NowSecure AI-Navigator finds mobile app risks that hide behind the login

AI governance is important because mobile apps increasingly send personal, proprietary, and regulated data to AI models and endpoints, sometimes without clear visibility or approval.

NowSecure research highlights risks such as privacy-law violations, cross-border data transfer, data leakage, model theft, hardcoded API keys, and unauthorized AI use.

AI governance works by creating policies, discovering AI components, testing runtime behavior, mapping sensitive data flows, prioritizing risk, remediating issues, and preserving evidence for audits, procurement, app stores, and regulators.

In mobile, runtime testing is essential because hidden AI behavior may not appear in code review.

For mobile apps, NowSecure views AI governance as a program to identify, control, and prove responsible AI use.

Core goals include discovering shadow AI, protecting sensitive data, preventing unauthorized AI integrations, securing AI API keys and models, validating third-party SDK behavior, and maintaining compliance evidence.

Key principles include transparency, least-privilege data access, continuous testing, risk-based prioritization, auditability, and shared ownership across security, privacy, compliance, and engineering.

Governance should cover both AI features intentionally built into apps and AI introduced through SDKs, generated code, APIs, or supply chain components

At NowSecure, we recommend building AI governance around visibility, policy, testing, ownership, and evidence.

Start by inventorying where AI exists across mobile apps, including AI models, endpoints, SDKs, local files, third-party components, and data flows. Then define policies for approved AI use, sensitive data handling, third-party AI services, licensing, API keys, and regulatory obligations.

Continuous mobile testing should verify what apps actually do at runtime, not just what teams believe is in the code.

NowSecure helps organizations discover hidden AI, prioritize risk, validate controls, and produce governance evidence across first-party and third-party mobile apps.

NowSecure focuses on the mobile app controls that support trustworthy AI: transparency into AI components, secure data handling, validated disclosures, and continuous testing.

Fairness and bias require model governance, training-data review, and business oversight, but mobile teams must also confirm which AI services are used, what data is collected, where it goes, whether API keys are protected, and whether third-party SDKs introduce hidden AI behavior.

By testing apps dynamically and mapping AI data flows, organizations can make AI use more transparent, reduce security and privacy risk, and provide defensible evidence for governance reviews.

 

Continuous AI governance testing helps security teams reduce manual review effort, find hidden AI earlier, prevent costly compliance surprises, and prioritize the apps that carry the most risk.

NowSecure enables teams to identify AI models, AI-powered services, SDKs, third-party components, runtime interactions, and sensitive data movement across mobile portfolios.

That visibility lets organizations focus remediation where security, privacy, and compliance impact is highest instead of treating every app equally. Automated, integrated testing also supports faster release schedules while reducing organizational risk and improving audit readiness.

NowSecure recommends preparing audit evidence by documenting AI inventory, data flows, runtime behavior, third-party AI components, API endpoints, local AI files, SDKs, permissions, and remediation history.

Evidence should show which apps use AI, what data AI systems can access or transmit, where that data goes, and whether usage aligns with policy, contractual obligations, app store disclosures, and regulatory requirements.

NowSecure testing gives teams centralized reporting, governance visibility, and repeatable validation across authenticated and unauthenticated workflows, helping organizations demonstrate that AI risk is understood and controlled.

Yes. NowSecure helps organizations detect AI local files, AI endpoint URLs, AI libraries, cloud-based AI usage, on-device AI usage, and exposed AI API keys in mobile apps.

We also test runtime behavior to identify data collection, transmission, third-party connections, and sensitive data flows that may not be visible through code review alone.

This helps teams uncover shadow AI, identify unauthorized AI services or SDKs, and understand whether personal, business, or proprietary data is moving to AI systems in ways that create security, privacy, or compliance risk.

NowSecure recommends building the inventory through continuous mobile app testing across first-party and third-party apps.

The inventory should capture AI models, local AI files, AI libraries, SDKs, AI-powered services, endpoint URLs, API keys, vendors, teams responsible for the functionality, and data flows observed at runtime.

