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.
At a glance
What this is: NowSecure says AI security platforms still lack visibility into mobile app behavior, even though those apps generate security signals from SDKs, permissions, infrastructure links and data flows.
Why it matters: For AI, IAM and security teams, mobile app intelligence matters because incomplete telemetry weakens correlation across identity, device, network and cloud controls.
By the numbers:
- 85% of mobile apps contain at least one security flaw, according to NowSecure.
- 70% have the potential to leak personal data, according to NowSecure.
Context
Mobile application intelligence is the security view you get when you can observe what a compiled app actually does, not just what its backend or device logs suggest. The gap matters because modern security analytics depend on signals, and mobile apps increasingly sit inside identity, transaction and AI-enabled workflows.
For AI security programmes, the issue is not whether mobile apps are important in general. It is that they create telemetry from SDKs, APIs, permissions and third-party infrastructure that many tools never see, which leaves agentic analysis working from an incomplete picture.
Key questions
Q: Where does AI security monitoring fail when mobile apps are part of the workflow?
A: It fails when the telemetry pipeline cannot see what happens inside the compiled mobile application. Security tools may observe traffic, device activity or cloud events, but still miss the SDK calls, permissions and internal logic that generated them. That leaves correlation incomplete and can hide risky data flows or third-party dependencies.
Q: Why do mobile SDKs create supply chain risk for AI and security teams?
A: Because each embedded SDK can add code, network destinations and data handling that were not part of the original control design. If the SDK is vulnerable or overly chatty, it can expose identifiers, telemetry or sensitive data through paths that ordinary endpoint and network tools only see after the fact.
Q: How should organisations govern private AI apps used on mobile devices?
A: Treat them as governed data-processing tools, not harmless consumer apps. Allow use only when you can verify where prompts and uploads go, whether sharing is enabled, and how feature access is controlled. The right control set combines app approval, content classification, and access review, especially when documents or images are involved.
Q: What is the best way to validate mobile app risk before release?
A: Analyse the compiled binary and its runtime behaviour in CI/CD, because source-only scans miss bundled libraries, configuration changes and device-level permissions. The release decision should be based on what the shipped app actually does, not just on what the repository appears to contain.
Technical breakdown
Why mobile telemetry is invisible to many AI security platforms
Most AI security platforms correlate risk by ingesting logs from endpoints, networks, identity systems and cloud services. Mobile applications often bypass that view because the meaningful behavior occurs inside a compiled binary running on a device. The app chooses which SDK to call, what permissions to request, and which infrastructure to contact, but those decisions are not always visible in standard telemetry pipelines. That means the platform may see outbound traffic or authentication events without understanding which internal component triggered them or why. Mobile app intelligence restores that missing layer by analysing the app package and its runtime behaviour.
Practical implication: Treat compiled mobile behaviour as a distinct telemetry source, not a downstream interpretation of network logs.
How SDK supply chains expand the mobile attack surface
Mobile apps commonly embed third-party SDKs for analytics, advertising, authentication or device telemetry. Each SDK can bring its own code paths, network destinations and update cadence, creating a supply chain inside the app itself. If a vulnerable SDK sends device identifiers, user data or AI-related signals to an external domain, traditional security tools may only see the traffic, not the dependency that generated it. That is why mobile risk is both application risk and supply-chain risk. The key control point is the final compiled binary, because that is where dependencies, configuration and runtime behaviour converge.
Practical implication: Assess bundled SDKs in the signed application, not just the source repository.
What changes when mobile apps become part of AI governance
AI governance typically focuses on models, prompts, data pipelines and policy enforcement, but mobile apps increasingly mediate access to AI services through APIs and embedded integrations. That creates an identity and data-governance problem as well as an AI problem, because the app becomes an intermediary that can transmit sensitive data into third-party services. Mobile intelligence helps teams understand where those requests originate, what data is exposed and whether the app’s behaviour aligns with governance intent. The practical question is no longer only who can use the AI service, but which mobile workflow is allowed to feed it.
Practical implication: Include mobile application flows in AI governance reviews when apps broker access to external AI services.
Threat narrative
Attacker objective: The attacker seeks to exploit opaque mobile code paths and third-party dependencies to move sensitive data or misuse application trust without triggering standard telemetry.
- Entry occurs through a mobile application that embeds third-party SDKs and connects to external infrastructure domains.
- Credential or data exposure follows when the app transmits sensitive information, permissions or device identifiers through code paths outside standard monitoring.
- Impact emerges when security teams cannot attribute the traffic to a specific in-app component, leaving vulnerable SDKs and risky data flows uncontained.
Breaches seen in the wild
- ArtiPACKED 2024: Unit 42 found public GitHub Actions artifacts in major open source projects leaking live GITHUB_TOKENs, runtime tokens and cloud tokens.
- DeepSeek database exposure 2025: An unauthenticated DeepSeek ClickHouse database exposed over a million log lines with plaintext chat history and API keys in 2025.
Read and download The State of NHI & AI Agent Breach Report 2026, covering 200+ breaches impacting Non-Human Identities including AI Agents.
NHI Mgmt Group analysis
Mobile app intelligence is now a telemetry governance issue, not just a mobile security niche. Security platforms increasingly assume they can correlate identity, endpoint, network and cloud signals into a usable risk picture. When mobile apps sit outside that picture, the analytics stack is operating with a blind spot that affects fraud, privacy, release risk and AI governance at the same time.
