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19/08/2026 3:31 pm
TL;DR: A Legit survey of 1,000 consumers found that only 22% believe most mobile app code is AI-generated, while 47% are concerned about AI in apps and 31% flag sensitive data requests as their top app concern, underscoring a widening trust gap around AI-assisted software delivery according to LEGIT Security. That gap matters because application teams now need to govern AI use, data exposure, and transparency as part of app security, not as a separate communications exercise.
NHIMG editorial — based on content published by LEGIT Security: consumer survey findings on AI in app development and app trust
By the numbers:
- Only 22% of respondents believe a typical mobile app’s code is mostly AI-generated.
- 47% of consumers say they are concerned about AI in apps.
Questions worth separating out
Q: How should security teams govern AI use in developer tooling?
A: Security teams should govern AI use as a data and access problem, not only a productivity feature.
Q: Why do AI-built apps raise trust concerns for consumers?
A: Consumers cannot directly see how much AI shaped the app, what data it consumes, or whether AI changed the underlying permission model.
Q: What do security teams get wrong about AI-generated code risk?
A: They often focus on catching insecure output after code is written, which is too late for AI-native workflows.
Practitioner guidance
- Inventory where AI is used in the SDLC Identify which applications, pipelines, and release paths use AI for coding, testing, content generation, or runtime decisions so security review can be scoped correctly.
- Tie app permissions to data necessity Review mobile and web app permissions, third-party integrations, and data collection fields to confirm each one has a specific business purpose and a documented retention rule.
- Harden AI-assisted pipelines with existing controls Apply static analysis, branch protection, secret scanning, and approval gates to AI-generated code exactly as you would for manually written code, with no exceptions for speed.
What's in the full report
LEGIT Security's full article covers the operational detail this post intentionally leaves for the source:
- Survey methodology and consumer segmentation across age groups and trust signals
- Detailed breakdown of how respondents judged AI-written code, privacy, and app-store credibility
- Vendor guidance on AI visibility, AI-specific testing, and threat modelling for app teams
- Consumer safety checklist covering permissions, passwords, two-factor authentication, and device hygiene
👉 Read LEGIT Security's survey findings on consumer trust in AI-built applications →
AI-built apps and consumer trust: what app teams need to know?
Explore further
19/08/2026 4:14 pm
AI in applications has become a governance problem, not just a development pattern. Once AI is embedded in code generation, data handling, or runtime decision-making, the security model must account for new dependencies and faster change velocity. Consumer trust is then shaped by whether teams can explain and control those dependencies, not by whether the app claims to use AI responsibly. The practitioner conclusion is that AI visibility must sit inside application governance.
A question worth separating out:
Q: How can organisations tell whether app transparency is credible?
A: Credible transparency explains where AI is used, what data it can access, and how that use affects security, privacy, and user choice. Vague claims about safety are not enough. Teams should be able to show the controls behind the claim, including access limits, retention rules, and testing coverage.
👉 Read our full editorial: Consumer trust in AI-built apps is lagging behind their use
