WO2008054403A2 - Systèmes et procédés pour identifier, catégoriser, quantifier et évaluer des risques - Google Patents

Systèmes et procédés pour identifier, catégoriser, quantifier et évaluer des risques Download PDF

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Publication number
WO2008054403A2
WO2008054403A2 PCT/US2006/044228 US2006044228W WO2008054403A2 WO 2008054403 A2 WO2008054403 A2 WO 2008054403A2 US 2006044228 W US2006044228 W US 2006044228W WO 2008054403 A2 WO2008054403 A2 WO 2008054403A2
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asset
level
risk
sig
matrix
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PCT/US2006/044228
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WO2008054403A9 (fr
WO2008054403A3 (fr
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Paul J. Frankel
Richard E. De Francesco
Robert K. Gardner
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Probity Laboratories, Llc
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Publication of WO2008054403A3 publication Critical patent/WO2008054403A3/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/08—Insurance

Definitions

  • the present invention relates to risk analysis and management, and more particularly to systems and methods for identifying, quantifying and evaluating risks in various contexts and for various purposes.
  • an asset can be analyzed into its various levels of sub-assets in a top-down manner.
  • lowest level sub-assets can be analyzed into components and elements of such components.
  • comprehensive and orthogonal threat probability and vulnerability data can be input for each of the elements of each component of each lowest level sub-asset.
  • such data can be input in the form of a threat probability matrix and a vulnerability matrix.
  • the input data can then be processed to generate an output set for each such sub-asset comprising a combined threat/vulnerability matrix, an index of overall risk vulnerability, or "Figure of Merit" (FOM) and associated retained risk.
  • FOM Figure of Merit
  • For each component and level of sub-assets such an output set can then be processed into combined output sets for the higher-level assets of which they are a part, proceeding back up the asset analysis tree.
  • This can provide an accurate risk calculus for the top-level asset and each level of sub-asset identified in the top-down analysis.
  • outputs can be displayed in various display modes, and an optional iterative risk remediation process can also be performed.
  • a risk calculus can be used to augment, maximize or exploit an adversary's vulnerabilities.
  • Fig. 1 depicts a high-level exemplary process flow according to exemplary embodiments of the present invention
  • Fig. 2 depicts a detailed process flow for asset analysis according to an exemplary embodiment of the present invention
  • Fig. 3 depicts a detailed process flow for computation of Special Interest Group ("SIG") Values and FOM at an individual SIG level according to an exemplary embodiment of the present invention
  • Fig. 4 depicts a detailed process flow for computation of Enterprise Level Gradient Matrix
  • Fig. 5 depicts an exemplary process flow for iterative risk remediation according to an exemplary embodiment of the present invention
  • Fig. 6 illustrates an exemplary asset analysis for an exemplary transportation sector according to an exemplary embodiment of the present invention
  • Fig. 7 illustrates an exemplary asset analysis for an exemplary pharmaceutical sector according to an exemplary embodiment of the present invention
  • Fig. 8 illustrates an exemplary asset analysis for an exemplary automotive sector according to an exemplary embodiment of the present invention
  • Fig. 9 illustrates an exemplary asset analysis for an exemplary enemy targets scenario according to an alternative exemplary embodiment of the present invention
  • Figs. 10 — 17 illustrate exemplary threat probability matrices for each defined component of an exemplary SIG according to an exemplary embodiment of the present invention
  • Figs. 18 — 25 illustrate exemplary threat vulnerability matrices for each defined component of an exemplary SIG according to an exemplary embodiment of the present invention
  • Figs. 26 - 33 illustrate exemplary weighted value arrays for each defined component of an exemplary SIG according to an exemplary embodiment of the present invention
  • Figs. 34 — 41 illustrate exemplary build coefficient matrices for each defined component of an exemplary SIG according to an exemplary embodiment of the present invention
  • Figs. 42 and 43 illustrate exemplary component-level build coefficient matrices for each defined component of an exemplary SIG according to an exemplary embodiment of the present invention
  • Fig. 44 illustrates an exemplary SIG-Level Build Coefficient Matrix according to an exemplary embodiment of the present invention
  • Fig. 45 illustrates an exemplary SIG Matrix according to an exemplary embodiment of the present invention
  • Fig. 46 is a graphic presentation of the exemplary SIG Matrix of Fig. 44 and the FOM derived therefrom;
  • Fig. 47 is the SIG Matrix of Fig. 45 as used in an exemplary enterprise level calculation according to an exemplary embodiment of the present invention
  • Fig. 48 depicts an exemplary SIG Matrix for an Amtrak Trenton Station SIG according to an exemplary embodiment of the present invention
  • Fig. 49 is a graphic presentation of the exemplary SIG Matrix of Fig. 48 and the FOM derived therefrom;
  • Fig. 50 depicts an exemplary Real Value Factors Matrix for an Amtrak 30 th Street Station SIG according to an exemplary embodiment of the present invention
  • Fig. 51 depicts an exemplary Proportional Real Value Factors Matrix for the Amtrak 30 th Street
  • Fig. 52 depicts an exemplary Real Value Factors Matrix for the Amtrak Trenton Station SIG according to an exemplary embodiment of the present invention
  • Fig. 53 depicts an exemplary Proportional Real Value Factors Matrix for the Amtrak
  • Fig. 54 depicts an exemplary Consolidated Real Value Factors Matrix for the Amtrak N.E.
  • Corridor enterprise according to an exemplary embodiment of the present invention
  • Fig. 55 depicts an exemplary FOM Build Coefficients Matrix for the Amtrak 30 th Street Station
  • Fig. 56 depicts an exemplary FOM Build Coefficients Matrix for the Amtrak Trenton Station
  • Fig. 57 depicts an exemplary FOM Consolidated Build Coefficients Matrix for the Amtrak N.E.
  • Corridor enterprise according to an exemplary embodiment of the present invention
  • Fig. 58 depicts an exemplary Enterprise Level Gradient Matrix for the Amtrak N.E. Corridor enterprise according to an exemplary embodiment of the present invention
  • Fig. 59 is a graphic presentation of the exemplary enterprise matrix of Fig. 58 and the FOM derived therefrom;
  • Fig. 60 depicts overall process flow for an exemplary Amtrak Level 3 Asset comprising an exemplary Amtrak N.E. Corridor enterprise, from data entry for the respective components of the exemplary Amtrak N.E. Corridor enterprise through outputs at the Amtrak Level 3 Asset;
  • Figs. 61-63 depict in greater detail the overall process flow of Fig. 60;
  • Fig. 64 illustrates an iterative risk remediation process according to an exemplary embodiment of the present invention.
  • Fig. 65 illustrates an exemplary 2-D display of a SIG matrix according to an exemplary embodiment of the present invention.
  • a user can, for example, enter threat and vulnerability data for an asset into a 12x6 matrix.
