RiskStudy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "suppressMessages(library(\"mc2d\"))\n",
    "library(\"scales\")\n",
    "library(\"ggplot2\")\n",
    "library(\"gridExtra\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Risk Study for REPLACE ME\n",
    "\n",
    "See the [ISO 27005 Risk Cookbook](http://www.businessofsecurity.com/docs/FAIR%20-%20ISO_IEC_27005%20Cookbook.pdf)\n",
    "for a more detailed explanation of this template."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Asset\n",
    "\n",
    "Define the asset or assets at risk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Threat Community\n",
    "\n",
    "Explain the threat community. This should include where they operate, how effective they are, and any additional details that help understand them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Threat Capability\n",
    "\n",
    "Define the ability for the threat agent to overcome the controls. The guideline for values here are as follows:\n",
    "\n",
    "|Rating                |Value |\n",
    "|----------------------|------|\n",
    "|Very High (Top 2%)    |98-100|\n",
    "|High (Top 16%)        |84-97 |\n",
    "|Moderate              |17-84 |\n",
    "|Low (Bottom 16%)      |3-16  |\n",
    "|Very Low (Bottom 2%)  |0-2   |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tcap.min    <- 0\n",
    "tcap.likely <- 50\n",
    "tcap.max    <- 100\n",
    "tcap.confidence <- 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Controls\n",
    "\n",
    "Define the controls that resist the threat community. Provide any necessary links and descriptions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Control Strength\n",
    "\n",
    "Define the ability of the controls in play to overcome the threat agents.\n",
    "\n",
    "|Rating                |Value |\n",
    "|----------------------|------|\n",
    "|Very High (Top 2%)    |98-100|\n",
    "|High (Top 16%)        |84-97 |\n",
    "|Moderate              |17-84 |\n",
    "|Low (Bottom 16%)      |3-16  |\n",
    "|Very Low (Bottom 2%)  |0-2   |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cs.min    <- 0\n",
    "cs.likely <- 50\n",
    "cs.max    <- 100\n",
    "cs.confidence <- 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Threat Event Frequency\n",
    "\n",
    "Threat Event Frequency. Number assumes an annual value. Example values\n",
    "are as follows:\n",
    "\n",
    "|Rating   |Value |\n",
    "|---------|------|\n",
    "|Very High|> 100 |\n",
    "|High     |10-100|\n",
    "|Moderate |1-10  |\n",
    "|Low      |> .1  |\n",
    "|Very Low |< .1  |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tef <- .25"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loss Magnitude\n",
    "\n",
    "Define the types of loss that could occur during a loss event for this study.\n",
    "\n",
    "|Primary        |ISO/IEC 27005 Direct Operational Impacts                                  |\n",
    "|:--------------|:-------------------------------------------------------------------------|\n",
    "|Productivity   |The financial replacement value of lost (part of) asset                   |\n",
    "|Response       |The cost of acquisition, configuration, and installation of the new asset |\n",
    "|Replacement    |The cost of suspended operations due to the incident                      |\n",
    "|               |Impact results in an information security breach                          |\n",
    "\n",
    "|Secondary               |ISO/IEC 27005 Indirect Operational Impacts                               |\n",
    "|:-----------------------|:------------------------------------------------------------------------|\n",
    "|Competitive Advantage   |Opportunity cost                                                         |\n",
    "|Fines/Judgments         |Legal or regulatory actions levied against an organization including bail|\n",
    "|Reputation              |Potential misuse of information obtained through a security breach       |\n",
    "|                        |Violation of statutory or regulatory obligations                         |\n",
    "|                        |Violation of ethical codes of conduct                                    |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Probable Loss\n",
    "\n",
    "Set the probable amount for a single loss event. This is a combination in dollars of both the primary and secondary loss factors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "loss.probable <- 100000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Worst Case Loss\n",
