4 Ways AI Can Reduce Cybersecurity Threats

The world of cybersecurity is like a battlefield. The tools available to security experts are also used by criminals. That is why the former have their hands full, trying to outdo very smart nefarious individuals. 

Let’s take the example of artificial intelligence (AI). Security experts are implementing the use of these technologies to fight online crime.

Yet, the criminals, as we stated, are using the very same technology to improve their attacks. The sophistication levels are so high that many available security tools can’t win.

This scenario further reinforces the need to take personal online security seriously. Use the necessary preventive tools like antivirus, antimalware, and anti-ransomware.

Route all your internet traffic through a residential proxy. These proxies get their IP from an ISP and not a data centre. That means you don’t share a server with many other users.

By hiding your IP address, cybercriminals cannot track your online movements. The proxies will also scan the internet traffic into and out of your browser for malware.

So, how are security experts using AI to fight cybercrime?

1.    Threat Detection With AI

A significant contribution of AI to cybersecurity lies in threat detection. AI and ML analyze tons of data and can identify threats. The rate at which cybercriminals churn out new attacks is astounding.

Look at the evolution of phishing attacks as an example. Before, it would be easy to identify and avoid such threats. Now, AI phishing allows for tailoring or emails to specific circumstances and characteristics. 

At this very moment, you could be opening an email sent to you by an AI tool. Machine learning (ML) technologies learn behavior and find ways to gain your trust. It could explain why phishing continues to remain very popular amongst cybercriminals.

But, traditional security tools cannot keep up with identifying emerging threats. AI provides an effective solution to this. The teams develop advanced ML algorithms for pattern recognition and malware detection.

The software can then detect deviations or anomalies, allowing for quick action. The technologies can also track multiple sources of such attacks. It does not matter which geographical location they are coming from. This would not be possible using manual labour.

And, the proactive nature of AI systems is another plus. They do not wait for an attack to happen before taking action. The software looks for vulnerabilities allowing for pre-emptive action.

The tools will scan the hang-out places for hackers. These include dark web discussion forums. They also identify hacker patterns and track cybercriminals to determine sources of threats. The insights provide fantastic tools for security experts to plan well.

2. Ransomware and Malware Prevention With AI

Cybercriminals hit pay dirt when they held a US insurance company hostage with a ransomware attack. Reports alleged that CNA Financial had to part with a cool $40 million to get back access to its networks.

There was a 171% increase in ransomware payment in 2020 from the previous year. Organizations have to stay alert to the very real possibility of such attacks.

The challenge for security experts is hackers can bypass multi-factor authentication. They may even use bots to do so. AI-powered security can identify the threats at such points. It can, for example, identify suspicious log-in attempts.

AI and ML technologies are helping in the prevention of ransomware. The incorporation of bots in patch management is proving quite effective. AI bots use algorithms to determine updates or risk levels at the endpoints.

The software relies on historical and current data for the necessary  builds by establishing baseline behaviour at the endpoints. Anything outside of the behaviour pattern results in immediate flagging. The security teams can then take quick action.

AI bots also allow for large-scale management of all the network endpoints. The automation saves the team’s valuable time.

 3. AI in Credit Card Fraud Detection

A look at credit card fraud statistics can make you afraid to use your card.

• In 2020 identity theft cases were 1,387,615
• There was a 1,663% increase in benefits and government document fraud

• Credit card fraud in 2020 rose by 44.7% from the previous year to reach 393,207
• There were over 300 million victims of data breaches in 2020

Financial institutions are now adopting ML and AI to fight such crimes. The software allows for the detection of any unusual activities around card usage. This could include a large number of  purchases from different devices.

Abnormal behaviour around transactions will also raise flags. The technologies look at historical trends to identify what is outside of the norm. These could include a spike in large purchases happening within a short period.

There is also higher accuracy in detecting fraud. The Ai software looks at many factors to determine whether fraud is happening. Through learning, the ML algorithm keeps evolving with the changing environments.

They can identify suspicious patterns. It allows for the prevention of scams, by establishing new preventive rules.

4. Identity Proofing With AI

One of the methods cybercriminals use is to create false identities or credentials. They would use such in educational institutions, healthcare facilities, or even financial services. The aim is to breach the systems to defraud the organization.

Identity proofing provides a solution through proper verification of new customers. It happens when they are submitting applications for different services. AI uses neural networks or facial recognition to confirm the authenticity of documents or photo IDs.

The software can cross-match the facial images present in the identity documents. AI and biometric authentication reduce reliance on traditional methods like PINs and passwords. AI-powered 3D facial recognition reduces the fallibilities of 2D identification.

The ML learns from many pictures. The software can even detect if the person is using a mask or tampered documents.

Final Thoughts

We can all agree that cybercriminals are a clear and present danger in the digital space. We also know that they are here to stay.

Security experts have to evolve their methodologies to counter cyber-attacks. And, they are finding a lot of success with AI and ML.

The software helps in the early detection of attacks. The teams get instant notification, thus allowing for pre-emptive rather than reactive measures. The AI can go through tons of data and use ML pattern recognition to identify anomalies.