This course examines the planning and scoping phase of the Artificial Intelligence (AI) Security Lifecycle as a critical foundation for the development of secure, compliant, and trustworthy AI systems. Unlike traditional software, AI systems derive their functional behavior from statistical inference over training data, introducing novel categories of risk associated with data quality, model behavior, and automated decision-making. As a result, cybersecurity for AI cannot be limited to technical controls applied during deployment but must be embedded within the earliest stages of system conception and design.

The course explores how strategic intent is translated into technical and organizational design decisions through structured planning and scoping processes. Students will analyze how mission definition, scope determination, governance frameworks, and risk tolerance shape both the security posture and societal impact of AI systems. Particular attention is given to the integration of regulatory compliance, ethical reasoning, and threat modeling into system architecture prior to implementation. These elements are treated not as external constraints but as design parameters that influence how AI systems are built, constrained, and supervised.

Through conceptual analysis and applied case examples, the course demonstrates how planning and scoping function as mechanisms of risk containment and accountability. Students will examine how vulnerabilities may arise not only from infrastructure weaknesses but also from biased datasets, adversarial manipulation of training processes, and unintended inference behavior. The course further emphasizes the dynamic nature of planning, highlighting the need for continuous reassessment as AI systems evolve through retraining, expansion of scope, and changes in regulatory environments.

By the conclusion of the course, students will understand planning and scoping as a socio-technical process that integrates cybersecurity engineering with governance, law, and ethics. The course positions this phase of the AI Security Lifecycle as a strategic instrument that determines not only technical resilience but also institutional legitimacy and public trust in artificial intelligence technologies.

Course Learning Objectives

• Explain how AI systems differ from traditional software systems in terms of cybersecurity risk and vulnerability.
• Analyze the role of planning and scoping as foundational phases in the AI Security Lifecycle.
• Define system mission and operational scope in ways that constrain misuse and limit security exposure.
• Evaluate how governance frameworks distribute accountability and decision-making authority for AI systems.
• Assess how regulatory and compliance obligations can be translated into technical design constraints.
• Identify and classify AI-specific assets, including training data, model parameters, and inference interfaces.
• Apply threat modeling principles to anticipate adversarial behavior and misuse scenarios in AI systems.
• Examine how ethical considerations such as fairness, transparency, and human oversight influence security planning.
• Analyze the relationship between organizational strategy and technical security architecture in AI deployments.
• Describe how planning and scoping operate as dynamic, iterative processes rather than static design steps.
• Interpret real-world case examples to illustrate how abstract planning principles become operational practices.
• Synthesize technical, legal, and ethical perspectives into coherent security planning strategies for AI systems.