Ethical AI for Cybersecurity: A Framework for Balancing Innovation and Regulation

This study introduces an ethical AI-based optimization framework for cybersecurity problems, targeting objectives such as building as much robustness and efficiency of detection as possible while decreasing computational complexity, all without violating ethical principles such as fairness and transparency. With an exploitative side of fast convergence and a contrastingly explorative one of diverse solution search, metaheuristic optimizers such as PSO and GA are incorporated for this framework. Real-world cyberattacks attempt with datasets such as CICIDS and UNSW-NB15 yielded system adaptability to a number of attacks at a mean F1 score of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{F}_{1}=0.92$</tex> under PSO and an extremely elevated robustness metric by GA of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R(w)=0.18$</tex>. SHAP and LIME explainability capabilities explain model predictions and highlight attributes such as packet size and connection duration as having the greatest influence on the model output. Adaptive learning of optimization parameters (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\alpha, \beta, \gamma$</tex>) equips the framework to be configured for different cybersecurity use cases to allow for tradeoffs between detection performance and adversarial robustness. Thus, this lightweight framework can be readily scaled to large datasets and high-dimensional use cases. Privacypreserving approaches like Federated Learning could go well within this framework, enabling real-time cybersecurity attacks to be fought in tandem with hybrid optimization strategies. These kinds of frameworks would help build secure, efficient, and ethically oriented AI systems where AI has to deal with security issues of the 21st century.