How to secure the Software Development Lifecycle with Machine Learning
AI, which includes machine learning, is in high demand in the software industry. From identification to classifying bugs and sending alarms, machine learning can auto-complete the tasks. ML can identify thousands of bugs per day. Bugs can be different types like security and non-security bugs, critical or non-critical bugs, etc. ML can distinguish and prioritize the bugs and serially send them to the developers.
Software developers need to spend most of their work time finding bugs. It distracts their focus from the core job requirements. They also need to delay the delivery time. Even tools sometimes send false alarms that are like an extra burden. While managing manually, many bugs remain unnoticed, which creates errors in the production level. To tackle these problems, developers invented a new technology called machine learning. This article talks about how developers can make ML effective to secure the software development process.
Secure Software Development Lifecycle with Machine Learning
Building a high-level machine learning system to identify and classify bugs (security, non-security, critical, not critical, etc.) requires data, model, training, and experiment. Below are some steps to make ML capable enough for the software development lifecycle:
Collecting Data for Machine Learning
ML requires a high volume of good data to learn, identify bugs, and secure the development lifecycle. This data includes bugs with labeled security and non-security so that the machine can learn the differences and take action.
The data must include a large number of examples. Though it’s hard to collect perfect data, collecting and feeding trustable data is a better option. Developers must ensure that the data has no usage restriction. For an effective result, developers can generate data in a lab or a simulated environment.
Approval, Modeling, and Evaluation
After finding the data, the security expert team needs to review all the data. If the data is error-free, they send an approval message confirming the data is correct to use. Then the data scientists can create a model and train it with the data. After completing the training process, they can evaluate the performance.
If the model shows a satisfactory result in finding an average number of bugs without manual processing, it is ready to work in a real-life environment.
Machine Learning and Security
Machine learning learns from previous information. It can identify minor bugs that humans are unable to notice and solve. From writing code to delivering the software, ML can secure every stage of the development life cycle. Training the machine with a high volume of complex data makes it more operative. The models also need to train from time to time with the new data. Otherwise, it becomes unable to cope with the new types of bugs. Once an organization creates a robust machine learning model, it auto-secures the software development life cycle.
The higher accuracy level of the Machine Learning program increases the confidence of the developers. They make innovative software without worrying about security. It ultimately increases the profit of the organization.