  • a 12x6 matrix in exemplary embodiments of the present invention, is the primary data structure by which threat and vulnerability data can be entered, stored and thereafter processed to quantify risk values.
  • FIG. 1 depicts an overall (high-level) process flow according to an exemplary embodiment of the present invention.
  • Figs. 2 through 5 then illustrate in greater detail the functionalities depicted in Fig. 1. With reference to these figures, an exemplary embodiment of the present invention will be next described.
  • Exemplary embodiments of the present invention can be, for example, implemented in a software program or set of interrelated programs using known techniques. Such programs can be provided in software, in hardware or in any combination thereof.
  • an input and user interface module which allows a user to import data files or to input data in real time, as well as to input display and processing parameters and generally exercise control.
  • Such a module could, for example, prompt a user for inputs and provide online help to assist him with data input and control of processing.
  • Such a module could also, for example, allow a user to choose output parameters for display and printing functionalities.
  • an output module which can, for example, generate displays in various formats, send output results to a printer or other peripheral device, and/or export files and data to one or more destinations.
  • a top-down asset analysis can first be performed.
  • a top-down asset analysis is depicted, for example, in Figs. 6-9.
  • Fig. 6, for example is directed to an exemplary economic sector entitled "Transportation.”
  • such a sector can be considered as a top level or Level 1 asset.
  • Proceeding in a top-down approach, such a transportation sector can be divided, for example, into Level 2 assets such as, for example, Waterway, Freight, Rail, Metro, Air, and any others that may be relevant.
  • such a Rail sub-sector (a Level 2 asset) can be further subdivided into, for example, Amtrak railways and CSX Railways.
  • each of Amtrak and CSX (which are Level 3 assets) can be further divided into Level 4 assets.
  • Amtrak can be divided into a N.E. Corridor line and a CA Coastal line
  • CSX Railways can be divided into Ch-Norfolk and All Others.
  • the N.E. Corridor line Level 4 asset can be further divided into two component stations, the 30 th Street Station and the Trenton Station.
  • threat probability and vulnerability data presented, for example, in the 12x6 threat probability and vulnerability matrix structures described below, can be entered for the various components of each of the 30 th Street Station and Trenton Station.
  • a small subset of the components of the 30 th Street Station can be identified as Ticketing and Structure.
  • the component Ticketing includes all aspects of the Ticketing system used at the 30 th Street Station, most of which is primarily computer based and involves electronic communications.
  • a Structure component of the 30 th Street Station can also be identified. Structure includes tangible objects, such as buildings, people, tracks, platforms, etc., that can be found in the physical world of 30 th Street Station. A similar division can be made for the Trenton Station.
  • the 30 th Street Station and the Trenton Station are termed Special Interest Groups, or SIGs.
  • a SIG is the smallest division of a top-level asset which has a meaningful asset value for analysis.
  • a SIG as described above, can be considered to have components, such as Ticketing and Structure, and each component can in turn have a number of elements.
  • the cyber component Ticketing can have elements such as Data, Network, Communications, Computers, Software, and others.
  • the physical component of the 30 th Street Station identified as Structure can, for example, have as elements People and Platforms.
  • SIGs can be divided into components and those components further divided into their constitutive elements as may be best appropriate in a given context.
  • the asset level which SIGs are sub-divisions of e.g., N.E. Corridor
  • entity e.g., N.E. Corridor
  • a threat probability and threat vulnerability matrix can be defined for each of the elements of each SIG.
  • Figs. 10 through 17 are exemplary threat probability matrices for each of the elements of the SIG components Ticketing and Structure.
  • Figs. 18 through 25 contain exemplary raw vulnerability data for each of the five elements in Ticketing as well as each of the three elements in Structure.
  • each SIG the raw threat and vulnerability data for each of the elements in each of the components of each SIG can be processed.
  • the output of such processing is an overall vulnerability matrix at the SIG level, sometimes referred to as a "SIG Matrix," an associated Figure of Merit ("FOM"), and a calculation of Net Risk.
  • a Figure of Merit is a measure of risk, and is used in exemplary embodiments of the present invention to quantify the risk, or more precisely, the fraction of the asset value is vulnerable.
  • the Net Risk of an asset such as, for example, a SIG, can be calculated. The calculation and significance of an FOM and Net Risk are explained in more detail below, with reference to Figs. 3 and 4.
  • Fig. 46 depicts an exemplary 3-D display of the 30 th Street Station SIG Matrix according to an exemplary embodiment of the present invention.
  • other displays can be used, such as, for example, 2-D contour maps, bar graphs, or other methods of displaying functions of two variables as are known.
  • a 2-D display can also be formed by collapsing the vertical axis and "looking" from the top down at right angles to the plane of the data points that build the 3-D display. An example of this is shown in Figure 65, which is a 2-D projection of the data presented in 3-D in Fig. 49.
  • N. E. Corridor is thus an asset of one level higher than each of the 30 th Street Station and Trenton SIGs.
  • an asset being immediately above the SIG level, can be referred to as an "enterprise” asset.
  • a threat/vulnerability matrix can also be defined at the enterprise level.
  • Such a matrix can be known, for example, as a "Gradient matrix", inasmuch as its output, a function of two variables, can mathematically be considered as a mapping from an input surface to an output surface, or as a 3D surface.
  • a Gradient matrix is thus not an independent entity, but rather, a function of all of the threat and vulnerability data for each of the elements in each of the SIGs which comprise the enterprise.
  • the Gradient matrix for N.E. Corridor can be found by combining, in a manner to be described more fully below, the SIG level threat and vulnerability matrices for each of the 30 th Street Station and Trenton Station SIGs (which comprise the N.E. Corridor).
  • an enterprise level Gradient matrix can be created, and an associated enterprise level compounded FOM can be calculated.
  • a Net Risk Value can be calculated for each SIG.
  • the objective of a risk analysis is to minimize net risk
  • the objective can be to maximize it, as is the case with an opponent's or enemy's asset such as depicted in Fig. 9.
  • these Net Risk values can further be summed to generate a total Net Risk Value for each enterprise asset.
  • Net Risk Value can be calculated for each asset up the asset analysis tree. Net Risk Value is a measure of retained risk in an asset.
  • Net Risk Values for each of the 30 th Street Station and Trenton SIGs Their sum is the Net Risk Value shown for the N.E. Corridor enterprise.
  • Net Risk Value can be calculated by summing the Net Risk Values for the Level N + 1 assets which comprise it.
  • a risk remediation analysis can optionally be performed until a diminishing returns point is reached. This analysis shall be described more fully below and is depicted for the 30 th Street Station SIG in Fig. 60.
  • Fig. 2 is a process flow diagram for the Top-Down Asset Analysis referred to at 110 in Fig. 1.
