Artificial intelligence and machine learning, in the proper hands, can improve cyber defenses, but in the wrong hands, they have the potential to cause significant harm.
Artificial Intelligence (AI) and Machine Learning (ML) are already commonplace in day-to-day lives, and cybersecurity is no exception. AI/ML can discover vulnerabilities and reduce incident response time in the right hands. However, in the hands of cybercriminals, they have the potential to cause notable damage.
According to 2019 Capgemini report “Reinventing Cybersecurity with Artificial Intelligence”, without AI, 61% of businesses admit they won’t be able to identify key dangers. Increased cyber threats that can rapidly compromise essential operations within an organization necessitate better skills, which AI is best suited to supply.
Here are three ways AI/ML is affecting cybersecurity in both positive and negative ways.
3 benefits of AI and machine learning in cybersecurity
Detection of fraud and anomalies
In cybersecurity, this is the most prevalent method where AI tools come to the rescue.
The performance of composite AI fraud-detection systems in spotting complex scam patterns is exceptional. Advanced analytics dashboards in fraud detection systems provide detailed information regarding occurrences. This is a critical issue in the field of anomaly detection in general.
Spam filters for email
To identify malicious emails, defensive rules block out communications containing dubious words. Spam filters also protect email users by reducing the time it takes to sort through undesirable messages.
Detection of botnets
Both supervised and unsupervised machine learning methods help detect and avoid complex bot attacks. Moreover, they aid in the detection of undetected attacks by identifying user behavior patterns with a very low false-positive rate.
Network traffic analysis, intrusion detection and prevention systems, secure access service edge, user and entity behavior analytics, and most technology domains according to industry experts all use AI/MI. In fact, it’s difficult to conceive a modern security tool that doesn’t include some sort of AI/ML magic.
3 negative aspects of AI and machine learning in cybersecurity
ML is utilized for better victim profiling through social engineering and other strategies. Furthermore, this piece of information is exploited by hackers in order to expedite cyber-attacks.
ML can conceal malware that monitors node and endpoint behavior and creates patterns that resemble legitimate network traffic on a victim’s network. It can also include a self-destructive feature in malware to increase the speed of an attack. Algorithms are trained to pull out data faster than a human, making it far more difficult to detect and avoid.
Ransomware is making a comeback -there are several examples of criminal success stories; one of the most heinous events resulted in a six-day suspension of the Colonial Pipeline and a US$4.4 million ransom payment.
It’s evident why AI and machine learning are gaining so much traction. The only method to combat devious cyber-attacks is to employ AI’s defense capabilities. The business world needs to understand how powerful machine learning can be when it comes to detecting anomalies (for example, in traffic patterns or human errors). Possible damage can be avoided or considerably mitigated with the right countermeasures.
Overall, AI/ML has a lot of potential for defending against cyber-threats. Several Government agencies and businesses are already employing or considering employing AI/ML to combat cybercriminals. While there are valid privacy and ethical concerns about AI/ML, governments should ensure that AI/ML regulations do not impede enterprises from adopting AI/ML for security as cybercriminals don’t abide by the law.
For more such updates follow us on Google News ITsecuritywire News.