BLOG
BLOG
As 2025 comes to a close, it’s worth pausing, not to slow down, but to reflect on how rapidly the mobile security landscape is evolving and what that evolution now demands from all of us.
This year reinforced something we have long believed at Appknox: security can no longer be an isolated activity or a late-stage control. As mobile applications become more interconnected, AI-enabled, and globally distributed, security must operate continuously and at scale, without slowing teams down.
What changed in 2025 is not just the volume of mobile applications or the sophistication of threats. What changed is the expectation. Security teams are no longer asked to simply find vulnerabilities. They are expected to deliver confidence consistently, measurably, and at speed.
In 2025, Appknox helped customers scan 38,912 mobile applications, representing nearly 80 percent year-over-year growth in platform usage. This growth is not just a company milestone; it reflects a broader industry shift.
Security testing is shifting from periodic assessments to an embedded, repeatable practice across development and release cycles.
Across these applications, Appknox identified 346,874 vulnerabilities spanning customer portfolios. While the volume itself is notable, the nature of these findings is far more instructive.
The most common issues remained foundational:
These are not edge cases. They are systemic patterns that persist as development velocity continues to outpace manual review processes.
Among these findings were 8,412 critical-severity issues. With automated detection and prioritization, teams were able to surface these high-impact risks 60–70 percent faster than traditional manual approaches.
This comparison is based on widely cited industry benchmarks, where manual mobile application security reviews typically take several days, or even weeks, per release, depending on application complexity and reviewer availability. Automated testing enables near-immediate identification during development and CI pipelines, dramatically shrinking the window between vulnerability introduction and detection.
The conclusion is clear: speed is no longer a convenience in security; it is a prerequisite.
|
Metric |
2025 Result |
Why it matters |
|
Mobile applications scanned |
38,912 apps |
Demonstrates real-world scale across enterprise portfolios |
|
Year-over-year platform growth |
~80% |
Signals industry shift toward continuous mobile security |
|
Total vulnerabilities identified |
346,874 |
Highlights systemic security patterns across ecosystems |
|
Critical-severity vulnerabilities |
8,412 |
Represents high-impact risks requiring immediate action |
|
Detection speed improvement |
60–70% faster |
Shows how automation reduces exposure windows |
These numbers reflect not just growth, but a broader shift in how enterprises operationalize mobile security at scale.
|
Observed pattern |
What it indicates |
|
Repeated network misconfigurations |
Security gaps persist despite tooling |
|
Weak transport-layer protections |
Encryption is still inconsistently enforced |
|
Missing runtime defenses |
Pre-release testing alone is insufficient |
|
Insecure data handling |
Privacy risks remain systemic |
|
Fragile cryptographic usage |
Secure-by-default is still not the norm |
These findings reflect systemic challenges, not isolated engineering mistakes.
In 2025, we shipped 17 product releases, each guided by a simple principle:
Security must adapt to how teams build and ship mobile applications today, not how they worked in the past.
Several updates marked meaningful progress toward that goal.
Privacy Shield with AI-based PII detection pushed privacy risk identification earlier in the lifecycle, giving teams automated visibility into sensitive data exposure before apps reached production or app stores.
ML model detection in SBOM acknowledged a rapidly emerging reality: AI and machine-learning components are now part of the mobile supply chain and must be inventoried, governed, and assessed alongside traditional dependencies.
Auto discovery in Storeknox addressed a persistent organizational blind spot by continuously identifying unknown, duplicate, or newly published app listings across marketplaces.
Expanded geo coverage for drift detection reflected the complexity of global app distribution, enabling teams to detect store-level changes and inconsistencies across regions.
The AI reporting engine transformed raw security findings into clear, action-ready insights, helping engineering and security teams focus on what actually matters.
Taken together, these enhancements represent more than feature expansion. They reflect our continued focus on reducing cognitive load for teams while expanding security coverage across the entire mobile lifecycle.
|
Appknox product advancement |
Problem it addresses |
|
AI-based PII detection |
Late discovery of privacy risks |
|
ML model detection in SBOM |
Invisible AI supply chain risk |
|
App auto-discovery |
Unknown or unmanaged app listings |
|
Geo-level drift detection |
Regional inconsistencies and exposure |
|
AI reporting engine |
Overwhelming, low-signal findings |
Each release was designed to remove friction, not add process.
Our progress in 2025 was reinforced by strong customer outcomes:
Trust is earned through reliability, clarity, and measurable impact. We remain focused on delivering all three.
|
Legacy security model |
AI-native security model |
|
Point-in-time scans |
Continuous evaluation |
|
Static rules |
Adaptive intelligence |
|
Vulnerability lists |
Risk-driven insights |
|
Manual triage |
Context-aware prioritization |
|
Human-scale workflows |
Machine-speed systems |
As we look toward 2026, one shift stands out clearly:
Security platforms must become AI-native by design, not AI-assisted as an afterthought.
The scale and complexity of modern mobile ecosystems, frequent releases, AI-powered features, expanding supply chains, and global distribution have fundamentally outgrown human-centric security workflows.
The next phase of security evolution is not about replacing people.
It is about building systems that think, adapt, and learn at machine speed.
We see three defining changes shaping the year ahead.
AI-native platforms will embed intelligence at their core. Models will not simply accelerate testing; they will understand application behavior, risk patterns, and change over time. Testing depth and focus will continuously adapt based on real-world signals rather than static rules.
Security must move beyond vulnerability lists. AI-native systems will correlate findings across scans, releases, stores, and environments to deliver prioritization, impact analysis, and clear guidance on what matters most, and why.
As development velocity increases, security must provide ongoing assurance. In 2026, confidence will come from knowing that every code change, dependency update, store modification, and configuration shift is continuously evaluated—without requiring constant manual oversight.
At Appknox, our focus for the coming year is clear. We are building AI-native mobile security that scales with modern software delivery while remaining transparent, explainable, and actionable for teams.
2025 made one thing unmistakably clear: mobile security is at an inflection point.
The challenges are well understood. The risks are escalating. And the expectations placed on security teams have never been higher.
What comes next is not incremental improvement.
It is a shift in how security is delivered and experienced.
We are grateful to our customers, partners, and team for helping us push the industry forward. As we move into 2026, we remain committed to building security that works at the speed of modern mobile development.