Before this, fragmented computer networks struggled to communicate seamlessly. The introduction of the Transmission Control Protocol/Internet Protocol (TCP/IP) enabled consistent data transfer and became the standard for digital communication. However, this node-centric approach, which relies heavily on Internet Protocol (IP) addresses, has also created significant security vulnerabilities and privacy concerns due to its focus on network nodes rather than the data itself. In today's digital landscape, the centralized aggregation and storage of sensitive user data -- including IP addresses -- by service providers pose substantial security risks. These centralized repositories are prime targets for cyberattacks, potentially compromising user privacy and exposing sensitive information. Additionally, the reliance on IP-based system modeling has amplified these risks, necessitating a shift toward a more secure and resilient design approach. This paper proposes a novel data-centric design methodology that moves away from traditional node-focused models. By prioritizing data as the central entity and incorporating multimodal frameworks encompassing objects, events, concepts, and actions, this approach enhances data security and flexibility. The new informatics domain model reimagines data's role in system design, emphasizing its importance throughout its entire lifecycle to foster innovation, security, and seamless data interoperability.

Data-centric AI: The term "data-centric AI" refers to the use of machine learning techniques and algorithms that are optimized for specific kinds of data. This approach is particularly effective in domains where there is a shortage of representative and labelled datasets. Industries such as healthcare, manufacturing, and agriculture often have large volumes of unlabeled data and require an AI model to be trained from these sources. A data-centric approach emphasizes the technical aspects of a task instead of focusing on the algorithm itself. To demonstrate how useful, such an approach is, Andrew Ng and his team organized a competition called the Data-Centric AI Competition.

This is the second post in "AI role in CyberSecurity" series by #AILabPage, first post is available here It's easy to describe & define Artificial intelligence compare to what actually it is. Now to put it in one liner "AI is kind of intelligence demonstrated by machines to do the same task done by any human using natural intelligence". In other words same task performed by Human with Natural Intelligence and Machine with Artificial Intelligence should produce same results. Speed, quality and productivity are the measuring units here. Cybersecurity protect internet-connected systems, including hardware, software and data, from cyber attacks.