The rapid proliferation of digital pathology has created an urgent need for integrated, scalable, and secure platforms capable of supporting the full lifecycle of whole-slide image (WSI) analysis — from quality assessment through collaborative annotation to AI model deployment. Existing tools address these requirements in isolation, creating fragmented workflows that impede clinical adoption and AI development. Here we present A!Path, a modular ecosystem comprising three synergistic components: (1) A!magQC, a fully automated AI-assisted quality control pipeline assessing five image quality metrics across H&E and multiplex fluorescence modalities; (2) A!HistoClouds, a unified annotation and inference platform that combines cloud-based scalability with local server flexibility, implementing a three-phase closed-loop pathologist-AI interaction workflows, Segment Anything Model (SAM)-based annotation, multi-user collaborative workflows, integrated AI inference (A!Prostate), and project analytics on a deployable backend (A!Server); and (3) A!Secure, a cryptographic security layer for WSI protection, providing application-layer encryption, policy-based access control, streaming tile decryption, and format-aware protection of compressed, pyramid-structured pathology images. A!HistoClouds is deployable on both cloud infrastructure and institutional local servers, enabling organizations to select the deployment model best suited to their data governance requirements without sacrificing platform capability. Validation across a cohort of 302 prostate tissue specimens demonstrated that A!magQC achieved greater than 95% agreement with expert visual quality assessment, while A!Secure-protected WSI regions yielded a low PSNR of 7.42 dB and SSIM of 0.0289 relative to the original images, indicating strong visual obfuscation of diagnostically relevant content. A!Path provides a coherent, deployment-flexible foundation for clinical diagnostic AI development and multi-centre digital pathology collaboration.