  • Figs. 6 through 9 depict examples of the output of such a top-down asset analysis for different asset sectors.
  • the exemplary asset analysis depicted in Fig. 6. will be referred to throughout the following descriptions of process flow according to exemplary embodiments of the present invention.
  • Fig. 2 depicts an exemplary process flow for resolving an asset into its constituent parts.
  • a number L of sub-asset levels in a top-level asset to be analyzed can be identified.
  • the top-level asset is thus "Transportation.” As shown, it can be divided into three sub-sectors. The first such sub-sector is “Rail.” Rail itself can be divided into two “Level 3" assets, namely "Amtrak,” and "CSX.” Each sub-sector can be termed a "Level 2" asset.
  • Amtrak can be divided into a number of "Level 4" assets, namely "N.E. Corridor,” “Others”, and “CA Coastal.”
  • CSX the other Level 3 asset constituent of Level 2 asset “Rail”
  • Level 4 assets “Ch-Norfolk” and "All Others.”
  • the Level 4 assets are enterprise level assets.
  • an enterprise level asset is an asset at the L th level, which is subdivided into SIGs.
  • SIG is the smallest division of a top level asset which has a meaningful asset value for analysis. Focusing on the N.E. Corridor enterprise, at 210 N.E. Corridor can be divided into SIGs.
  • the SIGs that comprise the N.E. Corridor can be, for example, 30 th Street Station and Trenton Station.
  • each of the assets can be assigned an asset value known as the "At-Risk Valuation.”
  • Asset Value known as the "At-Risk Valuation.”
  • Net Risk Value can be obtained via the calculations described below, the process flow of which is illustrated in Figs. 3 through 5.
  • asset values and asset weight factors can be assigned at each asset level down to the SIG level.
  • asset values in dollars assigned at each of those levels.
  • an Asset Weight Factor is the asset value of a particular asset, sub-asset or SIG, divided by all of the other assets, sub-assets or SIGs at that asset level. For example, at the SIG level in Fig. 6 the two SIGs shown are the 30 th Street Station and Trenton Station. The 30 th Street Station SIG has an asset value of $2.5 billion and the Trenton Station SIG has an asset value of $500 million.
  • each SIG can be divided into components and weighting factors for each component can be assigned. This is illustrated, for example, in Figs. 18 through 25, for the 30 th Street Station SIG. In this example, this SIG, can be divided into two components, "Ticketing" and "Structure” and each component can be further divided into elements. These weighting factors take into account the fact that in many contexts the contribution of components within SIGs, or elements within components, to the overall risk is not equal.
  • Ticketing is assigned a weighting of 0.8 and Structure is assigned a weighting of 1.0.
  • each of the elements comprising Ticketing and Structure can also respectively each be assigned a weighting factor.
  • multiple sets of asset values and weighting factors can be stored in an exemplary system, and a user can run various risk analyses for the same input data, allowing such a user to obtain a multi-dimensional view of the overall risk situation.
  • the conceptual plane for threats can be divided into twelve categories. These categories comprise six cyber categories and six physical
  • the twelve categories are believed to comprehensively describe the various possible types of threats which any asset, or subdivision thereof, faces. Moreover, these categories describe such threats in an orthogonal or independent way where no category depends upon, or is significantly correlated with, any other category.
  • a threat taxonomy By using such an exemplary threat taxonomy, a risk analyst is guided to focus in on the various separate threats which any asset faces.
  • the following table contains such an exemplary threat taxonomy.
  • Cyberhacker Classical: non-malicious hacker actions; mischief; professional, serious, occasional, amateur, lucky, etc.; operates remotely on a network, does not require proximity; intercepts un-encoded transmission for recording; causes copy to be issued or sent electronically, reverse engineering
  • PHT Physical Cracker/(Hacker) Lock picker, safe cracker, penetrator, impostor: just to prove it (PHT) can be done or for collateral reason, Peeping torn, voyeur, paparazzi, browsing real physical asset elements in containers, dumpster diving, exploiting "loose lips", curiosity or mischief; pilfering, reverse engineering
  • Cyber Terrorist Destruction, subversion, denial of service with malicious intent against cyber elements of an asset; hired agent, angry or motivated current or former employee; directed EMP, co- opted/pressured employee; intercepts un-encoded transmission for recording, manipulation or pilfering Physical Terrorist (PTT) Destruction, vandal, penetration, subversion, sabotage against physical elements of an asset; hired agent, angry or motivated current or former employee; co-opted/pressured employee; exploiting "loose lips"
  • CST Physical Spy (CST) Classical; professional, serious, or amateur, etc. engaged in surveillance ("loose lips”), reconnaissance, trespassing, planting devices, dumpster diving, reverse engineering, tampering/subversion of security protections; hired agent, angry or motivated current or former employee; co-opted/pressured employee; reverse engineering
  • Cyber criminal Theft of information, uploads or damaging code, intentional and malicious cyber activities, hired agent, angry or motivated current or former employee; co-opted/pressured employee, dollar or commodity driver, expert skills available
  • PCT Physical Criminal
  • Cyber Environmental Electrostatic shock to cyber electronics, collateral EMP, ingress of RF into cyber based processes like SCADA or sensor monitoring (as from radio/TV stations, microwave links, medical equipment, cordless phones, call phone and WiFi PDA), equipment failure
  • a user can, for example, be prompted at a threat grid entry screen to enter the probability of each of the twelve identified types of threats as to each of six identified independent types of vulnerabilities.
  • Such vulnerabilities are also designed to be orthogonal as well as comprehensive.
  • vulnerabilities can be viewed from independent or orthogonal perspectives, in various vulnerability categories.
  • Each such category is intended to be an intrinsic property of an asset, which is independent of its other properties. Avoiding overlap makes a risk analysis more clear and the remedies more evident as to purpose.
  • the following vulnerability categories have been adapted for a much broader range of threat and vulnerability possibilities that can put an asset at risk. If, given changing technologies, economies and political systems, at some point new threat attributes arise that are truly independent from the existing set, new vulnerability value panels can be created. Thus, the taxonomy is scalable.
  • vulnerability classes are seen as intrinsic properties of an asset itself. Exemplary specific definitions that can be used for each vulnerability category are as follows:
  • the above provided threat and vulnerability definition tables provide an analyst or other user with a perspective that can be used for entering data in each cell in respective Threat and Vulnerability grids.
  • Threat values can be normally provided by outside sources.
  • Vulnerability values can either be provided by analysts using templates provided by an exemplary application according to the present invention or from external sources. Such analysts can, for example, utilize independent measures, such as, for example, actuarial and handbook failure data. Such analysts can, for example, glean vulnerabilities for cyber as well as physical assets form industry sources.
  • a given software program can, for example, allow a user to immediately access the definition and some common examples of the various types of threats and vulnerabilities by, for example, clicking on the main threat or vulnerability.
