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1 _________________________________________________________________________ www.irp-management.com Cyber Security Page: 1 Date: 27 januari 2017 Draft version Cyber Security Economics Hans Oosterling January 2017 General Framework -version 0.1-

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_________________________________________________________________________www.irp-management.com Cyber SecurityPage: 1 Date: 27 januari 2017 Draft version

Cyber Security Economics

Hans OosterlingJanuary 2017

General Framework-version 0.1-

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Markets for Information Goods

Data and Software– High fixed Costs unlimited volume, low or near zero variable costs

– Strategy: Growth Increase switching costs, lock-in customers

– Information Asymmetry in the market Markets of lemons, “bad quality drives out good quality” Metcalf’s Law: network value is proportional to the square of number of

users (like telephone)

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Classical Economics

Market price tends to the marginal costs– Marginal costs = variable costs + fixed costs / volume

The variable costs for information good producers is near zero so they will go for ever increasing market share and market dominancy (or even for monopoly)

Strategy of information good providers prefer market share over improved quality

Traditional business€

#Fixed/Volume

Variable

SW business

Fixed/VolumeVariable

#

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Market for Security Services / Products

The market of security services / products is battlefield for market dominancy and a race for ever increasing market share

Asymmetric incentives– Unsecure PIN entry device, could be solved by acquiring bank (of the merchant)

but issuing Bank (of the customer) bear the risk of fraud or skimming

Information Asymmetry– Buyers can’t assess the quality of the security software, so the market price will

tend to the cheapest (and possibly the less quality) product, so there is no incentive to invest in good quality software

– Solving the asymmetry by introducing certification, protocols, guarantees etc– Providers go for market share and after reaching market dominancy, act like a

monopolist with pricing close to “willingness to pay”

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Monopoly

If you know what everyone wants to / can pay, charge them accordingly (price differentiation)

Assume fixed price, revenue will be you miss out the customers who wants to pay less and there is lost revenue from the customers who were willing to pay more than the fixed market price

In a perfect market with many suppliers, price erosion will occur and tends to lowest level

Fixed Market price

enterprises students

Price

Volume

LostCustomers

Lost Revenue

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Security Management

Risk Reduction / Mitigation– Mitigated Risk, residual Risk

Risk Acceptance– Incorporate security risk into your general business risk

Risk Avoidance– Forgone profits from risky activities

Risk Transfer– Insurance– Moral Hazard– Lack of Historical Data (legislation on data breach reporting)

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Optimal Risk Mitigation

ExpectedLoss

Cost of Security Mitigation

SecurityExpenses

FinancialImpact €

MitigatedRisk

ResidualRisk

OptimalInvestment levelInformation Security

According to Gordon-Loeb modelInvestment level max 37% of expected Loss

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Security Metrics

Proactive tasks– CSA Control Matrix

133 identified controls by Cloud Security Alliance– Security Maturity Model (BSIMM)

Reactive tasks– Patch management– Intrusion detection – Incident management– Forensics

Events are difficult to measure and the effectiveness of more security measures is difficult to verify

– Many anticipated threats never materialize– Some of the unanticipated threats do occur

Controls Vulnerabilities Incidents (Prevented)Losses

Stochastics, event-drivenDeterministic, action-driven

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Closing Remarks

Cyber crime appears in the late 80th carried out by lone amateurs, but nowadays it’s a professional international business (teamwork)

Defense is always behind newly created attacks and law makers areslowly following new criminal inventions

Allocation of liability is difficult and complex:– Network providers (telecom, WAN, LAN etc)– SW suppliers (infrastructure, applications, antivirus providers etc)– HW suppliers– Internet users (DDoS meant insecure systems at firm A could harm firm B)

Police is biased: Bank robbery of > € 5 mio gets full attention but a cyber criminal stealing € 500 from 10.000 internet users has no priority (and is often difficult to investigate)

People don’t act rationally: underestimating risk factors they can’teasily understand or imagine (and overestimating the likelihood of events which can be thought of easily)