Description

Consumer mobile platforms now constitute the primary delivery channel for agentic AI, with global app releases surging 60-104% year-over-year in 2026, driven by AI-assisted development tools. Yet existing agentic governance frameworks, designed for mutable server-side enterprise deployments, cannot address the structural constraints unique to mobile: binary immutability, platform gatekeeper non-determinism, consumer-scale blast radius, ambient agent surface expansion, and regulatory exposure. We present MobileGuard, the first mobile-native governance framework for agentic AI, operationalizing four pillars across the mobile SDLC: Pre-Deployment Quality Contracts (PDQC), Tiered Autonomy Calibration for Mobile (TAC-M), Platform Gatekeeper Simulation and Governance (PGSG), and Ambient Agent Boundary Enforcement (AABE). MobileGuard is validated through three empirical studies. Study 1 presents a governance failure taxonomy derived from 2,847 real-world iOS and Android platform rejection records, identifying 23 failure categories across 6 pillars with 71.3% of failures undetectable by existing frameworks. Study 2 demonstrates a 74.1% Deployment Error Rate reduction (p less than 0.001) against a production mobile SDLC pipeline across three applications, outperforming AGENTSAFE by 45.7 percentage points. Study 3 presents a real-world cross-sectional audit of 942 mobile platform applications (iOS-primary; Android replication in progress) using AS-009, a novel release notes AI disclosure scanner covering both App Store and Google Play governance signals, finding a 4.0% governance signal rate with externally-derived violations observed in enterprise-scale developers including Adobe Inc. and Moleskine Srl, resolving the author-defined scenario circularity concern from Study 2. MobileGuard maps to ISO 42001:2023 and the EU AI Act, and is implemented as an open-source CLI available at github.com/jsingh6/mobileguard.

Files

Additional details