## 4 Automated Zero Trust Risk Management with DevSecOps Integration (AZTRM-D)
### NIST Artificial Intelligence Risk Management Framework  
While AI is integral to AZTRM-D's effectiveness, the AI models themselves introduce unique risks and must be managed responsibly [67; 72]. To govern these components, AZTRM-D incorporates the principles of the NIST AI Risk Management Framework (AI RMF). The AI RMF provides a structured approach to addressing the complex risks associated with AI, including issues of bias, transparency, and predictability [73].  
A key part of this is ensuring the AI models are explainable and trustworthy. Rather than operating as \"black boxes,\" the AI systems within AZTRM-D are designed to provide clear justifications for their security decisions, a concept central to the AI RMF's \"Govern\" function. Recent studies emphasize that leveraging techniques like an ensemble of label noise filters can significantly improve the reliability of local explanations, which is directly applicable to making AZTRM-D's security decisions more transparent and trustworthy, especially when dealing with imperfect real-world data [74].  
For instance, the framework emphasizes explainability. If the AI blocks a developer's code commit, it does not simply return a \"denied\" message but provides a clear, human-readable reason, such as: \"Commit blocked: This change introduces dependency 'library-x-1.2', which has a known critical vulnerability (CVE-2023-XXXX). Please update to version '1.3' or higher\" [75]. To generate these justifications, AZTRM-D is designed to integrate established eXplainable AI (XAI) techniques. For example, it could utilize SHAP (SHapley Additive exPlanations) to show precisely which code features most influenced the AI's 'risky' classification [76]. Alternatively, it could provide counterfactual explanations (e.g., 'This commit would be approved if the dependency 'library-x-1.2' were updated to version '1.3' or higher'), offering direct, actionable feedback to the developer.  
The framework also addresses fairness, ensuring that AI models used for detecting insider threats are trained on behavioral data like resource access patterns rather than on personal identifiers to mitigate algorithmic bias. To operationalize this, AZTRM-D incorporates specific fairness metrics during model validation. For example, it employs 'equalized odds' to ensure that the model's true positive rate (correctly identifying malicious activity) and false positive rate (incorrectly flagging normal behavior) are consistent across different developer roles or teams. If the AI flags commits from the 'Frontend' team at a statistically higher rate than the 'Backend' team for similar behaviors, the model is flagged for re-training with a more balanced dataset. This prevents a scenario where the AI develops a bias against a particular group, ensuring that security alerts are based on anomalous actions, not on pre-existing data imbalances or job functions.  
Finally, the framework ensures robustness by continuously testing the AI threat detection models against adversarial examples, which are inputs designed to intentionally fool the system, to ensure they are resilient and cannot be easily bypassed by sophisticated attackers [70]. By integrating these principles, AZTRM-D ensures that its own AI components are managed with the same rigor as the systems they are designed to protect.