AI security threat model:
A comprehensive approach
From threat model to mitigation plan: How to secure AI inside mobile apps
AI is now inside the app—not just behind it. As organizations embed AI directly into mobile experiences, the number and complexity of threats have exploded.
This new paper by Dr. Anton Tkachenko introduces a complete AI security framework for identifying, categorizing, and prioritizing risks across device, model, application, and agent levels. It also shows how Promon’s protection layers map to these threats to defend against real-world AI attacks.
What you'll learn
- The 49 key AI threats targeting on-device and embedded models
- How to classify and prioritize AI risks across your app ecosystem
- Practical defense strategies based on OWASP, MITRE, and NIST frameworks
- How Promon’s Shield, Data Protect, and Code Protect mitigate the most critical AI threats
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Framework migration and mobile app security: Prevent coverage gaps as code changes
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Inside an EMVCo evaluation: What labs test and how to prepare
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Mobile game security in Unity and Unreal: Reducing cheat ROI at runtime
How do attackers exploit runtime logic in Unity and Unreal engine mobile games? What does runtime hardening change for integrity, trust, and revenue?10 Feb 2026
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