Runtime Application Self-Protection (RASP) has emerged as a potent defense against mobile application threats. However, is it realistic to consider RASP as a bulletproof shield that can ward off all risks? This perception, often leading to overconfidence, can result in significant risks.

While being a valuable component of mobile application security, its effectiveness is subject to proper configuration and deployment. The necessity to continually assess and verify the security controls implemented by RASP cannot be overlooked. In a dynamic threat landscape, this proactive approach is essential to ensure that RASP remains resilient and capable of addressing the latest challenges.

RASP can be deployed in various ways, including SDK integration, agent-based, and cloud-based deployment. SDK integration is the most tightly integrated deployment type but requires the most development effort. Agent-based deployment is more straightforward to implement but can lead to some performance overhead and may be more susceptible to false positives. Cloud-based RASP solutions are the easiest to deploy and can scale to meet the needs of large and dynamic organizations. In recent years, there has been a growing trend towards the use of AI and ML in RASP deployment. AI and ML can be used to improve the accuracy of RASP detection and to reduce the number of false positives.

The best RASP deployment type for a particular organization will depend on several factors, including the size and complexity of the organization's environment, the specific security needs, and the budget.

For security professionals and Blue teams, understanding the diverse RASP deployment techniques and the features they offer for mobile app security is essential. The table below provides a high-level over view of various RASP deployment models and features.

The table below dives into potential issues and constraints of various RASP deployment models and features used to protect Mobile Applications. Understanding these details helps choose the right RASP solution and ensure it works effectively.

Requires deep understanding of the mobile framework and its implementation, may not be compatible with all versions of the mobile framework

The image below links RASP controls with MITRE ATT&CK Mobile Tactics, a crucial step in enhancing security. It aids organizations in identifying effective RASP controls against specific attacks, facilitating the selection and deployment of the right controls for their mobile applications.

By understanding which RASP controls are most effective against each attack, organizations can make more informed decisions about protecting their mobile applications from cyber threats. In addition to the benefits mentioned above, the mapping of MITRE ATT&CK Mobile Tactics and RASP Controls can also be used to:

• Identify gaps in security coverage
• Evaluate the effectiveness of existing security controls
• Plan for future security investments
• Improve communication and collaboration between security teams

The continuous verification of mobile application security, especially those enforced by RASP, is crucial because attackers and mobile apps are constantly evolving. As attackers find new ways to exploit systems and apps keep updating, we must ensure that our security measures are up to date. Continuous Threat Exposure Management (CTEM) can be a valuable program for helping organizations verify the existence and effectiveness of mobile application security controls enforced by RASP. By continuously monitoring mobile applications for threats and providing insights into the security posture of mobile applications, CTEM solutions can help organizations improve the overall security of their mobile applications.

The NST Assure Continuous Threat Exposure Management (CTEM) platform enables your organization to continually assess the security stance of your mobile applications. It ensures that security measures, like those from RASP and similar solutions, are consistently active and effective in defending against all types of mobile application threats.