We detail a platform-scale security intelligence architecture extending credential aliasing to encompass privacy-preserving cross-customer threat correlation, edge-embedded machine learning, and predictive attack trajectory modeling. The system uses a two-layer alias structure to maintain tenant privacy guarantees across shared threat data, while deploying a compact machine learning model within the client SDK to achieve localized, low-latency threat classification entirely within the application boundary. The architecture also introduces multi-hop trust chains for AI agents and probabilistic predictive models to deploy pre-emptive countermeasures against multi-stage attacks. This paper presents the high-level theoretical framework and architectural design. Full technical specifications are outside the scope of this paper. These technologies are subject to pending patent applications.