How AI Can Improve Cybersecurity | Arvig Blog Skip to main content
Reading Time: 6 minutes
AI Digital Image

How AI Can Improve Cybersecurity

Important points for Cybersecurity Awareness Month

Cyber-attacks continue to grow in volume and sophistication making “smart cyber” capabilities urgently needed by an ever-expanding number of businesses. Artificial intelligence (AI) has already dramatically improved business processes and manufacturing efficiency. When applied with a range of technologies like machine learning (ML), AI could be a gamechanger in managing cyber security threats, with better response times and efficiency.

AI may also help companies facing severe labor shortages among those IT professionals with advanced cyber-security knowledge. Since October is Cybersecurity Awareness Month, we’ll explore how AI can be an ally to security teams, and the businesses that rely on them. Here are a few key ways. 

1. Early detection of cyber threats
Machine learning assists by using algorithms on gathered data and statistical analysis, mapping out assumptions about a computer’s behavior. Systems can then make updates, adjusting algorithms to optimize performance. As machines “learn” and increase capability, they are able to detect threats and anomalies more accurately than any human, and at a much greater volume.

Machine learning can help AI distinguish between normal activity and abnormalities at a speed that no human review could approach. This provides a window for organizations to neutralize incoming threats. The potential for advance warning, potentially avoiding a major breach putting data and whole systems at risk, is incredibly valuable. 

Machine learning is also instrumental in addressing the sheer volume of cyber-attacks. In 2018, there were 10.5 billion malware attacks, far too many for human IT teams to address without help. 

2. Better phishing detection and preventionWhile malware attack numbers dipped during the pandemic, mobile phone phishing attempts surged by 37% during the exodus of people from offices to work from home, according to Verizon’s 2021 Data Breach Investigation Report. A phishing scam is the fraudulent practice of sending fake messages to your phone or computer to gather personal information like passwords or to install malware. Hackers may assume a friend’s identity, pretend to be from a reputable organization like a bank, law enforcement or community group. Phishing emails are so prevalent that one in every 99 email messages is believed to be an attempted attack.

So how can AI and machine learning mitigate phishing attacks? In addition to being able to react faster than a human to a breach, smart technologies can monitor over 10,000 active phishing sources by identifying them, then tracking activity to prevent an attack.

Cybersecurity for a laptop

3. Improved vulnerability management
The 1970s is recognized as the beginning of the Information Age, shifting from the Industrial Revolution to an economy based on information technology (IT). Today, our personal lives are digitally connected, and every modern business relies on information technology. Along with technological advancements came a proliferation of bad actors attacking systems for profit or malice.

According to the 2021 Global Risk Report, cyber attacks, being the fifth, top-rated risk of 2020, have become a new norm for almost every industry and business type. Avast reports the key finding is that the overall chance of business users encountering a cyberthreat has increased worldwide year over year by 24% from 11.25% in 2020 to 13.9% in 2021. While this global number may seem pretty small, the rate by which threats are increasing is alarming.

AI and related applications can continuously check systems for flaws, repair and improve IT systems. Think of corporate AI as your anti-virus/malware software on steroids, combing for relevant information related to dark web forums, hacking trends and reports, and other developments relevant to the IT needs of business. AI helps with risk assessments, not only identifying existing flaws, but also predicting vulnerabilities that could expose a business to future attacks.

4. Amping up password protection and authentication
Consider passwords as the weakest link in security control, as they are often the only barrier between criminal activity and our identities. Biometrics was said to be the greatest advancement to password security, until serious gaps were discovered.

For example, I have never been able to successfully use my fingerprint scanner on my laptop. Facial recognition has proved to be a challenge in recognizing people of color and women. Yup, I can tell you my appearance when I first get out of bed is drastically different than when I have full makeup and hair done to host a Zoom meeting.  When biometrics work good for some but not all, it is not an effective solution to employ.

In addition, white hat hackers proved how quickly they could bypass biometric security.

However, developers are applying AI to make biometric authentication more robust and reliable. A good example is Apple’s Face ID, which uses infrared sensors to create an advanced representation of a face, and allows it to recognize key similarities. Apple claims there is a one-in-a-million probability of bypassing this improved biometric system. It’s not hack proof, but it’s better. The company is reportedly working on a sophisticated version of Face ID to recognize a face wearing a COVID-19 mask. 

See my companion article this month which digs a little deeper into the use of facial recognition, including racial implications.

No login screening is 100% safe, so until AI catches up, make sure and set a strong password.

5. Automated network security
IT and cyber-security departments have their hands full, with updates to security policies, as well as maintaining and updating the physical structure of the network, also known as topography. Both of these areas take a ton of human resource hours to manage. However, AI can automate many of the processes within these two areas. Through analysis of network traffic, AI can recommend and generate new policies and procedures customized to a business’ particular needs, saving time, energy and money.

6. More robust behavioral analytics
AI and machine learning is already evident in our daily lives. For example, what ads or articles show on your smartphone is based on studying your behavior patterns. Cataloging what you have read, viewed, or purchased, how long you stayed on which types of sites, your favorite login times, and patterns in how you text and browse. 

So how does this help cybersecurity? AI algorithms can detect unusual actions that are outside your normal patterns, such as shipping expensive products you purchase to a far away place you have never been to, logging in from a new device or IP address, an increased volume of uploads or downloads, or even recognizing a difference in how your keys hit the keyboard. Potentially, a bad actor creating suspicious activity could be locked out of your account by cybersecurity protocols applying AI to your account management.

Investigate smarter cybersecurity for your business
Understand the impact of exponential growth of threats and educate yourself about smart cyber solutions for your industry. You can start small and scale a practical strategy that applies AI technologies to your cybersecurity plan. Look for opportunities that deliver high impact without being overly complex, based on readily available data. Always keep top of mind that as fast as AI cybersecurity grows, there are bad actors also using smart technologies and getting more sophisticated by the day.

Related Posts