Abstract
A Technical Architecture for AI Agent Authorization, Mobile Operating-System Security, Privacy-Preserving API Access, and Non-Discriminatory Platform Interoperability
Author: Sangam Kumar Das Affiliation: Independent Inventor and Researcher, India Technical Areas: AI Agent Security, Mobile Operating Systems, Platform Interoperability, Hardware Security, API Authorization, Privacy, and EU Digital Markets Act
Open-Access Technical Blueprint and Reference Implementation
Repository: Zenodo DOI: 10.5281/zenodo.22053979 Access: Open Access
The full publication provides the detailed security architecture, implementation alternatives, technical explanations, downloadable source materials, and reference implementations. Readers interested in the algorithms, security tests, mathematical conditions, and implementation details are encouraged to consult the original publication.
Abstract
Can Apple, Google, and other mobile operating-system providers give competing AI assistants equal access to device features without compromising user privacy, payment security, or control over sensitive actions?
The European Union's Digital Markets Act (DMA), particularly Article 6(7), requires designated gatekeepers to provide effective interoperability with relevant operating-system hardware and software features, subject to applicable security and integrity safeguards.
As AI assistants become capable of sending messages, sharing documents, initiating payments, accessing applications, and controlling device functions, these obligations raise an important technical challenge: how can operating systems support competing AI assistants while preventing unauthorized or unintended actions?
In June 2026, Apple announced that its upgraded Siri AI would not initially be available on iPhone and iPad in the European Union, citing concerns surrounding DMA interoperability requirements and the privacy and security implications of third-party assistant access. The European Commission disagreed that the DMA prevented the release and emphasized the obligation to provide effective, secure interoperability.
This disagreement highlights the need for an architecture that supports both fair platform access and enforceable security controls.
The proposed technical solution separates an AI assistant's permission to request an operation from the protected operating-system authority required to execute it.
Under this architecture, first-party and third-party AI assistants can access standardized application functions through common interfaces without receiving unrestricted access to sensitive operating-system capabilities.
An assistant may request a specific operation, such as sending a message, sharing a file, opening an application, or initiating a payment. Before execution, a protected authorization mechanism independently verifies the intended recipient, selected content, requested operation, user approval, application identity, applicable security policy, and other transaction-specific conditions.
Immediately before the action takes effect, a protected execution component checks the actual operation against the approved parameters. An operation-specific, cryptographically bound, single-use authorization prevents the permission from being transferred, reused, or applied to an unrelated action.
For example, if an AI assistant is authorized to send one photograph to a specific recipient but a prompt-injection attack changes the attachment or destination, the final security check detects the difference and blocks the unauthorized transmission.
The architecture supports hardware-backed security through trusted execution environments, secure processors, protected credential storage, and isolated authorization components. It is designed to complement existing application sandboxing, OAuth 2.0, operating-system permissions, API gateways, digital signatures, and user-consent mechanisms.
A significant feature is the application of equivalent authorization and security-enforcement rules to both first-party and third-party AI assistants. This makes it possible to test whether competing assistants receive comparable functional access without weakening the platform's security requirements.
The design is applicable to Apple Siri, iOS, iPadOS, Android, on-device AI agents, third-party assistants, application-intent frameworks, secure messaging, file sharing, digital payments, and cross-application automation.
It also provides a technical approach relevant to GDPR privacy-by-design and security obligations, DMA interoperability requirements, and applicable EU AI Act provisions. Actual regulatory compliance depends on implementation and legal assessment.
The central engineering principle is simple: an AI assistant may request an action, but the operating system's protected security boundary must independently verify the exact action before permitting its execution.
This approach offers a potential way for mobile platform providers to support open AI competition, user choice, privacy protection, and strong device security without granting unrestricted execution privileges.
Key Technical Features
Equal API access: First-party and third-party AI assistants can request supported operating-system and application functions through common interfaces subject to equivalent security conditions.
Transaction-specific authorization: Permissions are restricted to the actual requested operation, including its destination, content, resource, purpose, and applicable limits.
Hardware-backed enforcement: Sensitive authorization decisions and execution permissions can be protected using secure hardware or isolated operating-system security components.
Prompt-injection protection: Unauthorized changes to recipients, attachments, transaction amounts, or other sensitive parameters can be rejected before execution.
Single-use cryptographic authorization: Requester-bound, operation-specific credentials help prevent stolen permissions, unauthorized reuse, and replay attacks.
Privacy-preserving execution: AI assistants can receive task-specific access without unrestricted exposure to messages, documents, credentials, or application data.
Non-discriminatory security enforcement: The same authorization criteria can be applied to a platform's own AI assistant and competing assistants, enabling technical comparison of access conditions.
Failure recovery: Protected transaction state and idempotency controls reduce duplicate execution risks; uncertain external outcomes require reconciliation rather than automatic repetition.
Technical Validation and Reference Implementation
The author reports a runnable Python companion implementation with 22 of 22 automated security tests passing.
The reported tests address prompt injection, attachment substitution, unauthorized recipients, stolen execution credentials, replay protection, incorrect execution boundaries, spending limits, crash recovery, and comparable treatment of first-party and third-party AI assistants.
Author-reported median processing overhead for the tested authorization mechanism is approximately 273–321 microseconds per action on one cloud virtual CPU.
These results are illustrative laboratory measurements rather than independently audited production benchmarks. The linked Zenodo record also contains a separate reference-implementation release with its own tests and measurements; results from different versions should not be treated as interchangeable.
The implementation materials are available through the open-access publication:
The detailed PDF and source materials explain the authorization model, cryptographic verification, protected execution logic, security assumptions, test procedures, and implementation alternatives.