  • a given system could allow a user to press a button or right-click on a mouse within a particular cell in a threat vulnerability matrix and thereby bring up the definitions of the relevant threat and the relevant intersecting vulnerability as well as seek common examples of that type of threat and that type of vulnerability so as to be better able to enter threat and vulnerability data.
  • each "alternate term set" row depicts a conventional alternate term set for categorizing the vulnerability conceptual plane.
  • the cells with asterisks are typical groupings of conventional terms that either do not cover the full range of vulnerabilities or are not independent of each other.
  • Figs. 10 through 17 depict exemplary threat probability matrices for each of the exemplary twelve threat types and six vulnerability types described above.
  • threat probabilities can have any value from 0 to 1 which correlates to a percentage of likely occurrence between 0% and 100%.
  • the range between 0 and 1 could be expressed using three or more decimal places, in which case the range would not be construed as percentages but rather as a probability scale corresponding to the number of decimal places used.
  • threats could be expressed as having a probability of X in a thousand (using three decimal places), Y in a million (using six decimal places), etc., depending upon the number of decimal places allowed in a given embodiment for the entry of threat probability data.
  • FIG. 10 depicts element level Threat Probability Matrices, where a separate matrix is input for each of the respective eight elements in the two components of the 30 th Street Station.
  • Figs. 10 through 17 depict element level Threat Probability Matrices, where a separate matrix is input for each of the respective eight elements in the two components of the 30 th Street Station.
  • the 30 th Street Station SIG can be divided into two components, "Ticketing” and “Structure.”
  • the component “Ticketing” can be further divided into five elements, namely "Data,” “Network,” “Comms,” “Computers,” and “Software,” and the component “Structure” can be further divided into three elements, namely "Building,” “People,” and “Platforms.”
  • This schema will be used in the following description to illustrate the entry ⁇ and processing of data in exemplary embodiments of the present invention. IV. SIG LEVEL PROCESSING
  • a user can input threat and vulnerability source data for each element of each SIG component.
  • Figs. 10 through 17 are exemplary Threat Probability Matrices
  • Figs. 18 through 25 are exemplary Vulnerability Matrices for each of the elements comprising the 30 th Street Station SIG.
  • the cell values in the matrices of Figs. 18 through 25, as is the case with entries in any vulnerability matrix in to exemplary embodiments of the present invention, are on a scale of 1 through 15. These numbers are actually negative exponents.
  • this scale can be chosen, in exemplary embodiments of the present invention, to represent the likelihood or probability of occurrence of a given threat against a particular vulnerability.
  • Vulnerability Matrix corresponds to the odds of occurrence expressed as 1 in 10 (Ce " Value) . Accordingly, a cell value of 1 in such a matrix means that there is a likelihood of 1 in 10 that that particular vulnerability could occur in the presence of a relevant threat, 2 nd a value of 15 is interpreted to mean that the likelihood is 1 in 10 15 that such a vulnerability could occur. Obviously, because these numbers are actually negative exponents, the higher the number the better protection there is for that vulnerability; i.e., the lower the risk that a particular threat/vulnerability combination poses to the asset in question.
  • this data can then, for example, begin to be processed according to the methods of the present invention.
  • the risk probability can be decomposed into two key components: threat probability and vulnerability probability.
  • these probabilities can be combined into a compounded value, for ease of calculation.
  • a Compounded Source Cell Value can be calculated for each element, as shown in Figs. 18 through 25. This involves combining the data in Figs. 10 through 17 with that of Figs. 18 through 25, respectively, according to the following rule:
  • CSCV Compounded Source Cell Value
  • the vulnerability values are logs (exponents) and the threat probability values are non-exponent (i.e., value from 0 to 1), it is necessary to form a resultant exponent.
  • a threat probability matrix value can be converted to a logarithm and added to, the associated vulnerability matrix cell value. Because the compounded threat-vulnerability cell values are thus actually negative exponents, they get more negative to represent a smaller number. The unsigned value then increases as that cell's value is made less contributing. It is assumed that the threat probabilities can have different values for each type of vulnerability and threat type, i.e., for each cell in the threat probability matrix.
  • the Cell Value for the Compounded Threat- Vulnerability Matrix is the Cell Value of the Vulnerability Matrix, in this case 6, added to LOG [1 /Threat Probability Cell Value].
  • the Threat Input Cell Value 1
  • the Threat Probability Matrices i.e., Figs. 10 through 17, were defined such that there is 100% probability of occurrence of each of the relevant threats against each of the identified vulnerabilities except in the case of cyber threats against people and physical structures wherein the example shows a nil threat. Because in such a case the formula would become indeterminate the exemplary application has a Boolean logic override for the entry of a "0" which makes the corresponding cell value N/A (not applicable).
  • Compounded Threat- Vulnerability Matrices will be identical to the Threat Vulnerability Matrices as is shown in Figs. 18 through 22.
  • the application uses a Boolean override to set the value to N/ A to avoid reducing the equation at 310 to an indeterminate expression.
  • the Compounded Threat- Vulnerability Matrix Data for each element can, in exemplary embodiments of the present invention, be transformed to Weighted Value Matrices at 320.
  • Weighted Value Matrices implicate the importance weighting factors assigned at the elemental and componental Levels as is illustrated in Fig. 2, at 230 and 240, and in Figs. 6-9. Accordingly, with reference to Figs. 18 through 25, as between the two components of 30 th Street Station Ticketing and Structure, Ticketing has a component weighting of 0.8 and Structure has a component weighting of 1.0. Similarly, within the Ticketing component at the elemental level, the following elements of component Ticketing have the following weightings: Data 1.0, Network 0.9, Comms 0.9, Computers 0.8 and Software 0.6. Within component Structure, the following elements have the following weightings: Building 0.8, People 1.0, and Platforms 0.7. In exemplary embodiments of the present invention the weightings are user-assigned relative measures of importance or contribution and thus there is no requirement that they sum to unity or any other fixed number.
  • each of the elemental and componental levels can be, for example, used to transform the Compounded Threat- Vulnerability Matrix Data at 310 Weighted Value Matrix Data at 320. This is illustrated at 320 all with respect to Fig. 3.
  • each of the Compounded Threat- Vulnerability Matrices such as are shown, for example, in Figs. 18 through 25, can be, for example, processed using the following equation:
  • WCV Weighted Cell Value
  • Compunded Threat- Vulnerability Matrices of Figs. 18 through 25 can be transformed to the Weighted Value Matrices of Figs. 26 through 33, respectively.
  • the Weighted Value Matrices can, for example, be transformed to create Build Coefficient Matrices.
  • Exemplary Build Coefficient Matrices can be generated by processing the Weighted Value Matrices of Figs. 26 through 33 to yield those shown, for example, in Figs. 34 through 41, respectively.