NowSecure AI Discovery helps organizations identify which apps use AI, where AI enters through code or third-party components, what data AI systems can access or move, and which apps require deeper review or remediation.

Security teams need AI governance controls now because AI is rapidly entering mobile apps through features, generated code, third-party SDKs, embedded services, and external AI endpoints.

NowSecure notes that AI introduces new security, privacy, reputation, regulatory, and compliance risks, especially when sensitive data is transmitted to models or unauthorized AI services.

Mobile apps are uniquely exposed because they handle personal data, authenticate users, connect to APIs, and include many third-party components. Without governance controls, teams may miss shadow AI, hardcoded AI keys, insecure integrations, and unapproved data flows until after release.

In a mobile release process, AI governance controls should run continuously from development through release.

NowSecure recommends testing each build for AI endpoints, SDKs, local models, hardcoded API keys, sensitive data flows, insecure transmissions, and third-party AI behavior.

Findings should be prioritized by security, privacy, and compliance impact, routed to developers with remediation guidance, and retested before release. For authenticated app areas, NowSecure AI-Navigator can automate login and navigate real workflows so testing reaches the places where data is collected, processed, stored, and shared.

A mobile AI governance program should include AI discovery, third-party SDK analysis, runtime data-flow testing, endpoint monitoring, API key protection, model and local file inventory, policy enforcement, secure storage, encryption, regulatory mapping, and audit reporting.

NowSecure also recommends tracking where AI endpoints are hosted, identifying local models and AI libraries, securing sensitive data transmission, and testing against OWASP MASVS for broader mobile privacy, resilience, networking, cryptography, authentication, storage, and code risks.

These controls should apply to both first-party AI features and AI introduced through SDKs, open-source components, or supply chain dependencies.

NowSecure recommends managing these risks by continuously identifying AI SDKs, libraries, endpoints, local models, vendors, and runtime data flows across the mobile app ecosystem.

Teams should validate whether AI use is disclosed, authorized, licensed, secure, and compliant with privacy, procurement, app store, and regulatory expectations.

Third-party SDKs deserve special attention because they can introduce AI functionality or data sharing outside direct engineering control. Dynamic testing is critical to determine whether sensitive data is transmitted, stored insecurely, sent across jurisdictions, or exposed through unencrypted connections or hardcoded API keys.

NowSecure recommends shared ownership with clear accountability.

Security should own technical risk testing, AppSec should integrate controls into mobile release workflows, privacy should define data-use and disclosure requirements, compliance should map regulatory and audit obligations, and engineering should remediate issues and control SDK adoption. Product and business owners should approve AI use cases and acceptable data flows.

Because AI risk crosses security, privacy, compliance, procurement, and development, governance should operate as a coordinated program supported by continuous evidence from mobile app testing rather than as a one-time checklist.

NowSecure recommends testing AI-enabled mobile apps with a combination of static analysis, dynamic runtime testing, authenticated workflow testing, API and network inspection, SDK analysis, and sensitive data-flow validation.

Teams should identify AI endpoints, local models, AI libraries, hardcoded API keys, unencrypted connections, insecure integrations, and unauthorized transmission of personal or business data.

Testing should also confirm where data is hosted, whether disclosures match actual behavior, and whether app behavior aligns with OWASP MASVS, privacy, procurement, and compliance requirements. Authenticated dynamic testing is especially important because major risks often appear only after login.

At NowSecure, we recommend starting with AI discovery, then building enforceable controls into the mobile SDLC.

Inventory AI features, SDKs, local models, endpoints, API keys, vendors, data flows, and ownership. Define policies for approved AI use, sensitive data handling, licensing, disclosures, jurisdiction, and third-party components.

Continuously test apps at build time, runtime, and after authentication to uncover shadow AI, insecure transmissions, hidden SDK behavior, and unauthorized data collection. Prioritize findings by security, privacy, and compliance impact, remediate before release, retest fixes, and preserve evidence for audits and governance reviews.