Industry Applications
The proposed approach is relevant to mobile operating-system developers, AI assistant providers, smartphone manufacturers, application framework developers, cybersecurity vendors, financial-technology platforms, secure hardware manufacturers, and regulators evaluating digital-platform interoperability.
Potential implementation environments include iOS App Intents, Android AppFunctions, mobile application permissions, secure APIs, operating-system service brokers, trusted execution environments, hardware security modules, cross-application AI automation, messaging services, payment gateways, and enterprise device-management platforms.
The architecture is intended to complement existing identity, authorization, sandboxing, and data-protection systems rather than replace them.
Related International Patent Applications
Principal International Patent Application:
PCT/IB2026/055615 — International Publication WO 2026/150382, filed 4 June 2026.
Additional Related Technical Disclosures:
PCT/IB2026/055760; PCT/IB2026/055870; PCT/IB2026/056058; PCT/IB2026/056571; PCT/IB2026/056771; PCT/IB2026/056809; PCT/IB2026/056941; PCT/IB2026/057198; PCT/IB2026/057540; PCT/IB2026/058236.
These identifiers are supplied for technical cross-referencing and patent discovery. Their inclusion does not independently establish priority entitlement or claim coverage for every mechanism described in this article.
Keywords — Industry, Regulatory and Patent Search
Primary technical keywords: AI assistant interoperability; AI agent security; mobile operating-system security; secure AI agents; third-party AI assistants; first-party AI assistants; autonomous AI agents; on-device AI; agentic AI; mobile AI integration; secure cross-application automation; app-to-app interoperability; operating-system API access; secure application execution; AI tool-use authorization; AI action verification; platform security; hardware-backed authorization; trusted execution environment; secure enclave; cryptographic authorization; capability-based security; per-action authorization; transaction-specific authorization; single-use credentials; proof-of-possession authentication; replay attack prevention; secure API gateway; privilege separation; least-privilege access; runtime security; policy enforcement point; reference monitor; time-of-check/time-of-use protection; secure transaction processing.
Platform and product search terms: Apple Siri; Siri AI; Apple Intelligence; iOS; iPadOS; Android; Google Gemini; Android AppFunctions; Apple App Intents; Trusted System Agent; mobile operating system; smartphone AI agents; third-party application integration; application permissions; application entitlements; secure inter-app communication; device-side authorization; messaging API; file-sharing API; payment initiation; mobile wallet security; app action frameworks; secure device control.
Security and privacy search terms: Prompt injection; indirect prompt injection; malicious AI tool calls; unauthorized data sharing; attachment substitution; recipient verification; sensitive data access; data minimization; privacy by design; cryptographic challenge-response; key-bound authorization; credential isolation; single-use authorization; replay prevention; privilege escalation prevention; authorization revocation; application sandbox; OAuth 2.0; API security; trusted computing; hardware security; crash-consistent transactions; idempotency; protected state management.
Regulatory and competition search terms: EU Digital Markets Act; DMA; DMA Article 6(7); DMA interoperability obligations; gatekeeper obligations; effective interoperability; platform neutrality; equal access; non-discriminatory access; interoperability APIs; strictly necessary and proportionate integrity measures; fair competition; third-party developer access; GDPR Article 25; GDPR Article 32; EU AI Act; data protection; privacy-preserving interoperability; user consent; regulatory compliance engineering; mobile ecosystem interoperability.
Supplementary technical disclosure terms: Execution finality; protected execution boundary; exact-action verification; protected authorization; single-use execution capability; independently enforced authorization; final execution verification; non-bearer authority; secure effectuation.
Cooperative Patent Classification (CPC) — Author-Suggested Search Codes
G06F 21/53; G06F 21/604; G06F 21/6218; G06F 21/6245; G06F 9/54; H04L 9/3228; H04L 9/3271; H04L 63/10; H04L 63/0807; H04W 12/08; H04W 12/37; G06Q 20/40; G06Q 20/3223.
International Patent Classification (IPC) — Author-Suggested Search Codes
G06F 21/53; G06F 21/60; G06F 21/62; G06F 9/54; H04L 9/32; H04L 9/40; H04W 12/08; H04W 12/37; G06Q 20/40; G06Q 20/32.
These are author-suggested patent-search classifications, not official classifications assigned by a patent office.
Technical and Legal Notice
This publication presents an independently developed technical proposal for secure AI assistant interoperability. It is not an Apple, Google, or European Commission implementation, endorsement, certification, or legal compliance determination.
The proposed security guarantees depend on actual enforcement at the relevant operating-system or application-execution boundary, protected authorization state, and appropriate controls over alternative execution paths.
The reference implementations are research demonstrators and do not establish production security certification or performance on commercial smartphones.
Author and Contact
Sangam Kumar Das Independent Inventor and Researcher Balasore, Odisha, India
Email: [email protected] Phone: +91-9861363532
Open to strategic collaboration, patent licensing, and acquihire discussions with mobile operating-system providers, AI assistant developers, platform security teams, and related technology organizations.
Conclusion
Secure AI interoperability should not require unrestricted operating-system access.
Mobile platforms can provide common interfaces to competing AI assistants while independently verifying sensitive actions at the protected execution boundary.
This approach offers a technically testable framework for combining third-party AI interoperability, equal treatment, user privacy, and secure operating-system execution.
Full technical specification and supporting implementation:
Open the Technical Blueprint on Zenodo — DOI: 10.5281/zenodo.22053979
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Recommended Citation
Das, Sangam Kumar, "Secure AI Assistant Interoperability for Apple Siri, iOS and Android: Equal Third-Party Access with Hardware-Backed Security Under the EU Digital Markets Act", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/12670