The Application of AI in Cybersecurity

Auto Tech Outlook | Wednesday, October 14, 2020

FREMONT, CA – The incorporation of artificial intelligence (AI) and machine learning (ML) has opened new windows of opportunity for cybersecurity. AI has proved its limitless potential across various sectors. Its implementation not only helps the organizations in safeguarding against existing cyber threats but it also aids in the detection of new malware and viruses. 

The organizations can utilize the vast databases of digital footprints compiled by security specialists to train the AI in identifying the common hacking patterns. It will enable the AI-powered cybersecurity solutions to track intrusion signatures and take necessary precautions to mitigate the damage.

For instance, many organizations neglect to incorporate everyday devices such as video cameras, printers, and so on into their security strategies. As a result, the hackers gain access into the broader network by breaching the lax security measures at specific points. However, the utilization AI in cybersecurity can help the organization in scanning the entire network for weak spots and securing them against cyberattacks.

Several organizations have already implemented AI based cybersecurity measures in their operations. For instance, Gmail utilizes AI spam filter to detect spam emails. Also, the AI-based fraud detection feature of MasterCard uses advanced algorithms to predict customer behavior and assess the authenticity of the transactions.

However, AI learning is not without flaws. For instance, a recent experiment conducted by a research team proved the existence of vulnerabilities in the learning-based systems of self-driving cars. The team used simple graffiti sprays to alter the road signs, successfully forcing the vehicles to misclassify the signals.

The additional concern plaguing the implementation of effective cybersecurity measures is the ability of sophisticated hackers to trick the AI learning. Skillful hackers usually erase their digital footprints through tunneling protocol and log file alteration. Since the AI learning utilizes the signature databases to train itself, the absence of intrusion patterns will obstruct its capability.

However, several developers are working on integrating blockchain technology and AI to design cybersecurity systems that can prevent log tampering. The decentralized, cryptographically sealed log will make it virtually impossible for hackers to alter the files through traditional hacking methods, no matter the level of sophistication.

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