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This chapter develops practical business analysis capabilities for adversarial AI security, translating technical vulnerabilities into quantifiable business impacts that drive strategic investment decisions. Organizations deploying machine learning systems face a critical challenge: communicating complex technical risks to executives who control security budgets. You will build risk quantification methodologies using Monte Carlo simulation for probabilistic financial modeling, stakeholder analysis frameworks that align security priorities with organizational objectives, and portfolio optimization techniques adapted from Modern Portfolio Theory (MPT) to balance defensive investments across multiple risk dimensions.

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Quantifying Adversarial Risk

  • 章节

  • 发表日期：

    2026年

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  • Goran Trajkovski

摘要

This chapter develops practical business analysis capabilities for adversarial AI security, translating technical vulnerabilities into quantifiable business impacts that drive strategic investment decisions. Organizations deploying machine learning systems face a critical challenge: communicating complex technical risks to executives who control security budgets. You will build risk quantification methodologies using Monte Carlo simulation for probabilistic financial modeling, stakeholder analysis frameworks that align security priorities with organizational objectives, and portfolio optimization techniques adapted from Modern Portfolio Theory (MPT) to balance defensive investments across multiple risk dimensions.