Content Analysis of Cyber Insurance Policies: How do carriers write policies and price cyber risk?

Romanosky, Sasha; Ablon, Lillian; Kuehn, Andreas; Jones, Therese

doi:10.7249/wr1208

Cited by 29 publications

(24 citation statements)

References 5 publications

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“…A diverse set of data sources is needed to study the interaction between insurance pricing, the effectiveness of security controls, and the cost of auditing claims. To this end, we combine the following data sources: a US law requiring insurers to report pricing algorithms [6], analysis of a data set of over 12, 000 cyber events [2], a study of the cost and effectiveness of security controls [31], and a range of informal estimates regarding the cost of an information security audit.…”

Section: Methodology and Data Collectionmentioning

confidence: 99%

“…Skeptics instead focused on the "high correlation in failure of information systems" [17,18,19], citing it as a major impediment to the supply of cyber insurance. Recent empirical work [6] analyzing 180 cyber insurance filings shows that the cyber insurance market is viable.…”

Section: Related Workmentioning

confidence: 99%

“…Romanosky et al [6] introduced a data set resulting from a US law requiring insurers to file documents describing their pricing schemes. Pricing algorithms depended on the applicant's industry, revenue, past-claims history, and-most relevant to this paper-the security controls employed by the organization.…”

Section: Introductionmentioning

confidence: 99%

“…Pricing algorithms depended on the applicant's industry, revenue, past-claims history, and-most relevant to this paper-the security controls employed by the organization. The insurer collects all this information and sets a price according to the formulas described in [6], reducing the premium when security controls are reported to be in place. This was corroborated by interviews with insurance professionals in Sweden [7].…”

Section: Introductionmentioning

confidence: 99%

“…An early study found that coverage was excluded for a "failure to take reasonable steps to maintain and upgrade security" [8]. A study from 2017 found few exclusions prescribing security procedures but the majority of policies contained exclusions for "dishonest acts" [6]. One such dishonest act is violating the principle of up-most good faith requiring insureds to not intentionally deceive the company offering the insurance [9].…”

Section: Introductionmentioning

confidence: 99%

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Post-incident audits on cyber insurance discounts

Panda

Woods

Lászka

et al. 2019

Computers & Security

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We introduce a game-theoretic model to investigate the strategic interaction between a cyber insurance policyholder whose premium depends on her self-reported security level and an insurer with the power to audit the security level upon receiving an indemnity claim. Audits can reveal fraudulent (or simply careless) policyholders not following reported security procedures, in which case the insurer can refuse to indemnify the policyholder. However, the insurer has to bear an audit cost even when the policyholders have followed the prescribed security procedures. As audits can be expensive, a key problem insurers face is to devise an auditing strategy to deter policyholders from misrepresenting their security levels to gain a premium discount. This decision-making problem was motivated by conducting interviews with underwriters and reviewing regulatory filings in the US; we discovered that premiums are determined by security posture, yet this is often self-reported and insurers are concerned by whether security procedures are practised as reported by the policyholders.To address this problem, we model this interaction as a Bayesian game of incomplete information and devise optimal auditing strategies for the insurers considering the possibility that the policyholder may misrepresent her security level. To the best of our knowledge, this work is the first theoretical consideration of post-incident claims management in cyber security. Our model captures the trade-off between the incentive to exaggerate security posture during the application process and the possibility of punishment for non-compliance with reported security policies. Simulations demonstrate that common sense techniques are not as efficient at providing effective cyber insurance audit decisions as the ones computed using game theory.

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Section: Methodology and Data Collectionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Post-incident audits on cyber insurance discounts

Panda

Woods

Lászka

et al. 2019

Computers & Security

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Enterprise security economics: A self‐defense versus cyber‐insurance dilemma

Miaoui

Boudriga

2019

Appl Stoch Models Bus & Ind

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We propose a model that optimizes enterprise investments in cybersecurity using expected utility theory. The model allows computing (a) investment in self-defense to reduce the risk of security breaches, (b) investment in cyber insurance to transfer the residual risk to insurance companies, and (c) investment in forensic readiness to make the insured firms capable of generating provable insurance claims about security breaches. A three-phase-based model of vulnerability rate evolution over time is proposed and used to estimate the different planned security expenditures throughout the investment horizon.At the starting time of investment, a decision maker invests to cover the existing risk of breach and periodically spends to cover the additional risk observed due to the release of new vulnerabilities. In this work, the intermediate tranches are determined while considering three different attitudes of decision makers, namely, optimistic, pessimistic, and realistic. An analysis is conducted to assess the performance of the proposed models. KEYWORDSforensic readiness, information security, optimal investment, risk minimization 448

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Cyber-Warranties as a Quality Signal for Information Security Products

Woods

Simpson

2018

Lecture Notes in Computer Science

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Consumers struggle to distinguish between the quality of different enterprise security products. Evaluating performance is complicated by the stochastic nature of losses. It is recognised that this information asymmetry may lead to a "market for lemons" in which suppliers face no incentive to provide higher quality products. Some security vendors have begun to offer cyber-warranties -voluntary ex-ante obligations to indemnify the customer in the event of a cyber attackto function as a quality signal. Much like how consumer protection laws are relatively more costly to firms offering low quality products, cyberwarranties are more costly for firms developing low quality enterprise security products. In this paper, we introduce a decision-theoretic model to explore how consumers might use cyber-warranties to increase information when purchasing security products. Our analysis derives four inferences that consumers can make about a security product. We discuss the difficulties customers might face in using these inferences to make real world decisions.

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Content Analysis of Cyber Insurance Policies: How do carriers write policies and price cyber risk?

Cited by 29 publications

References 5 publications

Post-incident audits on cyber insurance discounts

Post-incident audits on cyber insurance discounts

Enterprise security economics: A self‐defense versus cyber‐insurance dilemma

Cyber-Warranties as a Quality Signal for Information Security Products

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