Cybercriminals live on the cutting edge of technology, and nothing fits that label better than artificial intelligence. This helps them design sophisticated and scalable malware, impersonate superiors, and even successfully imitate biometric data like voice.
The use of AI in cybersecurity has developed as a natural response to these new and unpredictable challenges. How are cybersecurity experts using artificial intelligence to thwart the bad guys? The following eight use cases will tell you everything you need to know.
1. Threat prevention and pre-emption
It is not uncommon for businesses and organizations to experience persistent attacks. Cyber threats can infiltrate deep into their networks and wreak havoc for months before being detected. Because AI models have large data sets of past behavior, they can detect abnormal behavior much more quickly.
Preventing attacks before they are deployed is one of the most desirable goals of cybersecurity. If you have the right information, this can become a reality. For example, a cybersecurity team can use a proxy network to regularly scrape content from forums and other sites dedicated to hacking. They can then act on the information gathered and confront future attacks head-on.
2. Rapid Incident Response
Even an AI-enhanced cybersecurity framework cannot stop all incoming attacks. Someone could connect an unauthorized device or an update could contain malicious code. Regardless, robust cybersecurity AI can respond quickly to such incidents, blocking or removing offending actors.
3. Data protection
Data is the foundation on which modern economies operate. That you get it via the web scraping API, surveys, as part of your daily operations, etc., the data you collect needs powerful protections. AI can help by automatically classifying and encrypting them. Access control is another process you can automate, as is compliance with data protection laws like GDPR.
4. Endpoint Security
Endpoints like PCs or smartphones are gateways between secure corporate networks and the Internet. Antivirus and antimalware software are traditional means of protecting these endpoints. They too must develop to face an ever-changing threat.
Virus and malware protection previously relied on lists of previously identified threats. These are ineffective because AI-based malware can bide its time before deploying or masquerade as an innocent system process. AI allows these security tools to take a behavior-based approach. By inferring malicious intent based on patterns rather than viruses and malware documented in the past, the cybersecurity tools you implement can confidently address emerging and even changing threats.
5. Spam and phishing prevention
Detecting the spam that threatened to choke millions of email accounts in the early 2000s was one of the first large-scale implementations of AI and machine learning in particular. Spam still bombards us daily, but AI algorithms have become even more sophisticated at identifying it and relegating it to the trash.
Phishing is another old cyber threat that AI, or rather large language models, are revitalizing. recognize it before it was trivial, especially since the senders were not linguistically competent enough to write convincing messages. AI-powered phishing scams are more convincing because they better mimic human expression. Then, fighting fire with fire produces excellent detection and prevention results.
6. Advanced Multi-Factor Authentication
Password strengths vary widely, from unique, complex passwords that provide real protection to weak variations on themes that hackers can bypass in seconds. Unfortunately, even the strongest passwords are not safe from theft or compromise. MFA is a second protection designed to prevent someone who copied or stole your password in a breach from accessing the associated account.
Conventional MFA remains effective, but hackers are starting to leverage AI to circumvent it. This also applies to conventional authentication codes and biometrics. Fortunately, AI is playing a leading role in revolutionizing our approach to biometrics.
For example, keystroke dynamics allow AI to authenticate a user based on learned keystroke idiosyncrasies. Keyboard typing is part of a broader set of behavioral biometrics encompassing other behaviors such as mouse cursor movement, screen pressure in the case of smartphones, etc.
7. User profiling
Although not authentication in the conventional sense, deep user profiling is another security measure made possible through machine learning. It works by analyzing individual users and their expected behaviors. For example, a user may frequently access the same directory or use only a handful of services.
Changes in this behavior may be harmless, but they may also indicate a malicious insider or account takeover.
8. Fraud detection
A secure payment gateway is the main prerequisite for any reputable online business. Bad actors may want to exploit its weaknesses and carry out fraudulent transactions. The AI’s strange pattern recognition abilities also come in handy here. An AI can evaluate large volumes of transactions, identifying outliers while allowing regular payments to pass through unhindered.
These are the most promising use cases of AI in cybersecurity. We hope you found something useful.
Blog courtesy of AT&T Cybersecurity. Author Anas Baig is a product manager at Securiti.ai. Regularly contributed guest blogs are part of MSSP alerts Sponsorship Program. Read more cybersecurity news and guest blogs from AT&T here.
