Both AI and ML have positive and negative effects on cybersecurity. AI systems use data to learn how to respond in different situations. They then add additional information to their training data.
Image source: https://www.linkedin.com/pulse/impact-artificial-intelligence-cybersecurity-foluwa-t-rewane-ccta/
Challenges in Cybersecurity
· Due to the geographical distance, it is more difficult for cybersecurity experts to monitor and respond to incidents across regions.
· The manmade threat is frequent which brings unknown threats and malware which can be more expensive & time-consuming.
· The predictive nature of the threat is difficult challenging for cyber security genius. Most companies came across the threat after it happened and already create the destruction.
· Hackers are usually active to invade into user computer to do illegal activities such as a change in IP addresses, proxy servers. They are very to stay anonymous and undetected.
Relationship between AI and Cybersecurity
Artificial intelligence is one of the most commonly used tools for cybersecurity. According to a report by Norton, the average time it takes to recover from a data breach is almost 200 days. This is why it's important for organizations to invest in AI to minimize their risk of losing money and time. AI and machine learning can help security systems identify patterns in data to improve their performance and reduce incident response times.
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Improvisation in cybersecurity through AI
Traditional security techniques rely on indicators of compromise or signatures to identify threats. However, these methods are not always effective for new threats. AI can help detect about 90% of threats, but it can also lead to an explosion of false positives. Instead of replacing signatures with AI, the best approach is to combine both traditional and AI techniques. Through the use of AI, companies can enhance their threat hunting process by developing profiles of various applications within their networks.
Disadvantages of using AI for cybersecurity
· High Cost – Most companies invest lots of money to build and maintain AI systems.
· Set of Data - Each AI model is trained with learning data sets. The goal is to get a full understanding of the various data sets available to security teams.
· Useful for Hacker - Attackers use AI to develop new and improved malware that can resist security tools. They also learn how to attack traditional systems using these tools.
· Neural Fuzzing - Fuzzing is a process that uses AI to test software for vulnerabilities. It can be performed quickly and efficiently by taking advantage of the power of neural networks. This technique can help attackers learn about a system's weaknesses.
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