As GenAI features are embedded directly into mobile apps, the model, prompts, and on-device inference flows become part of the app’s attack surface.
Security teams need to assume adversarial conditions on real devices, including tampering, instrumentation, hooking, repackaging, and automated abuse.
Protecting GenAI on mobile therefore requires strong runtime integrity, resistance to reverse engineering, and controls that reduce abuse without breaking user experience.
On-device GenAI is moving from experimentation to mainstream product roadmaps, including how endpoint devices will run small language models locally, when workloads should shift between device and cloud, and why security becomes a prerequisite as adoption scales.
Promon note
Promon is mentioned in the report as part of an industry discussion.
“Endpoint devices will run small language models locally, blending with cloud resources when needed, to deliver on-device GenAI. But the product leaders must split workloads, use model compression and significantly improve security to win here.”
Gartner, Inc., Emerging Tech: The Future of On-Device GenAI, Ben Lee, 03 Oct 2025.
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