Cybersecurity is a hot topic of discussion nowadays which is drawing the attention of the majority of the companies and finding various approaches regarding data protection. Due to the worldwide availability of Internet services, vulnerabilities are exploited in even more sophisticated ways by increasing array of malignant actors in a large number of verticals, such as insurance, healthcare, finance, high-frequency trading, machine learning and AI in cybersecurity. It leads to store the enormous amount of sensitive data which creates a new liability and challenges for security analysts. Artificial intelligence and machine learning algorithms have been considered as a prominent tool to secure users and organizations from modern cyber attack. The purpose of this article is to make people aware of the pitfalls and challenges which development teams usually face at the time of implementation of AI and machine learning in their security solution including various other ways by which they can improve Cybersecurity. So, defined below are a few certain paradigms that help us decide where we are now and what we can achieve following these steps. Will AI & ML really improve Cybersecurity? How effective are AI & ML to improve Cybersecurity? What are the various methods it will opt to meet the Challenges? What are the other applications which help to improve the vulnerabilities? What factors need considerations while developing AI-based Security System Solution? Conclusion. Will AI & ML really improve Cybersecurity? The Internet of Things (IoT) enables the system access through internet, which increases the data storage and adds more threat to each individual using various real-world interaction devices, for example webcams and autonomous vehicles. This creates a massive amount of data that makes it tedious to monitor. However, the traditional security system generates too many undifferentiated alerts which are quite difficult interpret and almost impossible to monitor by individuals. To eradicate the old signal monitoring procedure developers are developing a new approach to cyber-attack detection and response. It will help to process high volume data and detect suspicious activity quickly. How effective are AI & ML to improve Cybersecurity? As we are equipped with sophisticated advanced tools for cybersecurity protection, but still we breach data that may arise a risk of detection, investigation, and remediation of damage by security managers. Artificial Intelligence (AI) is the science of making a machine replicate the human intelligence. It involves various technologies, some exist and some of them are under development phase. Examples are Apple’s Siri is intelligent personal assistants, game-playing programs like AlphaGo, IBM Watson is designed for solving big data business analytics solutions. There are many US- and UK-based penetration testing companies bringing up advanced business analytics and cybersecurity solutions. Machine learning is a constituent of AI that uses mathematical algorithms to predict the patterns and tries to learn from those patterns. With the help of Machine learning, we try to interpret the anomalous behavior of users and systems to determine the possible threats, which may occur from different sources. Supervised and unsupervised learning helps to improve Cybersecurity developed under machine learning algorithms. What are the various methods it will opt to meet the Challenges? Security Information and Event Management (SIEM) applications are getting improved to implement machine learning. SIEM solutions comprise of event and log management, behavioral analysis, real-time monitoring of databases and applications. If SIEM applications find any suspicious activity, it pushes an alert and blocks the further access. According to Gartner, advanced analytics (AA) is basically a tool that has been designed in order to solve the wide range of data with the help of AI techniques. AA also tries to find deeper correlations, provide recommendations and make predictions as well. Deep learning algorithms is another technique which processes large volumes of data using neural networks that stimulates the activity of the human brain. Implementation of AI and ML is only possible by Big Data integration in cybersecurity. All security tasks have been split into five sub-categories: prediction, prevention, detection, response, and monitoring. The major implication of this technology is to find in the endpoints of anti-malware, application of WAF or database firewalls or user point anti-fraud analysis. There are a few examples which will implement the Machine learning in the cybersecurity tasks. Regression: Regression, in other words, prediction, helps to utilize the current knowledge of the data in order to find the opinion of the new data. In terms of cybersecurity, application of ML helps to determine the user’s behaviour as well as fraud detection. Classification: Classification is also referred to as a supervised learning in which, we are aware of what we are exactly looking for and classify our requirement into groups in the subconscious mind. Its major application is in network layer for intrusion detection system(IDS) and identifies different classes of network attacks such as scanning, spoofing, etc. Clustering: Clustering is similar to classification with a slight difference in it. In such analysis, we are not aware of the classes of our data or whether this data can be classified. It is employed basically in cybersecurity tasks, where we can apply classification. What are the other applications which help to improve the vulnerabilities? Gartner aspects further integration of ML and AL in cybersecurity solutions within the next five years. The cybersecurity industry is booming these days with the evolvement of new start-ups. With the increasing use of IOT, business is investing into modern threat cybersecurity that is an intelligent approach to cater more business in the cybersecurity world like Darktrace, CrowdStrike, Cylance and Amazon Macie, which is former Harvest AI. What factors need considerations while developing AI-based Security System Solution? For a new start-up in AI, there are certain challenges, which are to be faced while developing UEBA solution as discussed: Information about Infected data affected by malware. Predefined Enterprise rules. Lack of computing resources Insufficiency of experienced resources. Conclusion: To solve the Cybersecurity problems with AI and ML, we require the best computing resources. However, it’s not easy to employ the full potential of AI and ML in deploying such an advanced technology.