BotNet News

With global cybercrime costing more than $23 trillion by 2027, it’s no wonder organizations are seeking new and improved ways to protect their data and networks. Enter AI cybersecurity, which uses proactive machine learning to enhance protection from threats that traditional signature-based methods may miss.

ML algorithms enable AI to learn and evolve from data collected on device, network and user behavior. This helps the system identify patterns of normal behavior that it can use to flag anomalies and distinguish between benevolent automated activity and malicious threats. For example, if an employee’s textual patterns do not match the average for that individual, AI can flag suspicious activity such as a spear phishing attack before it causes damage to business systems and assets.

The use of ML also enables AI to detect zero-day attacks that do not have a known code signature. It can do so by monitoring the behavior of devices and users compared to a model of typical behaviors, identifying deviations that indicate an attempted attack. AI can also prevent malware from spreading across your network by detecting and blocking malicious activities in real-time, minimizing the dwell time of attackers within your network and preventing costly damage to your organization’s reputation.

However, it’s important to note that like any software, AI can be subject to cyber attacks and exploitation. To mitigate these risks, it’s essential to ensure the implementation of secure development and deployment practices, conduct regular security assessments and penetration testing, and provide transparency into AI algorithms and decision-making processes.