Abstract

The objective is to explore the integration of AWS’s image analysis tools, particularly Amazon Rekognition, into Pega systems for automated content moderation. This involves a methodology that reviews AWS’s capabilities in image analysis, examines Pega’s system architecture, and assesses how AWS services can be applied within Pega for content moderation purposes. Key findings from this investigation include insights into the efficiency,  accuracy, and scalability of this integration. Specifically, the paper highlights how automating content moderation with AWS tools within Pega systems significantly reduces the need for manual moderation, thereby saving time and resources. It also emphasizes the accuracy of Amazon Rekognition in detecting inappropriate content, which minimizes errors common in human moderation. Furthermore, the scalability of AWS services ensures that the solution can handle varying content volumes effectively. The integration’s impact on operational costs is also analyzed, showing potential reductions due to decreased manual efforts. Lastly, the paper discusses how this integration enhances user experience by maintaining a safer and more engaging digital environment.

Keywords: Digital governance, AWS image analysis, Pega systems, Image recognition, Automation in governance