CSIS and McAfee Collaborate: Economic Impact of Cyber Crime and Cyber Espionage

Michael D. Moberly   October 7, 2014    ‘A long form blog where attention span really matters’.

In 2013, CSIS (Center for Strategic and International Studies) and McAfee partnered to examine cyber – economic espionage impact in a manner more inclusive than what I have previously observed over the past 25+ years. Dr. James Lewis, Senior Fellow and Director of CSIS’ Center for Technology and Public Policy Program, who directed the study, offered his best guess that ‘the upper limit of the costs-losses attributed to cyber – economic espionage might be under one percent of the GDP’ (gross domestic product). Lewis also states, and I paraphrase, ‘U.S. economic costs-losses to cybercrime and economic espionage attributed specifically to – originating in China, may reach as much as $140 billion annually’.

Lewis translates the $140 billion annual IP loss to 508,000 jobs…

While I have no basis to dispute those figures, or question Dr. Lewis’ experienced and respected record of achievements in the cyber crime – economic espionage arena, I do suggest there are two key factors necessary to arrive at the $140 billion annual loss figure, i.e.,

  • determining which assets and/or impacts to include (factor) and
  • the methodology for determining the lost assets’ near and long term value in terms of costs and losses companies will experience with respect to such things as market space, competitive advantages, profitability, sustainability, etc.

But, Lewis claims, and I agree, describing value loss – impact estimates with broad ranges is indicative of the difficulty in calculating losses. Accordingly, companies may be reluctant to reveal (their) victimization impacts, i.e., victim companies may be inclined to (a.) conceal particular portions of their losses, or, (b.) not know how to distinguish which/what intangible assets were targeted, stolen, comprised, or misappropriated. But, Lewis wisely, casts wide ranging estimates of losses attributed to cyber – economic espionage in other contexts, starting with World Bank reports which state global GDP stood at about $70 trillion for the year 2011.  Thus, a $400 billion loss representing the high end range of probable losses attributed to cyber crime and cyber espionage is a fraction of a percent of the global GDP figure. This, Lewis says, prompts additional questions, several of which I have been examining for many years, e.g. who are recipients and/or ultimate beneficiaries of the acquired (intangible) assets; can they expect to – be positioned to maximize those benefits, e.g., market (space) position, sector competitive advantages, reputation, value, sources of revenue, profitability, etc.

Conventional loss surveys assess – assign dollar value to losses… Some IP and intangible asset theft – loss estimates rely on surveys, which Lewis correctly points out, generally produce imprecise findings because among other things respondents, are inclined to “self-select” which can become a source of distortion to the findings. Lewis suggests loss estimates should be based on “scale and effect” which ‘will likely produce quite different and possibly more objective and accurate results in terms of adverse impacts and loss values’.

CSIS – McAfee Assessment model… Lewis’ intent was to bring greater clarity and validity to the loss figures being reported, so data from ‘car crashes’, ‘retail pilferage/shrinkage’, ‘crime stats’, and ‘drug usage’ were examined for their relevance and comparison as methodologies to draw upon insofar devising CSIS’ assessment (valuation) model. By incorporating these analogies into the design of their loss valuation assessment model, Lewis, and McAfee were suggesting it’s problematic to rely on conventional (existing) survey methodologies to calculate dollar value for losses, because, among other things…

  • companies that (publicly) reveal their losses are frequently unfamiliar with distinguishing the actual (proprietary, IP, intangible) assets which were stolen, compromised, or infringed, thus more guesstimates.
  • intellectual property – intangible asset losses are difficult to quantify because relevant dependant variables are often absent from the equation, and, often
  • the self-selection process associated with most conventional survey methodologies, frequently produces distortion in the findings.

CSIS model includes components – classifications of malicious cyber activity and economic espionage…

This, Lewis gleans, by asking ‘what should be included and counted insofar as arriving at more precise loss estimates’, i.e., there…

  • was a loss of intangible assets, i.e., intellectual property, sensitive business confidential/- proprietary information.
  • was an actual crime committed, i.e., a violation of federal law.
  • were opportunity costs, i.e., business and/or service disruptions that adversely effected consumer/customer expectations, particularly those related to the victimized company’s online activities.
  • would be additional costs incurred relative to…
    • re-securing their IT networks.
    • achieving greater company resilience insofar as to recovering from future cyber – economic espionage attacks, and
    • developing/executing business continuity plans designed to provide more rapid and fuller recovery when future attacks occur.
  • were damages to company reputations which tend to have a longer period for recovery, and lastly,
  • were costs to re-establish and re-secure company supply chain networks.

What’s the harm…?

If Lewis is correct in inferring there have, inadvertently, become “tolerated costs” and/or ‘ceilings’ for estimating losses.

So, a different perspective; is economic-cyber espionage the greatest transfer of wealth in history, or merely a rounding error in countries’ GDP…?

This, of course represents a perspective intended to elevate the significance and acknowledge the adverse impact of cybercrime-economic espionage, while the former represents a perspective intended to diminish the ‘sticker shock’ of the adverse economic impacts by characterizing them as percentages of national GDP’s.

As always reader comments are most welcome.

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