Building Secure, Explainable, and Auditable AI Systems for Modern Software Engineering Applications
DOI:
https://doi.org/10.64137/31078699/IJETET-V2I1P101Keywords:
Artificial Intelligence, Software Engineering, Explainable AI, AI Auditing, Secure AI Systems, Observability, Reliability Engineering, GovernanceAbstract
Artificial intelligence is now embedded in defect prediction, test prioritization, incident triage, release governance, and service observability. As these capabilities move into production workflows, software engineering organizations must evaluate more than predictive performance. They must show that model behavior is secure, explainable, auditable, and governable under drift, scale, and compliance pressure. This paper proposes a research grounded framework for building secure, explainable, and auditable AI systems for modern software engineering applications. The framework synthesizes trustworthy AI risk management, machine learning software engineering, explainability research, documentation practices, and cloud native observability. It argues that trustworthy AI in software engineering should be treated as a lifecycle systems property spanning data provenance, model design, evidence generation, runtime monitoring, human review, and post deployment accountability. The paper introduces architectural layers and control mechanisms for integrating security, interpretable evidence, and audit artifacts into AI enabled engineering platforms, and maps them to practical use cases including automated testing, defect forecasting, observability, document processing, and workflow governance.
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