Securing .Net Microservices Through Conditional Access and Zero Trust Principles using Azure AD and OAUTH2
Bibliographic record
Abstract
The increasing adoption of distributed microservices in enterprise applications has amplified the need for robust, identity-centric security frameworks. This thesis presents a policy-driven Zero Trust architecture for securing .NET-based microservices using Azure Active Directory (Azure AD), OAuth 2.0, and Conditional Access. The proposed approach leverages Microsoft Entra ID for centralized identity governance and employs Conditional Access policies to enforce real-time, risk-based access decisions. Fine-grained authorization is achieved through integration with OAuth 2.0 token scopes and claims, ensuring contextual access based on user identity, device compliance, location, and session risk signals. The framework is implemented within a cloud-native .NET Core microservices environment, utilizing Azure API Management for secure exposure and traffic mediation. Telemetry from Microsoft Defender for Cloud and Azure Monitor is integrated to dynamically adapt authorization rules, aligning access decisions with real-time threat intelligence. The system is validated through a series of controlled simulations, demonstrating its effectiveness in minimizing unauthorized access, preventing lateral movement, and reducing the attack surface. This research provides a practical and scalable methodology for implementing Zero Trust principles across modern .NET applications using Microsoft’s identity and cloud security ecosystem..
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".