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02/08/2026 11:53 am
TL;DR: AI security platforms still miss telemetry from mobile applications, even though those apps generate signals from SDKs, permissions, data flows and infrastructure connections that affect fraud, privacy and release risk, according to NowSecure. As agentic AI security matures, mobile visibility becomes part of the control surface, not a niche AppSec concern.
NHIMG editorial — based on content published by NowSecure: AI security platforms lack visibility into mobile application behavior
By the numbers:
- 85% of mobile apps contain at least one security flaw.
- 70% have the potential to leak personal data.
Questions worth separating out
Q: How should security teams govern mobile apps that sit inside identity workflows?
A: Treat mobile apps as part of the trust path, not just the user interface.
Q: Why do mobile applications create blind spots for AI security platforms?
A: Because many AI security tools correlate logs from infrastructure, endpoints and cloud services, while the key behavior happens inside the compiled app on a device.
Q: What do security teams get wrong about mobile malware and identity risk?
A: They often stop at authentication and overlook what happens after login.
Practitioner guidance
- Inventory mobile application trust dependencies Build a live inventory of embedded SDKs, third-party libraries and external service endpoints for every high-risk mobile app.
- Scan the compiled mobile binary before release Add testing for the final compiled mobile binary to CI/CD and app-store release gates.
- Correlate mobile signals with identity telemetry Feed app-originated signals such as permissions, domains, data flows and vulnerability findings into SOC and IAM correlation workflows.
What's in the full article
NowSecure's full article covers the operational detail this post intentionally leaves for the source:
- Mobile application risk intelligence examples, including how app signals are packaged for third-party security platforms
- The Agentic AI Data Partner Program context and integration model for vendors consuming mobile telemetry
- Examples of the mobile signals security teams can extract from compiled binaries and runtime behavior
- How mobile intelligence can be used to support AI governance and third-party risk workflows
👉 Read NowSecure's analysis of mobile app intelligence for AI security visibility →
Mobile app intelligence: what it means for AI security teams?
Explore further
02/08/2026 1:01 pm
Mobile app visibility is now an AI security requirement, not a niche AppSec add-on. AI-driven platforms can only reason over telemetry they can see, and mobile applications increasingly sit at the front door of enterprise services. That means the app layer is now part of the security evidence base for identity, transaction integrity and AI-assisted workflows. Practitioners should treat mobile telemetry as a missing control surface rather than a reporting enhancement.
A question worth separating out:
Q: How can organisations tell whether mobile app intelligence is improving control coverage?
A: Look for better linkage between app behavior and security outcomes. If analysts can trace an unusual mobile connection back to a specific SDK, identify data flows to third-party infrastructure and match that to identity or cloud events, the programme has gained control coverage. If not, the organisation still has a visibility gap at the application layer.
👉 Read our full editorial: Mobile app intelligence is closing an AI security visibility gap
