A Real-Time Monitoring Framework for Privacy in AdTech
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This praxis presents the design, implementation, and evaluation of a Real-Time Compliance Monitoring Tool (RTCMT) developed to mitigate regulatory risks and strengthen data governance in cross-device tracking systems within AdTech. By identifying unauthorized data transfers in near real time, the RTCMT aims to help organizations avoid costly non-compliance penalties and reinforce consumer trust in responsible data handling. Built using normalized datasets derived from the National Provider Identifier (NPI), Federal Election Commission (FEC) filings, and inferred IP geolocation records, the RTCMT simulates a dynamic consent environment where users may grant, revoke, or alter consent over time. The simulation processes over 20,000 records using randomized sequential event pacing, applying rule-based logic to flag non-compliant transfers with streaming delay mechanisms that emulate real-time conditions. Spatial analysis identifies ZIP code-level concentrations of non-compliance, while Poisson modeling evaluates the statistical significance of detected violations. The effectiveness of tool is measured on Accuracy, Precision, Recall and F1 score. Cybersecurity has increasing concern regarding this issue as cross-device tracking increases the attack surface for data exfiltration, privacy violation or regulatory breach. In the fast-paced AdTech ecosystem, where behavioral profiling and real-time bidding operate at high speed and high scale, continuously verifying consent is critical to the unauthorized use of personal data. Real-time methods of monitoring compliance with privacy standards will not only enhance enforcement mechanisms but also set out a cybersecurity-first framework to ensure accountable use and flow of data in the advertising supply chain. The study finds that real-time consent validation can find unlawful transfers, enable timely intervention and provide operational visibility to protect privacy in modern AdTech.
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