The superior functionality and versatility of generative AI have raised expectations for the improvement of human society and concerns about the ethical and social risks associated with the use of generative AI. Many previous studies have presented risk issues as concerns associated with the use of generative AI, but since most of these concerns are from the user's perspective, they are difficult to lead to specific countermeasures. In this study, the risk issues presented by the previous studies were broken down into more detailed elements, and risk factors and impacts were identified. In this way, we presented information that leads to countermeasure proposals for generative AI risks.

Recently, various risks have been pointed out in artificial intelligence (AI) systems. In particular, AI security, AI ethics, and AI quality are considerable risks. To make AI systems trustworthy against these risks, risk assessment technology is needed to identify potential AI risks and decide which risks should be dealt with in priority. We propose a risk assessment technology that assesses three kinds of risks—AI security, AI ethics, and AI quality—which have been considered separately. Our technology follows the ISO 31000 framework, consisting of four phases: risk candidate identification, impact assessment, likelihood assessment, and priority derivation for countermeasures. To realize this technology, risk candidate identification is achieved by extending AI ethics impact assessment— an identification method of AI ethics risk—to AI security and AI quality. Impact and likelihood assessments are conducted by extending assessment methods for AI security to AI ethics and AI quality. We conducted a case study using our technology and confirmed that the risks were appropriately extracted, and the priority of the risks to be dealt with was derived.

The large demand for cost reduction of system operation has made the shift to runbook automation essential, because runbook enables both the operational cost to be reduced and operational quality to be improved. Because of the reduction in the cost of automation development, it is important to assess the operation manuals in advance and to select the operational tasks to automate. In the assessment, the development volume of automatic operational procedure should ideally be estimated so that it corresponds to the contents of all operation manuals. However, there was a problem that the estimation was rough in the current assessment carried out manually by sampling research. Thus, we propose an assessment method that can estimate more accurately from all operation manuals by considering how the common procedures are integrated in the actual automation development. The proposed method was shown to be practical by comparing the estimated value of the development volume of an automatic operational procedure, calculated by applying the method to actual operation manuals, with the result value. Moreover, the proposed method enables to forecast beforehand whether operation tasks can be automated easily by calculating the payback period on the basis of the estimated development volume of automatic operational procedure.

Recently, server consolidation using virtualization is increasing and needs for IT system operation automation are increasing, as well. To shift to automated operation, it is necessary to analyze operation manuals and to standardize procedures by selecting suitable ones. In an environment of server consolidation using virtualization, operation manuals for different systems are gathered for analysis. However, analysis of operation manuals for automation requires high computational complexity. Therefore, analyzing them manually is almost impossible and it is one of the causes that prevent migration from manual to automated operation. In this paper, we propose efficient and comprehensive analysis focused on the characteristic wording of operation manuals and apply natural language processing and data processing algorithms. We apply this approach to a real case of automated operation migration and show that it can accurately and quickly process the entire operation manual.