securing artificial intelligence: a multi-layered defense framework against adversarial attacks, data poisoning, and privacy breaches

Artificial intelligence (ai) is increasingly integrated into critical applications such as healthcare, finance, cybersecurity, and autonomous systems. however, this rapid adoption exposes ai models to various security threats, including adversarial attacks, data poisoning, model inversion, and unauthorized access. traditional security measures fail to address ai-specific vulnerabilities, necessitating advanced mitigation strategies. this paper provides a comprehensive analysis of ai security risks and proposes a multi-layered defense framework integrating adversarial training, homomorphic encryption, federated learning, and blockchain verification. experimental evaluations demonstrate improved model resilience while maintaining accuracy and efficiency. the findings contribute to the development of robust ai security mechanisms to protect against evolving threats.