This article proposes a quantitative risk assessment and management framework that supports strategic asset-level resource allocation decision making for critical infrastructure and key resource protection. The proposed framework consists of five phases: scenario identification, consequence and criticality assessment, security vulnerability assessment, threat likelihood assessment, and benefit-cost analysis. Key innovations in this methodology include its initial focus on fundamental asset characteristics to generate an exhaustive set of plausible threat scenarios based on a target susceptibility matrix (which we refer to as asset-driven analysis) and an approach to threat likelihood assessment that captures adversary tendencies to shift their preferences in response to security investments based on the expected utilities of alternative attack profiles assessed from the adversary perspective. A notional example is provided to demonstrate an application of the proposed framework. Extensions of this model to support strategic portfolio-level analysis and tactical risk analysis are suggested.
This article develops a quantitative all-hazards framework for critical asset and portfolio risk analysis (CAPRA) that considers both natural and human-caused hazards. Following a discussion on the nature of security threats, the need for actionable risk assessments, and the distinction between asset and portfolio-level analysis, a general formula for all-hazards risk analysis is obtained that resembles the traditional model based on the notional product of consequence, vulnerability, and threat, though with clear meanings assigned to each parameter. Furthermore, a simple portfolio consequence model is presented that yields first-order estimates of interdependency effects following a successful attack on an asset. Moreover, depending on the needs of the decisions being made and available analytical resources, values for the parameters in this model can be obtained at a high level or through detailed systems analysis. Several illustrative examples of the CAPRA methodology are provided.
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