Jul 2026· Annual International Computer Software and Applications Conference· pp. 2191-2198· 0 citations· 13 references
Abstract
As DAOs take on broader operational roles, governance responsibilities are increasingly distributed across autonomous units within and across organizations. Decisions in one unit may condition actions in another, creating cross-DAO dependencies that require deliberate coordination. Existing interoperability tooling addresses technical transport, but not the governance question of when or how tightly such coupling should be activated. This distinction is important because on-chain cross-DAO actions can be difficult to reverse once triggered. This paper therefore introduces a decision framework for evaluating scoped cross-DAO dependencies across cost and value dimensions. The resulting integration score maps deterministically to one of five coordination modes: isolate, manual, partial, conditional, or full. The framework is implemented in a prototype comprising a governance decision layer and an on-chain enforcement layer, where automation is activated only after explicit multi-DAO authorization. A scenario-based evaluation across seven dependency scopes shows that the framework produces differentiated and auditable recommendations tied to scored governance attributes. It further shows that simulation-derived evidence primarily shifts mode selection in near-threshold cases. The framework thus treats coupling as a deliberate, evidence-grounded governance act rather than as a default consequence of available interoperability tooling, offering a step toward practical automation for complex real-world DAO interactions.
This work explicates the trust and transparency trade-offs of the design choices in implementing a DAO and highlights how poor choices introduce critical vulnerabilities, using real-world examples as case studies.
Vabuk Pahari, B. Chandrasekaran, Johnnatan Messias et al.· arXiv.org· 0 citations
Third-party dependencies have become a structural feature of contemporary supply chain operations, creating exposures that extend beyond the boundaries of any single organization. Prevailing assessment practice remains anchored in periodic, questionnaire-based methods that produce ordinal risk ratings. Such ratings cannot express uncertainty, cannot be aggregated across organizational entities, and decay between review cycles. Artificial intelligence offers analytical capabilities that address these constraints, yet the literature reviewed here treats those capabilities largely in isolation from the governance structures that determine whether their outputs acquire organizational force. This paper develops a governance-anchored framework for AI-enabled third-party risk quantification through design science research, positioning the contribution as an exaptation of quantification approaches established in credit risk to a domain characterized by sparse outcome data. The framework comprises four layers: data ingestion and signal extraction, quantification logic, governance architecture, and aggregation and escalation. Governance controls are specified within it as constitutive components rather than as subsequent additions, and its quantification layer is scoped to relative exposure ordering with explicit uncertainty rather than to calibrated absolute failure probabilities, a scoping decision that follows from the rare-event data conditions characteristic of vendor populations. Evaluation proceeds against external referents comprising named regulatory expectations and documented failure cases, following an established design science evaluation framework. Of six design requirements, four are assessed as adequately addressed, one as partially addressed, and one as conditionally addressed pending specification beyond the framework's present scope. The study contributes to supply chain risk management, enterprise risk governance, and applied artificial intelligence scholarship. As a conceptual artifact evaluated analytically rather than empirically, its propositions await implementation testing.
Olakunle Akintunde Akinbowale· International Journal of Mul...· 0 citations
This study examines governance design and participatory innovation across three DAOs: RARI DAO, Arbitrum DAO, and Optimism DAO, suggesting the emergence of increasingly formalized governance architectures designed to balance decentralization, coordination efficiency, and operational security.
The central finding is that the same four points generalize without modification to AI coding agents and to general agentic workflows, where a defined purpose, an auditable trail, verification, and safe delivery produce tamper-evident evidence at every stage.
P. Gresham· Universal Library of Innovat...· 0 citations
LATTICE (Layered Agentic Triad Topology for Intelligent Coordinated Execution), a governance-first architecture that reframes the authorization question from “do the authors trust this AI?” to “do they trust this architecture?”
Elias Calboreanu· Frontiers in Artificial Inte...· 1 citation
This article examines the governance conditions under which operational knowledge migrates from a mandated enterprise resource planning system into operationally trusted parallel records, developing the Visibility–Protection Gap as a governance configuration.
A three-year action research study across all 80 sites of one private security organisation. Evidence comprises 47 externally facilitated action learning sessions, documentary investigation yielding 1,120 binary governance decisions, separate confirmation of parallel systems, and divergence between client-approved counts and ERP system counts. Seven staggered interventions were measured before and after.
Deficient governance dimensions showed a monotonic relationship with confirmed parallel systems: none at 48 sites with zero or one deficiency, five of fourteen with two, and all eighteen with three. Architecture and record divergence tracked the same configuration.
Evidence derives from one organisational population and requires cross-sector testing. The insider design carries epistemic circularity that safeguards reduce without removing; the intervention lacks a control group and changes are associations.
A parallel record is better interpreted as a governance indicator than an implementation failure: eliminating it without reducing the deficiency removes access to operational information without restoring it. Learning metrics introduced before reporting protection is credible may deepen the gap.
Prior work examined power and governance around feral systems and shadow information technology. This study relocates the explanation to the governance architecture surrounding reporting, connecting parallel-system emergence and official-record degradation. Formal systems may retain authority after losing epistemic primacy.
Sid Ahmed El Hebri Harid· Journal of Enterprise Inform...· 0 citations
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