Cyber Risk Modeling

When:Wed PM-3
OrganizersPhil Huggins Phil Huggins

Phil brings his extensive experience to a discussion on modelling (general) risk and comparing security risk modelling maturity to other markets (finance, insurance, medical..). This also involves attribution of $ value to risk and how security teams can talk the language of the business

Current Security Risk Management is Broken

There is a lot of complexity and uncertainty in cyber risk. Current practice tends to hide uncertainty and present certainty.

We use Ordinal Scales (Red, Amber, Green / High, Medium, Low / 1,2,3,4,5 etc) rather than Cardinal measures (£ or %). Is a red x red risk a really red risk? Twice as bad? Three times as bad? We then assign numerical values to support ‘risk arithmetic’ (5 x 5 = 25 /2.5 = risk score) OWASP Risk Rating Methodology (Risk Factors / Ordinal Scales)

We then use risk matrices that arbitrarily identify an ordinal boundary as the ‘risk appetite’. (Amber =Good, red = Bad).

By assigning a single value to probability and impact we are communicating a level of certainty about the outcome we don’t really have.

People are individually poor at prediction Hedgehogs / Foxes / Superpredictors

We are awash with data about cyber events but few documented robust statistical methods deployed.

The solutions are well known by other risk professions

Quantitative Risk Approaches

  • Probability of event
  • Range of outcomes (lognormal distribution)
  • Monte Carlo Simulation
  • Loss Exceedance Curves <- Business understands these
  • FAIR / OpenFAIR

Prediction Approaches:

  • Risk Panels
  • Averaged predictions
  • Feedback !!!!!!!!!
  • Brier Scores
  • Base Rate Data
  • Calibration


  • Dan Geer
  • Doug Hubbard
  • Philip Tetlock
  • Jack Jones
  • Ryan Huber

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