  • the relationship between a Weighted Value Matrix for a given element of a given component of a given SIG, and the corresponding Build Coefficient Matrix ("BCM”) for that element can be expressed via the equation
  • Build Coefficient Cell Value 1- 10 ( WCV) as shown at 330.
  • the reason for this transformation is that Build Coefficients are patterned after reliability factors.
  • a failure rate is one part in one thousand (.001) then a corresponding reliability factor is (1 minus the failure rate) or 0.999, for example.
  • a Weighted Value Matrix cell value is essentially a negative exponent of 10, illustrating or expressing a vulnerability to a given threat. Therefore, a higher number corresponds to a more negative power of 10 and thus a larger denominator and an overall smaller vulnerability number.
  • the various Build Coefficient Matrices for each component can be combined to generate a Component Level Build Coefficient Matrix by cellwise multiplication.
  • Figs. 34, 35, 36, 37, and 38 can be combined via cellwise multiplication to generate a component level Build Coefficient Matrix as shown in Fig. 42 for the component "Ticketing" of the 30 th Street Station SIG.
  • Figs. 39, 40, and 41 can be combined via cellwise multiplication to generate a component-level Build Coefficient Matrix as shown in Fig. 43 for the component "Structure”.
  • Fig. 43 illustrates an exemplary Compounded Structure Component Build Coefficient Matrix, generated by cellwise multiplication across the matrices shown in Figs.
  • Fig. 44 illustrates an exemplary SIG Level Build Coefficient Matrix which perpetuates the values for cyber threats of Fig. 42, the Compounded Ticketing Component Build Coefficient Matrix, unchanged.
  • a SIG Level Build Coefficient Matrix can be generated by cellwise multiplication across the various Compounded Component Build Coefficient Matrices. In the illustrative example this entails cellwise multiplication of Figs. 42 and 43. However, as noted above, any cell which has no value is assigned a value of 1.
  • Fig. 42 which are those of the Compounded Ticketing Component Build Coefficient Matrix.
  • Fig. 44 is thus the product of cellwise multiplying the matrices of Figs. 42 and 43, and is thus a SIG Level Build Coefficient Matrix incorporating the data from both the Ticketing and Structure components of the 30 th Street Station SIG.
  • a SIG Level Build Coefficient Matrix for example, can be transformed to a SIG Matrix, i.e., a Gradient matrix for the SIG.
  • a SIG Matrix i.e., a Gradient matrix for the SIG.
  • each cell in the SIG Level Build Coefficient Matrix is transformed using the formula:
  • This mathematical transformation takes a build coefficient reliability factor type cell value and transforms it back to a number which represents a negative exponent of 10 to express the opposite of vulnerability or "index of protection", where the lesser vulnerability the higher the number.
  • the SIG Matrix value is of the same type as was the original Vulnerability Matrices and the Compounded Threat- Vulnerability Matrices. These numbers, therefore, run on a nominal scale of 1 through 16 (or, where the threat probabilities are all 100%, on a nominal scale of 1 to 15).
  • An exemplary SIG Matrix generated from the SIG Level Build Coefficients Matrix of Fig. 44 is depicted in Fig. 45.
  • Fig. 45 is thus a mathematically created matrix representing the overall vulnerability of the 30 th Street Station SIG.
  • a mathematical quantity known as a "Figure of Merit” or "FOM” can be calculated from the SIG Level Build Coefficients Matrix.
  • An FOM measures the total reliability or "invulnerability" of a SIG.
  • a FOM can be generated, for example, by multiplying all of the cells in the SLBC Matrix together, and then converting this product to an exponent (of 10) to indicate what an equivalent hybridized overall SIG Value is.
  • An FOM is somewhat analogous to a GPA score for academic performance but without the effect of high scores averaging out failures or very low scores. The non-linear FOM representation does not allow very low scores or "failures" to be hidden from view or computation.
  • an FOM can be calculated at 360 according to the formula
  • Fig. 46 is a color-coded three-dimensional depiction of the SIG Matrix Values of Fig. 45.
  • Fig. 46 also presents the FOM for this SIG, computed as described above.
  • Fig. 47 once again presents the SIG Matrix Values of Fig. 45 and additionally illustrates the Net Risk Figure for the 30 th Street Station SIG.
  • the Net Risk is a quality 10 "(S1G F0M) can be generated called the "Risk Multiplier", using the equation: n ⁇ ⁇ %•?ri%> ⁇ U V VoIlln VI ⁇ W * D I %i ⁇ o WlIXr I IWVIIi Vl il It Vll
  • a SIG Net Risk can be calculated. Accordingly, using a SIG FOM of 0.128280, a Risk Multiplier can be generated by 1(T 0 128280 , which yields 0.744252, or 74.425%.
  • a Risk Multiplier is a function of an FOM representing the fraction of the valuation of the SIG (or other asset) which is at risk given that FOM.
  • 74.425% of the total Asset Value is at risk given the current threat vulnerability data encoded in the FOM, yielding $1,860,631,568 as the net risk associated with the 30 th Street Station. It is noted that this is a very large at-risk value.
  • the 30 th Street Station SIG is a prime candidate for an optional risk remediation process, as described below with reference to Fig. 4.
  • the FOM for the SIG the SIG Matrix and the SIG Net Risk can be output "upwards" to be used in calculations at the enterprise level.
  • sub-assets at the L th asset level are divided into SIGs, and thus illustrated the N.E. Corridor, a sub-asset at the L th (4 th ) asset level, was divided into the two SIGs of the 30 th Street Station and Trenton Street Station.
  • RVFs Real Value Factors
  • STG Matrix values have been converted to real numbers in the form of RVFs, they can be linearly combined to create an Enterprise Level Gradient Matrix.
  • Such combination in exemplary embodiments of the present invention, can be linear and can, for example, be weighted by the relative value of each SIG (whether in dollar terms, quality of life terms, lives at risk, and any other convenient metric as may be useful) within the enterprise.
  • This is illustrated for the exemplary N.E. Corridor example in Figs. 47 and 48.
  • Fig. 47 shows that the at-risk valuation of the 30 th Street Station SIG is $2.5 billion.
  • the quotient defined by the at-risk valuation of a SIG divided by the total at-risk valuation of all SIGs in a given enterprise can, in exemplary embodiments of the present invention, be termed the "Risk Multiplier.” It is the Risk Multiplier that can be used to transform RVFs derived from SIG Matrices into proportional RVFs ("PRVFs") which are weighted RVFs.
  • PRVFs proportional RVFs
  • the Risk Multiplier can then be multiplied by each element in a RVF Matrix to generate a proportional RVF ("PRVF") Matrix for each SIG as shown at 420 using the formula:
  • Fig. 50 depicts an RVF matrix for the 30 th Street Station SIG
  • Fig. 51 depicts its corresponding PRVF matrix.
