Artificial Intelligence-Powered Cyber Attacks: Adversarial Machine Learning

Advances in digital technology and the rise of cyber threats have created an increasingly complex cybersecurity environment. Using more cloud services and connected devices has allowed enterprises to increase the attack surface, growing systems' susceptibility to hacking. The current study aims to investigate artificial intelligence-driven (AI) cyberattacks targeting devices studying enemies. Reviewing adversary machine learning insights reveals critical weaknesses in AI design and the vulnerability of adversary attacks to affect images, leading to misclassification and security threats. Case studies like the Microsoft Tay chatbot tragedy and the Tesla Model S attack proved these vulnerabilities -Apart from how world applications undermine public security and trust in AI systems, the implications for society and the economy happened as emphasis needs to be placed on addressing ethical issues such as bias and privacy when implementing AI in cybersecurity. The study suggests that organizations should adopt adversarial training to increase the robustness of the machine learning model against adversary attacks. Similarly, moral explanation requires multiple approaches. International companies must understand cybersecurity legislation, including EU and UK legislation, to develop effective breach response strategies. The study recommends that organizations adopt a multifaceted approach to enhance defences against adversarial attacks, including updating models with adversarial examples, regularization, developing anomaly detection systems, and addressing bias and privacy concerns.