Ethical and Adversarial Risks of Generative AI in Military Cyber Tools

Abstract

Introduction: This paper discusses the ethical and adversarial implications of implementing generative AI in military cyber secure methods. Generative AI has been displayed in numerous applications for threat simulation and defense from threats in civilian use. Still, there are important ethical considerations in military use because of the potential misuse of generative AI. Cyber threats against military systems continue to grow more sophisticated than previously, and we hope to add data to the body of research in this area to help bridge the identified gap in understanding the risks of generative AI in a military context.

Objectives: The paper seeks to explore the ethical dilemmas, including accountability, autonomy, and misuse, surrounding military applications of generative AI. The paper examines adversarial risks associated with generative AI, including manipulation or other uses by hostile actors. The objective is to recommend measures for considering the ethical dilemmas, while at the same time improving the defenses.

Methods: The methodology will assess ethical risks such as autonomy, weaponization, and bias related to AI systems. It will determine adversarial risks by recommending using adversarial training strategies, hybrid AI systems, and robust defense mechanisms against adversarially manipulated AI-generated threats. It will also propose ethical frameworks and accountability models for military cybersecurity.

Results: This paper provides a comparative performance evaluation of military cybersecurity systems in a traditional and an AI-smart cyber context. The significant findings establish that generative AI potentially improves detection accuracy and, most notably, response times. It also introduces new risks such as adversarial manipulation. The experimentation results illustrated how adversarial training increases the robustness of models, reduces vulnerability, and provides greater defensive capabilities against adversarial threats.

Conclusions: Generative AI in military cybersecurity has considerable benefits compared to traditional methods, particularly in enhancing detection performance, response time, and adaptability. As illustrated, the benefits of an AI-enhanced system improved the accuracy of malware detection by 15%, from 80% to 95%, and a 15% increase in phishing email detection, from 78% to 93%. The ability to react quickly to a new threat was also key, as response time was reduced by 60%, from 5 minutes to 2 minutes, which is essential in military situations where responding quickly will minimize impact. Additionally, the AI systems showed the ability to reduce false favorable rates from 10% to 4% (which is excellent) and lower false negative rates from 12% to 5% (which is also that employed the AI system greatly based on its ability to identify what a real threat looks like and its=apability to identify a real threat.