Journal article : Review
Universal Digital Identity Stakeholder Alignment: Toward Context-Layered RAG Architectures for Ecosystem-Aware AI
- Abstract:
- A universal approach to managing a person’s digital identity may be the single most important advancement to the Internet since its inception, promising the seamless flow of information, averting cybercrime, eliminating login credentials, and restoring privacy and trust through greater control of one’s identity online. However, this advancement brings significant risks, especially regarding personal privacy. It demands the meticulous development of digital identity infrastructure that balances robust data security measures with ethical handling of sensitive information, thereby safeguarding against misuse and unauthorised access. Currently, a consolidated vision for digital identity implementation remains unresolved, and aligning the different stakeholders’ motives and expectations is a challenging task. This article reviews and analyses the perspectives and expectations of four key stakeholder groups—government, business, academia, and consumers—regarding a digital identity ecosystem, aiming to increase trust in an eventual design framework. Using an online survey stratified across government, business, academia, and consumers, we identify areas of alignment and divergence regarding privacy, trust, usability, and governance expectations. We then encode these stakeholder expectations into a layered conceptual structure and illustrate its use as metadata for context-layered retrieval-augmented generation (RAG) in digital identity scenarios.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 732.8KB, Terms of use)
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- Publisher copy:
- 10.3390/digital6010004
Authors
+ Commonwealth Scholarship Commission
More from this funder
- Funder identifier:
- https://ror.org/051x4wh35
- Publisher:
- MDPI
- Journal:
- Digital More from this journal
- Volume:
- 6
- Issue:
- 1
- Pages:
- 4
- Article number:
- 4
- Publication date:
- 2026-01-14
- Acceptance date:
- 2026-01-06
- DOI:
- EISSN:
-
2673-6470
- ISSN:
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2673-6470
- Language:
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English
- Keywords:
- Subtype:
-
Review
- Pubs id:
-
2361701
- UUID:
-
uuid_f8e3b620-c879-4e0f-aa1d-b14043d52393
- Local pid:
-
pubs:2361701
- Source identifiers:
-
3741127
- Deposit date:
-
2026-02-09
- ARK identifier:
This ORA record was generated from metadata provided by an external service. It has not been edited by the ORA Team.
Terms of use
- Copyright date:
- 2026
- Licence:
- CC Attribution (CC BY)
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