  • Fig. 51 was generated by multiplying each element of Fig. 50 by a RM of 83.33% (see Fig. 6)
  • Fig. 52 depicts an exemplary RVF matrix for the Trenton Station SIG
  • Fig. 53 its corresponding PRVF matrix; generated by multiplying the RVF matrix of Fig. 52 by an RM of 16.67%.
  • various proportional RVF Matrices can be combined by cellwise addition to generate a consolidated RVF Matrix, or CRFV for the enterprise. This can be done, for example, using the following formula:
  • FIG. 54 A CRVF Matrix for the exemplary N. E. Corridor enterprise is depicted in Fig. 54, and an associated ELGM is depicted in Fig. 58.
  • Fig. 59 is a multi-colored 3D graphic display of the ELGM of Fig. 58.
  • the 3D surface of Fig. 58 can be seen as a complex weighted combination of the 3D surfaces of Figs. 46 and 49. Due to the more pronounced contribution of the 30 th Street Station SIG relative to that of the Trenton Station SIG, the surface depicted in Fig. 59 appears to be more similar to the 30 th display of the 30 th Street Station SIG Matrix depicted in Fig. 46.
  • Gradient matrices at the enterprise or any higher level can also be displayed as a 2D contour map or by using any other technique to display functions of two variables as is known.
  • an Enterprise Level Build Coefficient Matrix can be generated, using the formula:
  • ELBCMjJ [1 -10 (ELGMij) ].
  • Fig. 57 depicts such an ELBCM for the exemplary N.E. Corridor enterprise.
  • ⁇ s is described above in connection with SIG level processing, a build coefficient matrix can be used to generate an FOM.
  • an enterprise level compounded FOM (“ELCFOM”) can be calculated from an ELBCM using the formula
  • Fig. 59 depicts an FOM for the exemplary N.E. Corridor enterprise and a 3D multicolored graphic display of an associated ELGM. It is noted that the FOM value of 0.173 is dangerously low, which indicates that iterative risk remediation may be a useful option, as described below.
  • the Net Risk for the enterprise can be calculated by simply summing the individual Net Risk values for each SIG in the enterprise which were output to the enterprise processing at 380, of Fig. 3.
  • the Net Risk N E c omdor Net Risk 3 o th
  • output data can be insulated from input source data so that objective numerical results can be compounded without revealing the risks of individual contributors in non-federated analyses. This can be desirable where, for example, a given asset or sector has one or more lower level sub-assets that are sovereign entities.
  • a user can optionally proceed to an iterative FOM improvement process, at 470, if the FOM is low or if it is obvious that there are certain values in the ELGM that are unacceptably low, relative to a defined value or values.
  • the ELGM value of 0.79 in the cell at the intersection of threat "Physical Terrorist” and vulnerability “Discernable” is unreasonably low.
  • the intersection of threat “Physical Environmental” and vulnerability “Accessible” of 0.53 is also unreasonably low.
  • any vulnerability score less than 2 (which correlates to a probability of occurrence greater than or equal to 1 in 100) is unacceptably low in most contexts.
  • a cell value less than 1 correlates to a probability of occurrence greater than 1 in 10, which is almost always unacceptable. If these values can be raised then perhaps the overall FOM can be raised to a more acceptable number, resulting in a lesser portion of the value of the enterprise being at risk.
  • Such an iterative FOM improvement process is illustrated in Fig. 5, and an example of such process illustrated in Fig. 64.
  • the target for remediation is any cell value below an FOM value + 1 that would produce acceptable risks.
  • an FOM value of 4 might be an acceptable risk value, so any cell with a value less than 5 could be investigated.
  • the reason for such an increase in target value for cells is because cell values are multiplied together, a product will always be lower than any individual cell value.
  • an Enterprise Level Gradient Matrix is composed of derived elements, the only way to raise an overall enterprise level FOM enterprise level is to raise the corresponding vulnerability values at the elemental level. This can be done, for example, by going back to the element level vulnerability matrices contributing to the low vulnerability value and measuring the costs of remediating those risks with low values so as to increase their values compared with the associated reduction in the proportion of the asset that is at risk as a result of the remediation.
  • remediation cost In general, as long as the remediation costs are less than the change in Net Risk Value by an acceptable ROI factor, it is worthwhile to pay that remediation cost, recalculate the Net Risk Value and the FOM, and inquire as to whether further remediation, given its cost(s), would continue to decrease the Net Risk Value of that asset by a still greater amount than the associated remediation cost. This process will next be described in detail with reference to Figs. 5 and 64.
  • the vulnerabilities in the element-level matrices that have values below the initial FOM can be selected, net risk values are collected, and remediation costs to bring those cells to a value of at least one higher than the initial FOM can be calculated.
  • Gradient calculations at the relevant SIG levels can be rerun and the reduction in retained risk values observed.
  • cost metrics can be associated with assets in exemplary embodiments of the present invention. Therefore, although for the illustrated exemplary N. E. Corridor enterprise, dollars have been used in alternative exemplary embodiment, lives at risk, or quality of life impact, as well as a variety of other cost metrics or valuation metrics associated with an asset can be used.
  • a "strategic value” may not directly correlate with dollar values but may represent the value of such an asset to an enemy in operating militarily.
  • Numerous other exemplary asset valuation metrics can be used as may be appropriate or desirable in various contexts.
  • the return on investment or ROI can be calculated using the following formula:
  • ROI Reduction in L th Level Net Risk Value / ⁇ SIG remediation costs up to the L th Level
  • processes 501, 502, and 503 can be repeated until the ROI fails to meet investment/remediation-yield criteria.
  • investment/remediation-yield criteria can be set by a user or by a special risk analyst.
  • Such an analyst could be, for example, either the same analyst that performed an asset analysis process and generated the data input with the threat and vulnerability matrices, or, for example, a different type of asset analyst sometimes known in the art as a "risk governor".
  • Fig. 5 depicts process flow for an exemplary iterative risk remediation process at the enterprise level according to an exemplary embodiment of the present invention. To illustrate such a process with actual numbers would be rather complicated, inasmuch as each element of each component of each SIG would need to be analyzed for ways to improve (i.e., increase the values of) its respective vulnerability matrix entries and the costs of such remediation calculated. Each such iteration could result in a change to the FOMs for each SIG and a resultant recalculation of the FOM for the enterprise as described in connection with Fig. 4.
  • the source of a low enterprise level FOM is a SIG with a large Asset Weight Factor that has a significantly lower FOM than the other SIGs in the enterprise.
  • the 30 th Street Station SIG fits just such a profile. It has an Asset Weighting Factor of 72.289%, and it thus contributes significantly to the overall N.E. Corridor FOM, and Us FOM is an exceeding low 0.128.
