World Journal of Engineering Research and Technology

Siddharth Nandagopal*

ABSTRACT

This paper introduces the Secure-Explainable Agentic AI (SEAAI) framework to address critical trade-offs between explainability, security, and performance in agentic AI systems. The proposed conceptual model combines theoretical constructs, metrics, and game-theoretic approaches to achieve balanced outcomes across diverse application domains. By integrating explainability compliance measures, security risk assessments, interpretability scoring methods, performance trade-off calculations, and adaptive explainabilitymechanisms, the framework guides decision-makers in navigating complex design choices. Through conceptual analysis and qualitative case studies, the work establishes a robust foundation that accounts for adversarial influences, regulatory demands, and user expectations. The framework’s game-theoretic perspective incorporates defenders and attackers into a multi-stakeholder environment, ensuring dynamic resilience against evolving threats. The result is a flexible and generalizable reference point for aligning ethical standards and legal requirements with operational goals. Ultimately, this research fosters better understanding of how to configure explainability without compromising security or performance. Such insights empower developers, regulators, and users to embrace agentic AI responsibly. Future efforts can refine and extend the SEAAI framework, encouraging interdisciplinary collaboration and continuous improvement. In doing so, the journey toward trustworthy, interpretable, and safe autonomous systems advances, paving the way for widespread real-world adoption.

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