Going from rigid risk assessments to a more flexible and quick method is what dynamic risk assessment (DRA) and decision systems (DSs) are all about. The purpose of this study is to look into how adding DS to DRA methods can improve safety. As the threats are always changing, DRA knows that risks can quickly get worse, which means that decisions need to be made in real time. This shows that flexible solutions are needed because traditional risk assessment methods don’t always work well with these changing problems. Expert systems, Bayesian networks, machine learning, and other methods are all part of decision systems. These systems can look at huge amounts of data, find trends, and make smart choices on their own or with help from a person. The ability to constantly measure and react to changing risk scenarios can be used by businesses that integrate DS into DRA systems. Cyberattacks are less likely to happen and have less of an effect when this method is used to find threats ahead of time, respond quickly, and come up with ways to limit their effects. After providing an overview of the paper’s main points, the abstract will talk about the integration framework, actual uses, obstacles, and future goals. It will also talk about what DRA and DS are and how important they are in defense. This paper shows through case studies and examples how useful DS-enabled DRA is in different safety situations, like finding threats, handling incidents, and making sure that rules are followed. Concerns about ethics and biases are also talked about as problems that might come up when DS is used in DRA, along with suggestions for how to solve these problems. The last part of the introduction talks about how dynamic risk assessment with decision systems can improve safety and make it easier to deal with new threats.