Today's world is highly network interconnected owing to the pervasiveness of small personal devices (e.g., smartphones) as well as large computing devices or services (e.g., cloud computing or online banking), and thereby each passing minute millions of data bytes are being generated, processed, exchanged, shared, and utilized to yield outcomes in specific applications. Thus, securing the data, machines (devices), and user's privacy in cyberspace has become an utmost concern for individuals, business organizations, and national governments. For last two decades AI/ML techniques have been widely employed in cybersecurity, for example, intrusion or malware detection and user authentication, among others. In this talk, I will brief on my AI-based research including a recent work on adaptive multi-factor authentication (A-MFA). We developed an authentication framework for adaptive selection of multiple modalities at different operating environment so to make authentication strategy unpredictable to hackers. This methodology incorporates a novel approach of calculating trustworthy values of different authentication factors while the computing device being used under different environmental settings. Accordingly, a subset of authentication factors is determined (at triggering events) on the fly thereby leaving no exploitable a priori pattern or clue for adversaries. Such a methodology of adaptive authentication selection can provide legitimacy to user transactions with an added layer of access protection that is not rely on a fixed set of authentication modalities. Robustness of the system is assured by designing the framework in such a way that if any modality data get compromised, the system can still perform flawlessly using other non-compromised modalities. Scalability can also be achieved by adding new and/or improved modalities with existing set of modalities and integrating the operating/configuration parameters for the added modality.