How AI Is Changing Mobile Application Risk

Generative AI is changing not only how software is built, but also the scale and speed at which organizations need to understand and manage application risk.

Developers increasingly rely on AI systems to write code, debug applications, generate tests, and accelerate development workflows. At the same time, the rise of AI-assisted, prompt-driven coding, often described as "vibe coding," and no-code or low-code platforms is lowering barriers to software creation. Individuals and teams with limited technical resources can now prototype, modify, and in some cases deploy applications far more quickly than before.

This shift has the potential to drive significant innovation. Faster development cycles can increase productivity, reduce costs, and make software development more accessible. Small teams can build products that previously required far greater resources, and organizations can bring new applications to market more quickly.

However, these changes also raise important questions for security teams. The discussion around AI and cybersecurity often focuses on AI-generated malware, automated phishing campaigns, or other malicious uses of the technology. These concerns are valid, but they are only part of the security impact of generative AI. A more immediate challenge may be that AI is changing the assumptions that have traditionally underpinned application risk management.

Software Is Becoming Easier to Create

Historically, developing software required a meaningful investment of time, expertise, and resources. Building an application often involved specialized technical knowledge, software engineering experience, and a development team capable of designing, testing, and maintaining the resulting product. While not every application was secure, the process itself created natural constraints on how much software could be produced and how quickly it could be released.

Generative AI is changing that equation. Much of the discussion around AI-assisted development focuses on productivity. Developers can generate code more quickly, accelerate testing, and reduce time spent on repetitive tasks. Those capabilities are certainly important, but the larger implication is that AI is not only changing how software is built. It is also changing who can participate in building it.

Individuals with limited software engineering experience can now create applications using natural language prompts, low-code platforms, and AI-powered development tools. Small teams can build products that previously required significantly greater resources, and ideas that once remained prototypes can be turned into functioning applications in a fraction of the time.

This democratization of software development has clear benefits. Lower barriers to entry can foster innovation, increase competition, and expand access to technical creation. However, it also changes the assumptions that have traditionally underpinned application risk management. Security review processes evolved in a world where software creation was constrained by expertise, cost, and development capacity. As those constraints diminish, organizations may find themselves evaluating a growing volume of applications created by a broader and more diverse set of developers, often with limited visibility into how those applications were built, which dependencies they rely on, or what third-party components they contain.

More Software Means More Risk to Evaluate

Generative AI can help write code, but it does not eliminate many of the challenges that have historically contributed to application risk. AI-generated code may still contain vulnerabilities. Developers may unknowingly introduce insecure implementations. Applications often rely on third-party libraries, open-source packages, and software development kits (SDKs) that introduce their own dependencies and risks.

One of the most significant impacts of AI-assisted development may be the acceleration of software reuse and component assembly. Modern software development already depends heavily on third-party libraries and frameworks. As AI systems generate code based on patterns learned from existing software, questions around provenance, security, and dependency management become increasingly important.

The result is not necessarily more malicious software. Rather, it is a software ecosystem that may become larger, more complex, and more difficult to evaluate. Organizations may find themselves managing a growing volume of applications built on layers of third-party components, many of which are not immediately visible to end users or security teams.

From a security perspective, that lack of visibility creates challenges regardless of whether an application was written entirely by a human developer, generated primarily with AI assistance, or built through some combination of both.

These challenges apply across software ecosystems, but they are particularly relevant in mobile environments. Mobile applications have direct access to sensitive device capabilities, personal information, authentication mechanisms, and enterprise resources. As the volume of mobile software continues to grow, understanding application risk becomes increasingly important for organizations seeking to protect both users and corporate data.

Why Mobile Applications Deserve Special Attention

Mobile applications occupy a uniquely trusted position within both personal and enterprise ecosystems. They often have access to sensitive device capabilities, including location services, cameras, microphones, contact lists, authentication flows, platform-protected resources, and corporate data.

As a result, application risk is not limited to whether an app contains malware. Organizations must also consider questions such as:

• What permissions does the application request?

• What third-party SDKs are embedded within it?

• How is user data collected, processed, and shared?

• Are vulnerable components present?

• Does the application's behavior align with organizational security and privacy expectations?

A mobile application may be functional, popular, and entirely non-malicious while still introducing meaningful security or privacy risks. In many cases, those risks originate not from a single vulnerability but from the cumulative effect of permissions, dependencies, data collection practices, and software supply chain decisions.

This distinction becomes increasingly important as AI changes how software is created. Much of the discussion around AI and cybersecurity focuses on adversarial use cases such as AI-generated malware, automated vulnerability discovery, or large-scale phishing campaigns. While those concerns deserve attention, they can sometimes obscure a broader shift already underway.

Generative AI does not need to create malicious software to create new security challenges. If AI enables more software to be developed, modified, and deployed at a faster pace, organizations will face increasing pressure to evaluate application risk at scale. The challenge is not simply identifying malicious applications. It is understanding the growing number of applications that fall between obviously safe and obviously malicious.

Applications can introduce meaningful risk through vulnerable dependencies, excessive permissions, risky SDKs, insecure configurations, or problematic data handling practices, even when there is no malicious intent behind their development. As software ecosystems grow larger and more dynamic, maintaining visibility into those risks becomes increasingly difficult.

For security teams, the challenge is less about determining whether software was created by a human or an AI system and more about understanding the security and privacy implications of the software itself.

Rethinking Application Risk Management

Generative AI is unlikely to slow down software development. If anything, it will continue to accelerate it. The question, therefore, is not whether organizations should embrace AI-assisted development—many already have—but how security and governance practices must evolve in response.

As software creation becomes faster and more accessible, organizations will need scalable ways to understand application behavior, evaluate software risk, and identify potential security concerns before they impact users or enterprise environments.

This will require greater visibility into application composition, software supply chains, permissions, and runtime behavior. It will also require security teams to rethink assumptions about how software is developed and where risk originates.

The future of application security may be defined less by preventing software from being created and more by improving an organization’s ability to understand the software already operating in its environment.

Understanding Application Risk in an AI-Accelerated World

As AI continues to transform software development, application risk management will become increasingly important.

Organizations need visibility not only into the devices accessing corporate resources, but also into the applications operating on those devices and the risks they introduce.

This is one of the reasons iVerify and NowSecure are working together to provide organizations with broader visibility into mobile risk. By combining device intelligence with application risk intelligence, security teams can make more informed decisions about the technologies operating within their environments.

Learn more about how iVerify and NowSecure work together on our Better Together page.