The growth of apps over the past decade has changed the digital world a lot. Now people can do things on their phones like manage finances take care of their health communicate and share data in real-time. With more apps comes more risk of cyberattacks. Hackers see apps as easy targets for complex attacks. Old security methods, which rely on rules and reacting to threats are not enough to deal with today’s evolving threats.

Mobile apps are especially vulnerable because of data storage, weak authentication and unprotected API endpoints. They're also open to network attacks like Man-in-the-Middle and session hijacking. The variety of mobile platforms device fragmentation and user behavior make it hard to implement security. So, we need adaptive and proactive security solutions to tackle these challenges.

This research paper looks at the cybersecurity challenges for mobile apps and suggests a new security framework using Artificial Intelligence (AI) and Machine Learning (ML). We explore how AI systems can analyze user behavior data find anomalies detect threats and automate responses in real-time. Our approach includes layers of security like data encryption, secure

authentication, network protection and AI-based threat detection.

We also examine how AI can improve mobile app security through analysis, predictive modeling and anomaly detection. By comparing security systems with AI-enabled approaches we find significant improvements in detection accuracy response time and scalability. AI not strengthens existing security but also shifts cybersecurity from reactive to proactive.

This research helps us understand mobile cybersecurity challenges and shows how AI-driven solutions can work in real-world scenarios. We aim to provide insights, for developers, researchers and organizations to design resilient and intelligent mobile apps that can withstand emerging cyber threats.

Keywords— Cybersecurity, Mobile Applications, Artificial Intelligence Machine Learning, Data Security, Threat Detection, Mobile Threats.