Enterprises face significant challenges in ensuring GDPR compliance across their extensive data ecosystems, particularly given the stringent requirements and global reach of the regulation. The Trustworthy Language Model (TLM) by Cleanlab emerges as a solution by automatically detecting potential GDPR violations in log files with high accuracy, while also flagging analyses that may be untrustworthy for manual review. This capability is crucial as data collection and processing methods become more sophisticated, increasing the risk of compliance violations. Recent high-profile fines against companies like Meta and Amazon underscore the importance of robust compliance measures. TLM's unique feature of providing trustworthiness scores allows it to prioritize human review efforts and mitigate risks by quantifying uncertainties. In a study analyzing simulated log files, TLM efficiently identified GDPR violations with high confidence and highlighted areas needing human expertise, thereby reducing manual effort while maintaining accuracy. This approach is applicable to various data formats, offering a scalable solution for enterprises to manage compliance while allowing them to innovate and compete effectively.