Chapter Outline
- Define knowledge base for the artificial intelligence (AI) project
- Risk and security knowledge development
- Objectives of the risk and security knowledge base
- Identification of knowledge for risk and security
- Knowledge acquisition for risk and security
- Knowledge sharing
Key Learning Points
- Learn and understand knowledge base
- Understand risk and security knowledge base development
AI can be used repeatedly in applications with different needs. Human resources has been tough for organizations, and organizations have attempted to capture and use the human intelligence required for different roles. The captured knowledge and intelligence are stored in the knowledge base. Knowledge models can be built, trained, and used when necessary.
Capturing human intelligence requires an integrated information system. The idea is to develop an integrated system that can provide the knowledge required by human experts. These integrated systems should be readily available and sharable. The integrated system should be used with strategic objectives in mind. This leads to the development of AI.
AI solves important challenges based on the information stored in the knowledge base. Human experts have cyclical demands in their subject matter expertise. Similar approaches are applied to risk management and are explored with use cases.
The knowledge base stores data, techniques, and algorithms that are used to drive the AI-integrated system. The integrated system will be introduced gradually in the approach. The objective of the AI-integrated system is to seek mitigation solutions to risk and security in corporate settings. The objectives will consider using business rules that consist of “what if” questions. This approach will use strategic rules and logic to determine possible mitigation solutions to risk and security using the stored data in the knowledge base. The AI-integrated system will impact setting knowledge objectives, identification of knowledge, knowledge acquisition, knowledge development, knowledge sharing, preservation of knowledge, fixing of knowledge, use of knowledge, evaluation of knowledge, and measurement of knowledge. Integration of AI case-based knowledge, representation of risk and security cases, identification of similarities, and connection are the building blocks of the knowledge base. Bizstats.ai uses the knowledge base to build AI-integrated systems from scratch.
Risk and Security Knowledge Development
The purpose of this section is to generate the risk and security knowledge required. This will include ideas, models, skills, processes, and system methods. Machine learning (ML) can have various forms. Pattern-based learning will be used when discussing ML and neural networks. This method provides appropriate knowledge from a large amount of risk and security data, enabling change of behavior of the captured data in the AI system.
Objectives of the Risk and Security Knowledge Base
Skills and knowledge will be developed using appropriate corporate objectives in the Bizstats.ai risk and security knowledge base.
The objectives of the risk knowledge base are as follows:
- To capture the risks and associated risks from the data, process, people, Internet of Things (IoT), systems, and actions.
- To determine occurrence.
- To determine impact of risks.
- To determine risk priority.
- To allocate risk owner.
- To determine risk mitigation.
- To determine risk action.
- To risk continuously in real-time and determine corrective actions.
The objectives of security knowledge base:
- To capture the threat and associated security risks from the data, process, people, IoT, systems, and actions.
- To determine threat occurrence.
- To determine impact of threats.
- To determine security threat priority.
- To allocate security owner.
- To determine security mitigation.
- To determine security action.
- To monitor security threat(s) continuously in real-time and determine corrective action.
- To recommend security training and efficient security process(s).
Identification of Knowledge for Risk and Security
The corporate setting will be used to model the skills and knowledge that enables AI to work well. This will require mapping the risk and security knowledge. Every effort will be made to store the data in a form that enables the data to be retrieved correctly. This AI system will allow access to the collected data. Ultimately, the system should build a corporate knowledge base capable of being extended with new risk and security data. The AI system will prevent any loss of information, retain information, and keep the risk and security data up to date. The system will automatically be capable of building the knowledge base, and additional information can be accessed externally.
Knowledge Acquisition for Risk and Security
Risk and security data will be collected using formal and informal channels. The data will be used internally and externally and will enable suitable competencies of the AI system. The data will ultimately be used with statistical, ML algorithms.
Knowledge Sharing
Risk and security knowledge sharing is a critical part of the knowledge management cycle. It is important to realize that people, technology, and the corporate world are part of this phase. AI knowledge sharing solutions consist of machine intelligence that is capable of learning from other AI systems through real-time connectivity using real-time application programming interfaces. It has been proven that discovering trends in a specific area such as risk and security mitigation can be effective. Another area where AI has been used efficiently is the vehicle manufacturing industry. Humans do not need to go through the repetitive nature of using the data; the computer system does the job efficiently without overwhelming. Bizstats.ai has access to real-time application programming interfaces to solve knowledge sharing problems.