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The future of cybersecurity is AI-driven.
FREMONT, CA: As cyber-attacks get more complex in nature and targets, cybersecurity professionals must have the right visibility to decide how to remediate vulnerabilities accordingly, and AI can assist in coming up with issues that it is human can't alone. It's become evident that AI can both programmatically think wider, faster, and further outside the norms, and that is true of several of its applications in cybersecurity now too. Here are some use cases of AI in cybersecurity.
• Working with Staff
AI can work alongside cybersecurity staff to keep the enterprise safe. Machine learning, a subset of AI, can read the SoC analysts' input and transpose it into a database, which becomes ever-widening. When SoC analyst enters similar symptoms, they are presented with previous cases and the remedies, based on statistical analysis and the use of neural nets. AI can analyze the incident's features and suggest which SoC engineers would be the strongest team to resolve the problem based on past experiences.
• Battling Bots
Bots make up the majority of all internet traffic today. Most of them are dangerous. From account takeovers using stolen credentials to fake account creation and theft, they pose a real cybersecurity threat. But enterprises can't fight automated threats with human responses alone. They must implement AI and machine learning if they are serious about tackling the 'bot problem'. To truly differentiate between good bots, bad bots, and humans, firms must use AI and machine learning to develop a comprehensive understanding of their website traffic.
• Endpoint Protection
AI can play a key role in protecting endpoints. This is becoming important as the number of remote devices leveraged for work rises. By following best practices and staying current with patches and other updates, an organization can be reactive and stay safe against threats. AI may give IT and security professionals an advantage against cybercriminals. AI-powered endpoint protection takes a different tack by creating a baseline behavior for the endpoint through a repeated training process.