The COVID‐19 pandemic has impacted the world, prompting a shift toward remote work and stay‐at‐home economies, altering routines for individuals and businesses. Organizations have had to swiftly implement digital solutions to enable productive and efficient remote work, a trend that is becoming increasingly common. In this context, enterprise programmers often rely on open‐source software from social platforms to accelerate application development. However, the source code on these platforms may not always be regularly updated or well‐maintained, posing security risks. These risks are exacerbated when programmers need more security software‐focused development practices, testing for vulnerabilities, or applying necessary patches regularly. This study introduces two secure software development (SSD) performance baselines based on international standards and utilizing statistical process control (SPC): proactive information security awareness and reactive risk management. These baselines enable enterprise IT departments to monitor security awareness and improve the secure development capabilities of programmers and R&D teams, thereby mitigating the security risks of released software. A practical case study is presented to demonstrate the effectiveness of this approach.

Artificial intelligence algorithms and big data analysis methods are commonly employed in network intrusion detection systems. However, challenges such as unbalanced data and unknown network intrusion modes can influence the effectiveness of these methods. Moreover, the information personnel of most enterprises lack specialized knowledge of information security. Thus, a simple and effective model for detecting abnormal behaviors may be more practical for information personnel than attempting to identify network intrusion modes. This study develops a network intrusion detection model by integrating weighted principal component analysis into an exponentially weighted moving average control chart. The proposed method assists information personnel in easily determining whether a network intrusion event has occurred. The effectiveness of the proposed method was validated using simulated examples.