AI-Powered Cybersecurity: Revolutionizing Business Threat Detection and Response
Prottoy Khan, Md. Zahirul Islam, Sazib Hossain · American Journal of Smart Technology and Solutions · 2025
The modern day enterprise infrastructure needs cybersecurity as a crucial element to protect against increasing cyber threats that have multiplied because of digital business expansion. Security technologies that exist conventionally manage certain threats decently but lose their effectiveness when new forms of sophisticated cyberattacks emerge. Machine learning together with deep learning using anomaly detection methods enables Artificial Intelligence to function as an advanced security technology that boosts detection and response functions. The paper investigates how Artificial Intelligence cybersecurity systems modernize business defenses against threats and security incidents. An AI-based algorithm analyzes a dataset containing network logs and authentication trials together with encryption protocols and reputation scores of IP addresses to identify malicious occurrences. Different machine learning models with both supervised classification approaches together with unsupervised anomaly detection methods undergo assessment for determining their threat identification capabilities. The analysis verifies how AI solutions perform better than conventional rule-based procedures in identifying and obstructing cyber threats. Additional hurdles in the way of these methods include both false detection alerts and privacy security threats and adversarial attack vulnerabilities. The paper assesses AI security framework effects on the business field through suggested future developments for enriched AI threat detection and response techniques. The research shows that cybersecurity strategies must continue model training along with developing ethical practices for AI systems while combining these techniques with traditional security defense methods.