Experts believe that Artificial Intelligence (AI) and Machine Learning (ML) have both negative and positive effects on cybersecurity. AI algorithms use training data to learn how to respond to different situations.
Main Challenges Cybersecurity Faces Today
Attacks are becoming more and more dangerous despite the advancements in cybersecurity. The main challenges of cybersecurity include:
- Geographically-distant IT systems—geographical distance makes manual tracking of incidents more difficult. Cybersecurity experts need to overcome differences in infrastructure to successfully monitor incidents across regions.
- Manual threat hunting—can be expensive and time-consuming, resulting in more unnoticed attacks.
- Reactive nature of cybersecurity—companies can resolve problems only after they have already happened. Predicting threats before they occur is a great challenge for security experts.
- Hackers often hide and change their IP addresses—hackers use different programs like Virtual Private Networks (VPN), Proxy servers, Tor browsers, and more. These programs help hackers stay anonymous and undetected.
How AI Improves Cybersecurity
Traditional security techniques use signatures or indicators of compromise to identify threats. This technique might work well for previously encountered threats, but they are not effective for threats that have not been discovered yet.
Signature-based techniques can detect about 90% of threats. Replacing traditional techniques with AI can increase the detection rates up to 95%, but you will get an explosion of false positives. The best solution would be to combine both traditional methods and AI. This can result in 100% detection rate and minimize false positives.
Companies can also use AI to enhance the threat hunting process by integrating behavioral analysis. For example, you can leverage AI models to develop profiles of every application within an organization’s network by processing high volumes of endpoint data.
Improving Cyber Threat Detection With Machine Learning
In cybersecurity, foresight is priceless. Detecting cyber attacks in advance can give organizations the time they need to successfully neutralize these incoming threats. And it turns out that the application of machine learning to data analysis can help immensely in identifying them.
AI-Fueled Phishing Detection and Prevention
Phishing is the fraudulent practice of sending fake messages. Hackers use this all the time; they pretend to be from reputable organizations or groups so that victims either reveal personal information like passwords or install malware. AI and machine learning play an integral role in mitigating phishing attacks. Besides being able to respond much faster than a human can, these technologies can identify and track over 10,000 active phishing sources. They also allow for swift distinction between fake and valid websites. Because these technologies are now being employed around the world, AI’s knowledge of phishing campaigns isn’t relegated to only one geographic location.
Automated Network Security
Security policy development and organization network topography are two essential components of network security. Unfortunately, both take up a monumental amount of time and human effort to fulfill and manage. AI can automate both of these processes. By analyzing network traffic dynamics, AI can generate and recommend policies and procedures to fit your unique situation. The amount of time, energy, and money this could save organizations can’t be overstated.
Robust Behavioral Analytics
AI and machine learning can also be employed to improve behavioral analytics by studying your patterns. If an algorithm detects unusual actions that are outside your normal patterns, it can lock the culprit of this questionable activity out of your system. Massive shopping sprees, shipping products to addresses other than your own (e.g., why’d you ship that new game console to Beijing if you live in Los Angeles?), a sharp spike in uploads or downloads of files, and even a change in your typing pace can all alert AI to nefarious behavior.