Application of AI and ML in Cloud Security
Artificial Intelligence (AI) and Machine Learning (ML) are now integral parts of cybersecurity development. Due to the rapid spread of cloud technology, companies are using AI and ML to create new solutions and tools that save time and reduce human error.
As a highly specialized field, businesses often find a workforce shortage in the cybersecurity world, meaning it can be difficult to provide the necessary protection for their high-demand cloud services. So, AI and ML are also becoming essential to protect cloud security and prevent unauthorized data breaches.
Confidential computing is a cloud computing technology that separates and encrypts sensitive data within a protected CPU area during processing. As business leaders depend on public and hybrid cloud services, data privacy is an ultimate necessity.
For years, cloud service providers have provided data encryption services to secure data ‘at rest’ (in storage or a database) and data ‘in transit’ (shifting data through a network connection). Confidential computing removes the gap and enhances data protection by adding data ‘in use’ (encrypting data during processing). This continues to be paramount in cloud security moving forward.
DevOps Secure Automation
With the increasing demand for efficiency, DevOps are becoming more automated; it’s no longer just about development and operations. Companies that use DevOps want to enable more IT security in it to keep consistent productivity. This is what they call DevSecOps.
In the DevOps approach, software and feature releases occur in real-time. So unlike before, security is now included in the end-to-end distributed responsibility for the DevOps team. DevSecOps automates these security processes to run DevOps workflows smoothly, meaning that organizations can focus less on network security issues and more on their development tasks.