<![CDATA[Cloud-native computing has weakened the traditional network perimeter by distributing identities, workloads, applications, and data across dynamic infrastructures where successful login no longer guarantees continuing trust. This study develops and evaluates a continuous authentication framework for Zero Trust Architecture that dynamically reassesses trust throughout active sessions. A design science approach combined with controlled experimentation was employed using 1,200 authenticated sessions, including 800 legitimate and 400 adversarial scenarios involving token hijacking, session replay, privilege escalation, compromised devices, lateral movement, and anomalous API activity. The framework integrated identity confidence, behavioral consistency, device posture, contextual risk, workload telemetry, and resource sensitivity into adaptive trust scoring and graduated enforcement. Results showed that continuous authentication achieved a 93.0% detection rate, 91.9% precision, 93.0% recall, and a 92.4% F1-score, while reducing mean compromised-session exposure from 11.8 to 1.9 minutes. Moderate increases in latency and computational overhead remained operationally acceptable, and false-positive rates did not increase significantly. Improvements were strongest against post-authentication compromise and identity misuse. The study concludes that Zero Trust is more effectively implemented as a dynamic, revocable, and context-sensitive trust process than as repeated static authentication. Continuous authentication therefore provides a practical mechanism for protecting both human and machine identities in decentralized cloud-native environments]]>
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