Abstract
This publication discloses twelve platform-scale security intelligence inventions for web, mobile, and AI agent protection. Core platform inventions (B1–B4) include: privacy-preserving cross-customer threat intelligence via a two-layer alias architecture with k-anonymity; SDK-embedded quantized ML inference (<100KB, INT8, sub-5ms) with knowledge distillation and signed over-the-air updates; consent-scoped alias lifecycle management for automatic GDPR/CCPA compliance; and multi-hop AI agent trust chains with configurable attenuation and cascading revocation. Advanced security infrastructure (B5–B10) covers IDOR detection via per-alias resource access baselines, runtime dependency integrity verification via Ed25519-signed manifests, AI agent output exfiltration detection via semantic and entropy analysis, multi-level TLS certificate pinning with safe rotation and air-gapped fallback, constant-time authentication response orchestration, and shadow agent detection via distributed registry and behavioral fingerprinting. Predictive infrastructure (B11–B12) includes DNS hijacking detection via multi-path resolution consensus with challenge-response verification, and a Bayesian predictive attack trajectory engine with pre-emptive countermeasure deployment and outcome feedback learning.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Ndungu, Duncan Ndegwa, "Privacy-Preserving Cross-Customer Threat Intelligence Platform with Embedded ML, Predictive Attack Trajectory, and Multi-Agent Trust Management for Web, Mobile, and AI Agent Protection", Technical Disclosure Commons, (April 24, 2026)
https://www.tdcommons.org/dpubs_series/9908