Using AI In Cyber Security

In a rapidly transforming threat landscape, cyber defense solutions must be both innovative and flexible.

These challenges however are compounded by the individualised characteristics of a given network, as each demands a system that understands its unique threats. A significant change can come with the effective use of Artificial Intelligence (AI) as part of an overall cyber security strategy.

A typical cyber-attack is an attempt by cyber criminals to gain access to data and or modify or otherwise damage their target’s computer system or network.Using AI, cyber criminals can also gather incredibly detailed personal information from the Internet and social media, allowing them to conduct ever more in-depth social engineering. AI could also be used to improve the success rate of phishing scams.

These can be easy to spot because they typically display poor spelling and grammar, but using AI can dramatically improve this, and learning algorithms mean they will only get better. Added to this is the fact that AI can generate attacks far faster than any human could, so the potential of the threat cannot be ignored.

Advanced AI and Machine Learning (ML) tools are becoming increasingly critical in detecting and combating cyber threats.

AI can be used to identify system or device vulnerabilities and other likely malicious acts. It is a fact that traditional systems cannot keep up with the sheer number of malware generated each month, so it has become one of many prospective areas for AI to move in and resolve the issue.

Here are some of the ways these technologies will make a difference by giving cybersecurity the much-needed boost.

Machine Learning in Cyber Threat Detection. Organisations must be able to detect a cyber-attack in advance to be able to counteract whatever adversaries seek to achieve. Machine learning seems to be the aspect of AI, which has proven extremely useful in detecting cyber threats based on data analysis and finding a threat before leveraging a flaw in the information systems.

Machine learning helps computers to employ and adjust algorithms based on obtained data, learn from it, and understand the necessary improvements. In a cyber security sense, this would mean machine learning allows the computer to detect threats and identify any anomalies even more accurately than any human would.

Traditional technology relies heavily on past results and cannot improvise as AI can. Classic technologies cannot address hackers’ latest techniques and tricks as AI can. Additionally, the amount of cyber threats people face every-day, is too much for humans and is managed better by AI.

AI-powered Password Protection and Authentication. Passwords have always been a very weak security control and they are most often the only link between cybercriminals and our identities. Biometric authentication is being evaluated as an alternative to passwords, but it’s not very convenient, and attackers can also easily bypass these controls. Developers are utilising AI to improve current biometric authentication and eliminate any imperfections to make it a robust application.

One example is Apple’s face recognition technology that is currently used on their iPhone X smartphones. Called Face ID, the device detects the user’s facial features by built-in infrared sensors and neural engines. AI software produces a sophisticated face model by recognising key similarities and patterns.

AI and ML in Phishing Detection and Prevention Control. Phishing is one of the most used cyber-attack methods where hackers attempt to deliver their payload using a phishing attack. Phishing emails are extremely common and once opened, the email will contain a link luring the victim to install malware or one of the hacker-favorites, ransomware, onto their device.

AI and ML will play a major role in mitigating and thwarting phishing attacks. AI and ML can identify and respond much faster than humans can. AI and ML also work to monitor phishing threats from around the world, and its knowledge of phishing campaigns is not limited to any single geographic region. AI also allows fast distinction between a fake and a valid website.

Network Security and AI. AI will make our lives much easier, but also lead to the obsoleting of many technologies we currently use. It may also lead to certain positions or jobs becoming obsolete. Two essential aspects of network security are security policy development and the network topography of an organization.

Both tasks can be time-consuming and take up a lot of human effort and time. AI can be uswed to automate these processes by analysing and studying network traffic dynamics and recommending policies and procedures. This not only saves time, but also a lot of energy and money that we can devote to technical growth and enhancement areas.

Conclusion
In the current cybersecurity environment, adversaries are employing increasingly sophisticated algorithms and diversified methods, blacklists, rules and behavior-based cyber operations. Traditional, reactive measures are no longer enough.

Organisations need to quickly identify where intrusions occurred, the likely attack vectors moving forward and how to quickly remediate exploited vulnerabilities, all in a shortened window of response time.

With its ability to introduce workflow automation, behavior and streaming analytics, active monitoring, intelligent prediction and advanced network threat detection, AI can play a major role.

While AI is doing cyber security wonders, it is also making its way to hackers for malicious purposes. In the wrong hands, it can cause exponential harm and be an even bigger danger to cybersecurity. As AI sees more progress, we will indeed be witnessing how far the technology can go and in how many ways it will benefit us and our future generations.