents publication of policy-violating material >98% of attempts

SC-5.2: Processing Performance and User Experience

  • Automated content review completes within 30 seconds for 95% of content submissions
  • Users receive immediate feedback on content review status and any policy violations
  • Legitimate content publishes automatically without user intervention after passing review
  • Review queue processing maintains <24 hour turnaround for manual review cases

SC-5.3: Administrative Efficiency and Control

  • Admin dashboard provides clear visibility into content review queue with priority sorting
  • Administrative override and appeal review capabilities function correctly 100% of time
  • Manual review decisions integrate seamlessly with automated system recommendations
  • Content review metrics support continuous system improvement and policy effectiveness evaluation

SC-5.4: Policy Enforcement Effectiveness

  • Content policy violations detected automatically before reaching public platform
  • Repeat offenders identified through content violation pattern analysis
  • Appeal process provides fair recourse for incorrectly blocked content with <5% false negative rate
  • Content review system adapts to emerging threats and policy updates within 48 hours

SC-5.5: Integration and Infrastructure Stability

  • Content review system integrates seamlessly with existing Azure infrastructure and domain architecture
  • No degradation of existing malware scanning or file upload performance
  • System maintains 99.9% uptime for content review processing
  • Content review infrastructure scales automatically with platform growth and user content volume

SC-5.6: Compliance and Audit Readiness

  • Complete audit trail maintained for all content review decisions and administrative actions
  • Regulatory reporting capabilities provide required content moderation metrics and documentation
  • Content review system supports compliance with platform liability and digital services regulations
  • Appeal and escalation procedures meet due process requirements for content moderation transparency