AI tools can sift through enormous amounts of data to look for patterns and learn about user behavior. This allows for the early detection of hackers before they cause harm.
Companies involved in cyber security are investing money into these technologies to fend off attacks and are starting to reap the rewards. AI-based technologies' capabilities are growing exponentially, enabling businesses to identify more sophisticated cyber threats before they materialize; more companies will likely start using AI tools as their usability increases.
As a result, more complex attacks will be recognized, making AI a crucial tool in the fight against cybercrime.
Artificial intelligence and machine learning are becoming more and more popular among businesses as
Artificial intelligence (AI) functions like a computer program focused on gaining success. Machine learning (ML), on the other hand, is a self-learning tool that evolves as it gains experience. Combining the two can guarantee accuracy and success in the cybersecurity industry.
Traditional Cyber Security
The fundamental problem with traditional cyber security measures is that it cannot keep up with the scale of the threat today. Conventional methods to collect and analyze information lead to an overload of data. It is labor-intensive and, therefore, prone to manual errors. Since they need more visibility into the network, it is also challenging to prepare against a potential threat.
AI in Cyber Security
In comparison, artificial intelligence in cyber security has a higher success in detecting possible threats and defending against them.
1. Network Threat Identification
This is the most basic use case for AI in cyber security today. More than 70% of businesses today are dependent on AI-enabled network security platforms. Furthermore, as enterprises share sensitive data over networks, AI-enabled systems are better equipped to protect the data transmitted or stored.
2. AI Email Monitoring
AI is used today to monitor incoming and outgoing emails to safeguard against cyber threats like phishing. The most probable risks are reported to the security personnel, and appropriate action can be taken. This becomes very important in the finance sector. Here, anomaly detection is used to identify phishing attacks and misdirected emails, prevent data breaches and identify other cyber security threats.
3. AI Endpoint Protection
Traditional anti-virus software can scan files for known viruses. The software cannot safeguard your data without security updates on new viruses. On the other hand, software that uses AI can detect a threat owing to anomaly detection or unusual behavior. Therefore it is better equipped to predict, detect and prevent a cybersecurity threat.
4. AI-based User Behaviour Modeling
In some cyber security attacks, the login id of a person can be manipulated by a complete takeover, without the person's knowledge. The only way to identify and stop this is by identifying a change in the behavior pattern of their activity. With AI technology solutions, such changes can be easily detected and security can then be alerted to investigate the matter further.
Conclusion
Cyber security systems with AI can anticipate a threat and deploy necessary action to prevent an attack. As a result, more and more corporations today are making special allowances in their budgets for upgrading to AI-enabled cyber security systems. As long as companies continue to feed accurate data, the system will swiftly detect any deviation from the baseline.
