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Legendary

legendary.kiwi
◐Moderate Risk
Updated October 19, 2026

Legendary Kiwi is a small New Zealand mobile app studio whose published policies describe a deliberately minimal data posture: most apps work offline, require no account, and the vendor states it does not collect personal data, sell data, or use tracking SDKs. Payment handling is delegated entirely to Apple and Google. The policies are recent (1 May 2026) and well-matched to the modest, pre-launch product surface. The principal gaps are an absence of any compliance certifications, no documented security controls, and — importantly — no mention of any AI/ML functionality at all, meaning this does not read as an AI tool based on the supplied text. For a consumer app studio with no stated enterprise offering, the low-data-collection model makes this low-risk, though the silence on security practices and certifications caps confidence.

AI Transparency Facts

Independent analysis by TermsWatchdog · © 2026 TermsWatchdog

Input Data Ownership

Moderate Risk

The policies do not address ownership of any user-submitted data, prompts, or files — consistent with apps that are stated to collect little or no personal data and often work offline. Because there is no express statement on input ownership, this is a silence finding rather than an unfavorable term.

Confidence
40%

Output Data Ownership

Moderate Risk

The documents assert that all content within the apps is the vendor's intellectual property but are silent on ownership of anything a user might generate through the apps. For games and offline utilities this is a conventional IP clause, but the absence of any user-generated-content ownership statement leaves the question unresolved.

Confidence
45%

Training Data Usage

Low Risk

The policies make no mention of AI model training, and the privacy policy states only anonymised, aggregated platform analytics are used. There is no stated mechanism by which user data would be used to train models, and the product appears not to be AI-based.

Confidence
55%

Data Retention & Deletion

Moderate Risk

The policy describes minimal collection and states no account or personal data is required for most apps, but it provides no retention schedule, no deletion SLA, and no documented deletion request process beyond a general support contact. The low-collection posture mitigates risk, but the absence of any explicit retention or deletion terms is a gap.

Confidence
45%

Third-Party Data Sharing

Low Risk

The privacy policy explicitly states the vendor does not sell, rent, or share user data with third parties, and does not use advertising SDKs. Payment processing and app distribution are handled by Apple and Google under their own policies, which is disclosed and integral to app distribution.

Confidence
75%

Opt-Out Rights

Moderate Risk

The policy provides no explicit opt-out mechanism, but the data model largely obviates the need for one: most apps require no account and collect no personal data, and only anonymised aggregate analytics are used. The absence of a formal opt-out process is a gap, though mitigated by the minimal collection.

Confidence
45%

Compliance & Certifications

High Risk

No compliance frameworks or certifications are mentioned anywhere in the supplied documents. The vendor is a New Zealand company processing data that may implicate GDPR and other universal-baseline frameworks, yet none are referenced. As a consumer app studio, sector-specific frameworks are not expected, but the complete absence of universal-baseline references warrants a RED under the stated rubric.

Confidence
60%

Model Explainability & Auditability

Moderate Risk

The documents contain no reference to AI models, model behavior, or enterprise auditing, consistent with a product that does not appear to be AI-based. There is therefore no transparency or audit capability described, which is a silence finding.

Confidence
40%

Security Practices & Breach History

Moderate Risk

The policies disclose no specific security controls — no encryption, access control, penetration testing, bug bounty, or incident response details — and reference no security page or trust center. No breaches are disclosed. The minimal-data model reduces the attack surface, but the total absence of documented security practices is a gap.

Confidence
50%

Enterprise vs. Consumer Risk Delta

Low Risk

The terms explicitly state apps are for personal, non-commercial use only, and no enterprise or paid-tier data-handling distinctions are described. There is a single consumer posture, so no adverse tier delta exists.

Confidence
55%

Human Review of User Inputs

Low Risk

The policy describes no human review of user inputs or outputs, and states personal data is collected only when a user directly contacts support. Given that most apps collect no personal data and work offline, there is no described mechanism for staff to access user content.

Confidence
50%

Regulatory & Litigation Exposure

Moderate Risk

The documents make no reference to government data requests, law enforcement cooperation, or litigation. The governing-law clause sets New Zealand jurisdiction for disputes. Silence on data requests is typical for a minimal-collection app but leaves the vendor's practices undocumented.

Confidence
45%

PII & SPI Data Inventory

Low Risk

The privacy policy affirmatively lists categories it does NOT collect — name, email, contact details, location, payment data — and states most apps require no account. Only anonymised, aggregated analytics are used, and no SPI is collected. This is a strong minimal-data posture clearly disclosed.

Confidence
70%

Policy–Product Currency

Moderate Risk

Both policies are dated 1 May 2026, well within 12 months of the analysis date, which is favorable on recency. However, the product surface is pre-launch ('Our first apps are coming soon') and too thin to establish AI or other capabilities, so coverage cannot be confirmed; per the insufficient-evidence rule the rating is capped at YELLOW. Notably, nothing in the policies or marketing indicates an AI/ML product despite the subject being framed as an AI tool.

Confidence
50%

Cross-Document Consistency

Low Risk

Two documents were supplied — a Privacy Policy and Terms of Use — and no contradictions were found between them. Both share the same effective date, the same corporate entity, and consistent statements on in-app purchases, data handling, and governing scope.

Confidence
70%

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