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Data Privacy and Security Framework for Digital Advertising Platform
  1. case
  2. Data Privacy and Security Framework for Digital Advertising Platform

Data Privacy and Security Framework for Digital Advertising Platform

lineate.com
Advertising & marketing
Media
Telecommunications

Challenges in Ensuring Data Privacy, Security, and Compliance in Digital Advertising

The client faces increasing consumer concerns over data privacy and regulatory requirements such as GDPR and CCPA, necessitating a comprehensive data security strategy. Managing consent across multiple data partners, implementing emerging privacy standards, detecting ad fraud, and securing application infrastructure are key pain points impacting their ability to leverage consumer data responsibly while maintaining ad performance.

About the Client

A mid-to-large sized digital advertising firm managing vast amounts of user data, requiring robust privacy, security, and compliance solutions to support targeted advertising and consumer trust.

Goals for Enhancing Data Security, Privacy Compliance, and Fraud Prevention

  • Implement a comprehensive data governance framework that manages consumer consent and ensures compliance with GDPR, CCPA, and similar standards.
  • Integrate privacy-preserving solutions such as the Google Privacy Sandbox to align with emerging privacy laws.
  • Develop systems to analyze data trends, detect potential ad fraud, and prevent misuse such as bot traffic and fraudulent impressions.
  • Secure application infrastructure through vulnerability scanning, penetration testing, and continuous monitoring.
  • Establish proactive data orchestration capabilities to connect, analyze, and automatically act on first-party data in real-time, reducing dependence on third-party applications.

Core Functional Capabilities for Privacy-Driven AdTech Security Platform

  • Consent Management Module to oversee data permissions across upstream and downstream partners supporting GDPR, CCPA, and other standards.
  • Integration of emerging privacy solution frameworks such as privacy sandbox implementations.
  • Data trend analysis and fraud detection systems utilizing AI to identify bot traffic, fraudulent clicks, and attribution flaws.
  • Application security tooling including vulnerability scanning, penetration testing, and 24/7 monitoring.
  • Infrastructure security components to safeguard cloud and application environments.
  • Data orchestration layer that automates data collection, processing, and action triggers based on first-party data insights.

Preferred Technologies and Architectural Approaches

Cloud-based security and compliance tools
AI-driven analytics for fraud detection
Privacy sandbox and similar privacy-preserving technologies
Automated deployment and monitoring solutions
Tier 3 support for ongoing security operations

Essential External System Integrations for Compliance and Functionality

  • GDPR and CCPA compliance management systems
  • Privacy Sandbox APIs
  • Ad fraud detection platforms
  • Security vulnerability scanning tools
  • Real-time data processing pipelines

Non-Functional Requirements for System Performance and Security

  • System scalability to handle large volumes of data and high transaction rates
  • Real-time processing with low latency for fraud detection and automated actions
  • Robust security measures to prevent breaches and unauthorized data access
  • High availability with automated failover capabilities
  • Compliance adherence with data privacy laws and standards

Anticipated Business Benefits and Outcomes of the Data Security Initiative

The project aims to significantly reduce ad fraud and improve data privacy compliance, enabling the client to enhance user trust and maximize advertising performance. Expected outcomes include increased accuracy in ad attribution, reduced financial losses from fraudulent activity, and sustained compliance with evolving privacy laws, leading to improved market competitiveness and consumer confidence.

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