Securing the mobile business is at a breaking point. Cyber teams need to release more defenses than ever into the mobile channel. At the same time, Engineering teams, with AI-driven automation, are pushing out new releases faster than ever. The result? A shrinking window to implement the needed security layers in mobile apps effectively.
This is not a hypothetical challenge. It’s the new reality for businesses trying to protect the mobile channel and work. Cyber teams must address the increasing diversity, scale, and sophistication of attacks against the mobile business. AI-Native Defense Platforms—security solutions that embed AI into every part of the defense lifecycle, delivering more defenses with less work across a wide variety of attack vectors—are the only way to do this at DevOps speed.
The Forces Reshaping the Security Equation
Attackers know that mobile is fragmented and that mobile users rely on mobile devices for all parts of their lives, from saving to shopping to entertainment. They use this dependency to infiltrate every facet of the mobile experience. Simply put, attackers get users to install malware, fall for social engineering scams, take advantage of outdated operating systems, exploit brands and businesses that fail to defend their mobile apps, and ship malware straight from the factory floor. With AI-powered fraud networks and bot automation, attackers can adapt in hours, bypassing static security defenses and launching massively scaled attacks in minutes.
Meanwhile, DevOps is pushing for increased automation across the development lifecycle and has already moved the quarterly release into weekly and bi-weekly release schedules. How do cyber teams keep up with these forces? Cyber teams can’t slow the business down, and they can’t afford to fall behind.
Traditional Mobile Defense Strategies Can’t Keep Up
The days when a single security layer could protect the mobile channel are gone. Today, mobile brands and organizations are, at a minimum, trying to implement three to four layers of defense for their Android & iOS apps and users: deepfake detection, anti-fraud, anti-bot, and RASP-security. The brick wall they encounter in this journey is the delivery model — multiple, competing Software Development Kits (SDKs) and trying to cram multiple security SDKs into an increasingly smaller implementation window.
Where the Math of Security SDKs Breaks Down
After you’ve paid for the license of any security SDK – from RASP to Anti-Bot, Anti-Fraud, Behavioral Biometrics, etc. – brand and business face the real cost of defending the mobile business this way. Each security SDK introduces learning curves, dependencies, trial-and-error configurations, and manual workarounds. Security SDKs are not designed to work with other security SDKs, and many of them have competing functionality. Even when one SDK offers some automation—through CLIs, scripts, or wrappers—this automation doesn’t extend to other SDKs and breaks or interferes with other SDKs, forcing engineering teams to deal with the fallout.
Engineers also have to learn and maintain each and every security SDK used in their mobile apps indefinitely. This means that with every App, OS, or SDK update, engineering teams must relearn the SDK, rework the integrations, update the configurations, resolve new conflicts, test compatibility, and troubleshoot regressions. This creates fatigue and drains engineering time and resources. On top of this, every time an experienced developer leaves, SDK knowledge is lost. New hires must learn SDK implementations from scratch, further delaying releases and increasing risk.
Here is an AI-generated calculation of the average costs of implementing multiple security SDKs into one application.
| #Of Security SDKs | Engineering Time Required Per Year | # Of Full-Time Engineers Needed | Estimated Cost in U.S.$ |
| 1 SDK | 19–41 months | 1.5–3 engineers | $229K – $495K |
| 2 SDKs | 26–52 months | 2–4 engineers | $312K – $624K |
| 3+ SDKs | 34–69 months | 3–6 engineers | $408K – $828K |
To complicate matters, security SDKs are not integrated platforms. Security SDK vendors don’t design their tools to work together. In fact, many of them offer overlapping detection and enforcement that conflict with each other rather than forming a cohesive defense. For example, every anti-fraud, anti-bot, and RASP SDK includes its own Android root and emulator checks. With each SDK trying to enforce its own policy at the same time, race conditions emerge, leading to app crashes, ANRs (Application Not Responding errors), and broken user flows. Even if a brand or business had infinite budget, it’s not the cost that kills the defense project. It’s the time and work – it’s too much, too slow, and too hard.
The Power & Promise of AI-Native Defense in Mobile
AI-Native security isn’t about bolting AI onto existing tools—it’s about fundamentally rethinking how security is built and delivered. AI-Native Defense Platforms eliminate the challenges above by:
- Embedding any number of security layers directly into mobile apps without requiring manual SDK integration (to eliminate learning curve).
- Using AI to automatically orchestrate and create a cohesive defense posture between security layers (to eliminate race conditions and conflicts).
- Generating code-level security protections without developer intervention (to eliminate resource dependency).
- Automatically adapting security defenses dynamically as updates are generated in the App, the OS, or the threats themselves (to eliminate maintenance).
Instead of forcing engineering teams to work hard to stitch together multiple point products, addressing conflicts, race conditions and the fallout of each and every update, AI-Native platforms can deliver the multi-layered defense – across anti-fraud, anti-bot, anti-ATO, anti-cheat, anti-malware and RASP security – into Android & iOS applications automatically, eliminating work for the engineering team. AI-Native platforms also have the advantage of automating other parts of the defense lifecycle from certification, monitoring, incident response, and end-user remediation.
With AI-Native defense, the math works (again) and cyber teams can be confident that they can keep up, beat the attackers, and make their engineering teams happy.
Sign up for your personalized demo to see the power and efficiency of the Appdome AI-Native Defense Platform.
