Adaptive Trust for Autonomous Access Governance
DOI:
https://doi.org/10.5281/jy34yx93Keywords:
Autonomous services, access control delegation, adaptive policy enforcement, policy invariance, variable granularity, policy models match, formal languages, expressiveness analysis, TPMS, transitive trust, policy-supported trust, role-temporal communities, external variables, antecedents-based access controlAbstract
Modern distributed digital ecosystems—particularly those built around identity and access management—are in constant flux, not just in their components and operating environments, but in how the actors within them interact. Policy-aware access control has begun to address this by folding contextual signals into rule evaluation, yet it still overlooks something critical: the trust relationships between actors shift over time, and most models treat trust as static.
This paper argues that autonomous protection and policy-aware access control are not competing priorities but complementary ones, and proposes a trust-adaptive intelligence framework that unifies them. The framework directs decision-making effort toward the areas of greatest risk as disruptions ripple through the ecosystem.
We frame actors in these ecosystems as participants in a global trading community, transacting through services such as trust services, identity federation, and third-party auditing. Trust-adaptive intelligence is a goal-oriented reasoning approach that treats a trust metric for each actor as a live, dynamic input to decision-making—rather than a fixed attribute. This metric does more than shape which services an actor selects or requests: it can also drive identity provisioning and incident detection, and support information-sharing among protecting actors to strengthen overall resilience.
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Data Availability Statement
No new datasets were generated or analyzed in this study. All data supporting the findings are contained within the article.
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