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With the rapid enhancements in technology and the adoption of web services, there has been a significant increase in cyber threats faced by organizations in cyberspace. Organizations want to purchase adequate cyber insurance to safeguard against the third-party services they use. However, cyber insurance policies describe their coverages and exclusions using legal jargon that can be difficult to comprehend. Parsing these policy documents and extracting the rules embedded in them is currently a very manual time-consuming process. We have developed a novel framework that automatically extracts the coverage and exclusion key terms and rules embedded in a cyber policy. We have built our framework using Information Retrieval and Artificial Intelligence techniques, specifically Semantic Web and Modal Logic. We have also developed a web interface where users can find the best matching cyber insurance policy based on particular coverage criteria. To validate our approach, we used industry standards proposed by the Federal Trade Commission document (FTC) and have applied it against publicly available policies of seven insurance providers. Our system will allow cyber insurance seekers to explore various policy documents and compare the paradigms mentioned in those documents while selecting the best relevant policy documents.
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A Semantic Approach for Automating Knowledge in Policies of Cyber Insurance Services
With the rapid adoption of web services, the need to protect against various threats has become imperative for organizations operating in cyberspace. Organizations are increasingly opting to get financial cover in the event of losses due to a security incident. This helps them safeguard against the threat posed to third-party services that the organization uses. It is in the organization’s interest to understand the insurance requirements and procure all necessary direct and liability coverages. This helps transfer some risks to the insurance providers. However, cyber insurance policies often list details about coverages and exclusions using legalese that can be difficult to comprehend. Currently, it takes a significant manual effort to parse and extract knowledgeable rules from these lengthy and complicated policy documents. We have developed a semantically rich machine processable framework to automatically analyze cyber insurance policy and populate a knowledge graph that efficiently captures various inclusion and exclusion terms and rules embedded in the policy. In this paper, we describe this framework that has been built using technologies from AI, including Semantic Web, Modal/ Deontic Logic, and Natural Language Processing. We have validated our approach using industry standards proposed by the United States Federal Trade Commission (FTC) and applying it against publicly available policies of 7 cyber insurance vendors. Our system will enable cyber insurance seekers to automatically analyze various policy documents and make a well informed decision by identifying its inclusions and exclusions.
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- Award ID(s):
- 1747724
- PAR ID:
- 10143552
- Date Published:
- Journal Name:
- 2019 IEEE International Conference on Web Services (ICWS)
- Page Range / eLocation ID:
- 33 to 40
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICWS.2019.00018
