Expert Systems (ES) have revolutionized decision-making in business, healthcare, and education, but their reliance on opaque AI models raises critical challenges in trust, privacy, and security. This work addresses these gaps by introducing a robust, multi-tiered framework that integrates Explainable AI (XAI), cryptographic safeguards, granular access controls, and compliance mechanisms. The current work empirically validates the proposed approach on three real-world benchmarks: MIMIC-III for clinical diagnostics, the Adult Income dataset for financial automation, and EdNet for adaptive learning systems. The analysis made in the paper reveals that adversarial attacks, biased outcomes, and data leakage risks can be mitigated effectively through hybrid XAI methods (SHAP and LIME) and privacy-aware techniques like federated learning and homomorphic encryption. Experimental results show a 30–45% reduction in susceptibility to attacks (like model evasion, membership inference) with less than 5% compromise in predictive performance. The framework not only enhances auditability but also aligns with GDPR, HIPAA, and FERPA standards, providing a deployable blueprint for ethical ES adoption in high-stakes environments.