  • risk remediation at the 30 th Street Station level may be sufficient to solve the problem.
  • Fig. 64 Such a process is illustrated in Fig. 64. With reference to Fig. 64, there are seven columns labeled 6401, 6403, 6405, 6407, 6409, 6411 and 6413.
  • Each column contains a different type of metric associated with a given set of threat and vulnerability matrix values.
  • the Baseline analysis row this is the starting point for the iterative risk remediation process.
  • the FOM listed in the baseline analysis row is the same FOM provided in Figs. 46 and 47 for the 30 th Street Station, namely 0.128. As noted above, this figure is very low, and therefore the 30 th Street Station SIG is a prime candidate for an iterative risk remediation process.
  • column 6405 provides a Net Risk Multiplier associated with each FOM. As the FOMs in column 6403 increase, the Net Risk Multiplier, which represents the fraction of the assets value which is at risk, will decrease. Associated with each Net Risk Multiplier, in column 6407 is a Net Risk Value, or actual dollar value at risk. The Net Risk Value is the asset value of the asset, here the 30 th Street Station, multiplied by the Net Risk Multiplier. Column 6409 provides the remediation cost in moving from each row to the next row below.
  • An ROI greater than 1 is generally beneficial, and an ROI greater than 100 represents a significant return on the invested risk remediation costs.
  • Fig. 64 the values in the remainder of the rows in Fig. 64 can be similarly generated.
  • an iterative risk remediation process can continue until a DRP, or Diminishing Return Point, is arrived at.
  • the Diminishing Return Point is achieved when the investment cost equals, or substantially equals, the prospective reduction in retained risk, or when the ROI is substantially equal to unity.
  • the remediation cost is greater than or equal to the associated decrease in net risk value, it simply does not make economic sense to further remediate the risk. This is seen in the row entitled Next Analysis-5, where the DRP has been reached.
  • the percentage of the 30 th Street Station which is at-risk has been reduced from 74.427% to only .0015%.
  • Figs. 61 through 63 trace the processing of data from input at the component level through calculation of the FOM, Net Risk Value, and Level 3 Asset Gradient Matrix (one level above the enterprise level) which can be graphically displayed as a 3-D multicolored surface (or as any other representation of a function of two variables, as noted above, such as for example a 2D contour map, a 2D "hot spot" map, etc.) for the Level 3 Asset Amtrak.
  • Fig. 60 is a end to end depiction of this process, which originates on the far right side of Fig. 60 and terminates on the far left side of Fig. 60.
  • Figs. 61 through 63 each represent approximately one-third of the process flow depicted in Fig. 60, for ease of description and illustration.
  • Fig. 61 depicts a portion of the processing flow from the inputting of data at the SIG component level to the generation of outputs at the SIG level. This is the process flow which corresponds to Fig. 4.
  • Fig. 62 depicts the process flow starting from using as inputs the enterprise level data for each of the N.E. Corridor enterprise and the CA Coastal enterprise to the generation of Proportional PRV matrices for each of these enterprises and carrying forward the Net Risk Value for each of these enterprises.
  • the process flow depicted in Fig. 63 begins with the Proportional PRV matrices generated as shown in Fig. 62 and using this data to generate an Amtrak Level 3 Asset Gradient Matrix as well as an Amtrak FOM.
  • the Level 3 asset "Amtrak” can be divided into three enterprises, namely, the N. E. Corridor, Others, and CA Coastal.
  • the Level 3 asset Amtrak will be considered to be composed of only the N.E. Corridor and CA Coastal enterprises.
  • the N.E. Corridor enterprise is composed of two SIGs: 30 th Street Station and Trenton Station.
  • Fig. 61 beginning at the right side of the figure, data from the two SIGs comprising the N.E. Corridor can be entered and processed.
  • the 30 th Street Station SIG Matrix shown in Fig.
  • the Trenton Station SIG Matrix (shown in Fig. 50) can be input and at 6107 the Trenton Station SIG Matrix (shown in Fig. 50) can be input.
  • Each of these SIG Matrices can be processed in parallel.
  • the 30 th Street Station RVF matrix can be generated, as shown in Fig. 50.
  • the Trenton Station RVF matrix can be generated, as shown in Fig. 52.
  • these two RVF Matrices can be transformed to Proportional RVF Matrices at 6103 and 6105, respectively, as shown in Figs. 51 and 53, respectively.
  • the proportional RVF matrices 6103 and 6105 can be added cellwise at 6113 to generate a N.E. Corridor Consolidated RVF Matrix, shown in Fig. 54.
  • N.E. Corridor Consolidated RVF matrix from which the N.E. Corridor enterprise level Gradient matrix is generated at 6201.
  • the creation of FOMs will next be described.
  • the 30 th Street Station SIG Matrix is input at 6101. This matrix is shown in Fig. 47. This matrix can be transformed via the formula 1 -10 ce va ue to generate a 30 l Street Station Build Coefficients matrix at 6110, as shown in Fig. 55.
  • a 30 th Street Station FOM Intermediary Product can be generated, which is the product of all cells in the 30 l Street Station Build Coefficients matrix.
  • a 30 th Street Station FOM can be generated at 6121 by the formula
  • FOM -LOG(I-FOM Intermediary Product).
  • a 30 l Street Station Net Risk Multiplier 6122 can be generated via the operation 10 "FOM . Taking the product of the Net Risk Multiplier 6122 and the 30 th Street Valuation Input 6112 generates a Net Risk Value 6123. In the case of the 30 th Street Station, this is $1,860,667,446 (See Fig. 6).
  • the Trenton Station SIG matrix can be input, shown at Fig. 48. From this matrix, using the formula
  • a Trenton Station Build Coefficients matrix can be generated at 6114, shown at Fig. 56. From there, by taking product of all cells in that matrix, a Trenton Station FOM Intermediary Product can be generated at 6115 which can be transformed to an FOM at 6125 using the formula
  • FOM -LOG (1 -Trenton Station FOM Intermediary Product).
  • the Trenton Station FOM can then be transformed to a Net Risk Multiplier at 6126 using the formula 10 " and the Net Risk Multiplier 6126 multiplied by the Trenton Station Valuation Input 6116 can thus yield an exemplary Trenton Station Net Risk Value 6127 of $5,674,000 (see Fig. 6).
  • Fig. 62 deals primarily with processing at the N.E. Corridor enterprise level.
  • the N.E. Corridor Enterprise Level Gradient Matrix shown in Fig. 58, can be carried over from Fig. 61.
  • this ELGM can be transformed to a N.E. Corridor Consolidated Build Matrix, as shown in Fig. 57.
  • an N.E. Corridor FOM Intermediary Product can be generated, by multiplying all of the cells in the N.E. Corridor Consolidated Build Matrix (Fig. 57) together, and at 6211 a N.E. Corridor FOM can be formed from the FOM Intermediary Product 6203 using the equation
  • FOM -LOG (1 - N.E. Corridor FOM Intermediary Product).