SDK supply chain visibility is the named concept practitioners should take seriously. Mobile risk is rarely just the app binary. It is the bundled dependencies, external domains and runtime behaviors that travel with the app and alter what the enterprise can actually observe. Practitioners should treat compiled mobile applications as governed software assets with their own dependency and telemetry profile.
AI security teams are moving from model-centric visibility to workflow-centric visibility. If a mobile app brokers access to an AI service, the relevant question is not only whether the model is approved. It is whether the mobile workflow is transmitting data, invoking third-party services or creating shadow integration paths that were never designed into the control model. That makes mobile intelligence part of AI governance, not an adjacent AppSec concern.
The visibility gap becomes more expensive as agentic AI correlates more signals automatically. Agentic systems can only reason over what they can ingest, so missing mobile telemetry suppresses both detection quality and decision quality. The enterprise response is to govern mobile behavior as a first-class source of risk evidence, especially where apps mediate authentication, transactions or AI service access.
From our research library:
- Only 5.7% of organisations have full visibility into their service accounts, according to the Ultimate Guide to NHIs.
- Half of companies using generative AI will deploy agentic AI by 2027, according to Deloitte's 2025 Technology Predictions.
- Read next: Agentic AI Security Guide
What this signals
Mobile app visibility gap: security teams need a workflow view, not just an infrastructure view, because the app itself is now where SDK trust, identity use and data movement converge. Without that layer, agentic analysis will continue to miss key signals in mobile-first environments.
The operational shift is straightforward: mobile binaries and runtime behavior need to be treated as governed evidence sources. That matters most where apps mediate authentication, transactions or AI service access, because the control failure is usually upstream of what endpoint and network tools can see.
For practitioners
- Instrument mobile binaries as a telemetry source Inspect the final signed application package and runtime behavior so security teams can see SDK calls, permissions and external connections that never appear in source scans.
- Map embedded SDK dependencies to data exposure paths Create an inventory of third-party SDKs, their network destinations and the data they touch, then tie each dependency to a specific business or AI workflow.
- Bring mobile flows into AI governance reviews Review applications that feed external AI services through APIs or embedded features, and verify what data the app can transmit before approval.
- Test release artifacts before app store distribution Run security analysis on the compiled mobile binary in CI/CD so build-time changes, new libraries and configuration shifts are checked before release.
Key takeaways
- Mobile app intelligence fills a real telemetry gap in AI security by exposing SDK behavior, permissions and data flows that standard tools miss.
- NowSecure says 85% of mobile apps contain at least one security flaw, and 70% may leak personal data, which shows how broad the mobile risk surface already is.
- Teams need to treat compiled mobile apps as governed assets, especially when those apps broker access to AI services or third-party infrastructure.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP API Security Top 10, MITRE ATT&CK and OWASP Non-Human Identity Top 10 address the attack and risk surface, while NIST CSF 2.0 and NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP API Security Top 10 | API8 — Security Misconfiguration | Mobile apps connect to APIs and infrastructure paths that can hide risky data handling and trust boundaries. |
| Recommendation — Review mobile API integrations for insecure configuration and hidden data paths that bypass standard telemetry. | ||
| MITRE ATT&CK | TA0009;TA0010 — Collection; Exfiltration | The article focuses on app-side data collection and movement that adversaries can exploit. |
| Recommendation — Map mobile telemetry gaps to collection and exfiltration techniques and prioritise those paths for monitoring. | ||
| NIST CSF 2.0 | PR.DS-01 — Data-at-rest is protected | Mobile application data handling and leakage directly affect protection of sensitive data. |
| Recommendation — Extend data protection controls to mobile app paths where sensitive information is stored or transmitted. | ||
| NIST AI RMF | MEASURE — AI Governance and Risk Measurement | AI security analytics depend on complete telemetry to measure risk accurately. |
| Recommendation — Measure whether mobile telemetry is included in AI risk scoring and close gaps that distort governance decisions. | ||
| OWASP Non-Human Identity Top 10 | NHI-02 — Secret Leakage | The article notes mobile apps can leak sensitive information through SDKs and compiled behavior. |
| Recommendation — Scan mobile apps for secret and sensitive data leakage in bundled SDKs, logs and runtime traffic. | ||
Key terms
- Mobile Application Intelligence: Mobile application intelligence is the analysis of compiled apps and their runtime behavior to reveal security-relevant signals that ordinary infrastructure monitoring misses. It focuses on SDKs, permissions, network destinations and data flows so security teams can understand what the app actually does on a device.
- SDK supply chain: An SDK supply chain is the chain of third-party libraries, analytics packages and service integrations embedded inside an application. Each component can add code, permissions, network behavior and data transfer paths, creating hidden trust dependencies that must be governed like other software supply-chain risks.
- Telemetry Gap: A missing or incomplete stream of security events that prevents the SIEM from building a reliable picture of activity. In hybrid environments, telemetry gaps often appear where identity context is weakest, turning visibility problems into detection and governance problems.
- Compiled mobile binary: A compiled mobile binary is the packaged application artifact delivered to users after source code is built and bundled with dependencies. It is the version that actually runs on devices, so release-time changes, embedded SDKs and runtime behaviors can introduce security risk that source review alone may not reveal.
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Published by the NHIMG editorial team on August 2, 2026.
Updated on October 12, 2026.
NHI Mgmt Group, the independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org