    "\n",
    "Set the worst case amount a single loss event. This is a combination in dollars of both the primary and secondary loss factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "loss.worstCase <- 1000000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Qualified risk based on loss tolerance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "loss.veryHigh <- 10000000\n",
    "loss.high <- 1000000\n",
    "loss.moderate <- 100000\n",
    "loss.low <- 50000\n",
    "loss.veryLow <- 10000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate distribution of samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "sampleSize <- 100000\n",
    "cs <- rpert(sampleSize, cs.min, cs.likely, cs.max, cs.confidence)\n",
    "tcap <- rpert(sampleSize, tcap.min, tcap.likely, tcap.max, tcap.confidence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
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     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "csPlot <- ggplot(data.frame(cs), aes(x = cs))\n",
    "csPlot <- csPlot + geom_histogram(aes(y = ..density..), color=\"black\",fill=\"white\", binwidth=1)\n",
    "csPlot <- csPlot + geom_density(fill=\"steelblue\",alpha=2/3)\n",
    "csPlot <- csPlot + theme_bw()\n",
    "csPlot <- csPlot + labs(title=\"Control Strength\", x=\"Sample Value\", y=\"Density\")\n",
    "csPlot <- csPlot + scale_x_continuous(breaks=seq(0,100, by=10))\n",
    "\n",
    "tcapPlot <- ggplot(data.frame(tcap), aes(x = tcap))\n",
    "tcapPlot <- tcapPlot + geom_histogram(aes(y = ..density..), color=\"black\",fill=\"white\", binwidth=1)\n",
    "tcapPlot <- tcapPlot + geom_density(fill=\"steelblue\",alpha=2/3)\n",
    "tcapPlot <- tcapPlot + theme_bw()\n",
    "tcapPlot <- tcapPlot + labs(title=\"Threat Capability\", x=\"Sample Value\", y=\"Density\")\n",
    "tcapPlot <- tcapPlot + scale_x_continuous(breaks=seq(0,100, by=10))\n",
    "\n",
    "grid.arrange(csPlot, tcapPlot, heights=4:5, ncol=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Vulnerability Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "CalculateVulnerability <- function() {\n",
    "    if (sampleSize < 100) {\n",
    "        stop(\"Sample size needs to be at least 100 to get statistically significant results\")\n",
    "    }\n",
    "\n",
    "    vulnerability <- 0\n",
    "\n",
    "    for (i in 1:sampleSize) {\n",
    "        if (tcap[i] > cs[i]) {\n",
    "            vulnerability <- vulnerability + 1\n",
    "        }\n",
    "    }\n",
    "\n",
    "    return(vulnerability / sampleSize)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loss Event Frequency Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "CalculateLossEventFrequency <- function() {\n",
    "    return(CalculateVulnerability() * tef)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Risk Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "CalculateRisk <- function(loss) {\n",
    "    if (loss >= loss.veryHigh) {\n",
    "        return(\"Very High\")\n",
    "    } else if (loss < loss.veryHigh && loss >= loss.high) {\n",
    "        return(\"High\")\n",
    "    } else if (loss < loss.high && loss >= loss.moderate) {\n",
    "        return(\"Moderate\")\n",
    "    } else if (loss < loss.moderate && loss >= loss.veryLow) {\n",
    "        return(\"Low\")\n",
    "    } else {\n",
    "        return(\"Very Low\")\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Annualized Loss Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "CalculateAnnualizedLoss <- function(lef, lm) {\n",
    "    return(lm * lef)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lossEventFrequency <- CalculateLossEventFrequency()\n",
    "worstCaseLoss <- CalculateAnnualizedLoss(lossEventFrequency, loss.worstCase)\n",
    "probableLoss <- CalculateAnnualizedLoss(lossEventFrequency, loss.probable)\n",
    "worstCaseRisk <- CalculateRisk(worstCaseLoss)\n",
    "probableRisk <- CalculateRisk(probableLoss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probable Risk: Low $12,510 \n",
      "Worst Case Risk: Moderate $125,100 \n"
     ]
    }
   ],
   "source": [
    "cat(\"Probable Risk:\", probableRisk, dollar_format()(probableLoss), \"\\n\")\n",
    "cat(\"Worst Case Risk:\", worstCaseRisk, dollar_format()(worstCaseLoss), \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Risk Treatments\n",
    "\n",
    "Document any risk treatments that may come out of this study."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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