  • the N.E. Corridor ELGM can be graphically displayed, as shown in Fig. 59, at 6210. Adding together the Net Risk Values from the 30 th Street Station (6123, Fig. 61) and the Trenton Station (6127, Fig. 61), at 6212 a Net Risk Value for the N.E. Corridor enterprise can be generated. This can be input to the next level s the Level 3 Asset Amtrak, as described below. Finally, 6230 represents all of the data relative to the CA Coastal Enterprise that was carried through from lower level computations. The processing required to generate that data is not shown, it being assumed that there is enterprise level data for the CA Coastal enterprise which was generated in an analogous manner as shown for the N.E. Corridor enterprise.
  • CA Coastal Asset Waiting Factors can be input to 6224, a CA Coastal ELGM can be input to 6225, and a CA Coastal Net Risk Value can be input to 6331, on Fig. 63.
  • N.E. Corridor Enterprise data can be input to 6225
  • CA Coastal Net Risk Value can be input to 6331, on Fig. 63.
  • the N.E. Corridor Enterprise data can be processed into a N.E. Corridor Real Value Factor Matrix at 6221. This matrix can in turn be transformed to a N.E. Corridor Proportional Real Value Factor Matrix at 6222.
  • the CA Coastal ELGM can be processed into a CA Coastal Real Value Factor Matrix 6224, and that matrix can, in turn, be processed into a CA Coastal Proportional Real Value Factor Matrix at 6223.
  • the two proportional Real Value Factor matrices at 6222 and 6223 can be respectively combined into an Amtrak Consolidated Real Value Factor Matrix at 6301, in Fig. 63.
  • This matrix can, for example, be transformed into an Amtrak Level 3 Asset Gradient Matrix at 6310 using the equation
  • Intermediary Product can be generated at 6312 by multiplying all elements of this matrix together.
  • an Amtrak FOM can be generated from such an Amtrak FOM Intermediary Product 6312 using the equation:
  • Amtrak FOM -LOG(I - Amtrak FOM Intermediary Product)
  • Net Risk Values for any net risk above the SIG levels can be calculated by linearly adding the relevant lower level Net Risk Values.
  • Net Risk Values follow a different data flow, or data path, than do the Gradient matrix and FOM calculations.
  • the N.E. Corridor Net Risk Value from 6212 in Fig. 62 can be input to Fig. 63 at 6330 and the CA Coastal Net Risk Value from 6230 in Fig. 62 can be input to Fig. 63 at 6331.
  • These two values can, for example, then be summed to generate an Amtrak Net Risk Value at 6340.
  • an Amtrak Level 3 Asset Gradient Matrix 6310 which can be displayed in the 3-D multicolored graphic 6320
  • an Amtrak FOM 6321 which is a measure of the risk vulnerability of the entire Amtrak system
  • an Amtrak Net Risk Value 6340 (sometimes referred to as retained risk).
  • risk assessment is understood in the context of having one or more assets to protect that have intrinsic vulnerabilities and potential threats.
  • the objective is to lower the vulnerabilities in the face of potential threats to lower overall risk.
  • an asset belongs to an enemy or aggressor.
  • a user knows or strongly suspects its vulnerabilities and has no intention of lowering them. In fact he may seek to maximize them.
  • exemplary embodiments of the present invention can be used to analyze the effectiveness of the threats that may be generated.
  • the data input is all the same, as described above, the data processing is the same, but the activity level to determine the input data to the threat matrices is very high and the vulnerability matrix activity level is significantly reduced.
  • an "improved" FOM is a smaller FOM, indicating a higher risk.
  • ROI calculations can, for example, follow the same process, but the intention on the input of the data is vastly different.
  • ROI can be measured as the amount of risk AUGMENTATION divided by the costs of implementing a LOWER FOM.
  • Threat values can be as granular as desired since the methods of the present invention are indifferent to granularity.
  • retained risk values can be maximized as opposed to minimized in the more conventional risk management applications as illustrated above.
  • the methods of the present invention could also be applied to analyze the "risk" - in political terms, using some meaningful metric - that an opposing candidate faces in a political campaign comprising multiple candidates.
  • vulnerabilities the weaknesses (education, record, finances, hidden information, dirt, cosmetics, speaking ability, knowledge, prior experience, etc.) expressed in likelihood of looking bad, happening or being revealed in the campaign or relative to the candidate, and for threats, the strengths (threats) of the opposition (debates, misrepresentations, dirty tricks, cosmetics, outspending, media, etc.) expressed in probability or likelihood of implementation in countering those vulnerabilities.
  • Such an analysis can be done across several boundaries (assets) geographic or demographic, as well as considering the strengths and weaknesses of multiple candidates.
  • the "what if or iterative risk remediation analysis can be used against different strategies to foil the effectiveness of the competition and/or raise the potential voting point separation between candidates (i.e., lower the retained risk).

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Abstract

L'invention concerne des systèmes et procédés pour identifier, catégoriser, quantifier et évaluer des risques. Dans des modes de réalisation exemplaires de la présente invention, un actif peut être analysé en différents niveaux de sous-actifs d'une manière descendante. Ensuite, les sous-actifs des niveaux les plus bas peuvent être analysés en composants et éléments de ces composants. Dans des modes de réalisation exemplaires de la présente invention, des données de probabilité et de vulnérabilité de menaces orthogonales et complètes peuvent être entrées pour chacun des éléments de chaque composant de chaque sous-actif de niveau le plus bas. Dans des modes de réalisation exemplaires de la présente invention, ces données peuvent être entrées sous la forme d'une matrice de probabilité et d'une matrice de vulnérabilité de menaces. Les données d'entrée peuvent être alors traitées pour générer un ensemble de sortie pour chaque sous-actif comprenant une matrice combinée de menaces/vulnérabilité, un index de la vulnérabilité de risque globale, ou un « facteur de mérite » (FOM) et le risque retenu associé. Pour chaque composant et niveau de sous-actifs, un tel ensemble de sortie peut être alors traité en ensembles de sortie combinés pour les actifs de niveau plus élevé dont ils font partie, en remontant l'arbre d'analyse d'actifs. Ceci permet d'offrir un calcul de risque précis pour l'actif de niveau supérieur et chaque niveau de sous-actif identifié dans l'analyse descendante. Dans des modes de réalisation exemplaires de la présente invention, ces sorties peuvent être affichées dans différents modes d'affichage, et un processus de conversion de risque itératif optionnel peut être alors réalisé. Dans des modes de réalisation exemplaires « inverses » alternatifs de la présente invention, un calcul de risque peut être utilisé pour augmenter, maximiser ou exploiter les vulnérabilités d'un adversaire.
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