Applications of AI in Cyber Security
Adding an additional layer of protection to existing enterprise security
Ever since the coronavirus pandemic began, people have started to work from home where they have little to no protection for their home networks. Hackers and malicious users have been taking a lot of advantage of this.
In 2020, 80% of companies in the US have seen an increase in cyberattacks. Ransomware attacks rose 148%. The monetary damage is projected to hit $6 trillion annually by 2021. Cloud-based attacks rose 630% between January and April 2020.
This spike of attacks has definitely kept cybersecurity experts busy. However, it is impossible for any human to go through gigabytes of logs to find an attack in order to take the necessary steps to prevent it. This is where Artificial Intelligence comes to the rescue.
AI can be used to learn how to remove noise or unwanted data, helping security experts understand the environment to detect any abnormal activity. It can also analyze massive amounts of data and allow further development of systems and software to reduce cyber attacks.
Artificial Intelligence techniques are playing an increasingly important role in antivirus detection. Heuristic techniques, data mining, agent techniques, artificial immune, and artificial neural networks are some of the AI techniques proposed to be applied in antivirus detection.
One of the well-known players implementing AI in cybersecurity is Cylance. Cylance Smart Antivirus is a consumer antivirus product, bringing enterprise-level AI protection to households. It relies entirely on AI and ML to distinguish malware from legitimate data. The result is an antivirus that waits until the moment of execution and immediately kills the threat without any human intervention. It saved a lot of customers from the attack of the infamous Wannacry Ransomware.
The AI-based vulnerability management systems are proactive in detecting the vulnerabilities instead of waiting for the IT team to take the necessary actions on them. They can analyze the pattern and discover the loose ends that can be the potential vulnerability.
After recognizing the attackers’ patterns, infiltrating methods can be discovered and it becomes easy to distinguish when and how any vulnerability would make its way to the network or system.
User and Entity Behavior Analytics
User and Entity Behavior Analytics or UEBA is a cybersecurity process that detects insider threats, targeted attacks, and financial fraud. AI in UEBA can enhance the pattern recognition abilities to find any anomalies. If any malware is introduced in the system, the working way would be changed, and that’s where the AI would detect the abnormality and report it to the authorities.
Some examples are detecting phishing emails, a rogue employee sending out insider information, etc.
Most organizations and individuals still use the traditional methods of authentication of username/email and password. And the major drawback of a simple username-password is that most people use the same password for most of their accounts. And not a lot of those passwords are strong. AI can help detect weak passwords and suggest best practices to strengthen them.
And moreover, modern biometric authentication like face recognition and iris recognition help add the extra layer of protection. The use of AI in biometrics has ensured that cybercriminals cannot hack them.
Drawbacks of AI in Cyber Security
While implementing AI in this field has its own advantages, there are a few hindrances.
In order to build and maintain AI systems, cybersecurity companies need expertise in building these systems and different data sets of malware codes, non-malicious codes, and anomalies.
These systems also take a lot of computing power, raw memory, data, and more. Running them becomes a costly affair than most companies can afford.
The AI systems, if not properly trained and tested, can produce a lot of false-positive predictions about malware and vulnerabilities which can become ineffective for the customers.
Apart from cybersecurity firms using AI to protect their customers, it can also be used for unethical purposes as well. Hackers and other cybercriminals can use the AI to train their malware to become AI resistant and behave in a more random way compared to traditional malware.
The solution to the drawbacks
Knowing these limitations and drawbacks, it’s obvious that AI is a long way from becoming the only cybersecurity solution. The best way is to implement traditional techniques with AI systems.
A good team of both AI and cybersecurity experts working in cohesion will help any organization strengthen its security posture.