Curriculum Overview: Security and Privacy Considerations for AI Systems

Prerequisites

Before diving into the security and privacy considerations of Artificial Intelligence (AI) and Generative AI (GenAI) systems, learners should have a solid foundation in the following areas:

• Cloud Computing Fundamentals: A basic understanding of cloud infrastructure, particularly AWS environments (e.g., EC2, VPCs, and IAM).
• AWS Shared Responsibility Model: Comprehension of how security in the cloud is divided between the provider (security of the cloud) and the customer (security in the cloud).
• Basic AI/ML Concepts: Familiarity with the definitions of Artificial Intelligence, Machine Learning, Deep Learning, and Foundation Models (FMs).
• General Cybersecurity Principles: A baseline understanding of encryption, network security, and access control concepts.

[!IMPORTANT] If you are unfamiliar with the shared responsibility model, review it before proceeding. In AI workloads, securing the underlying foundation model training environment may fall to the cloud provider, while protecting your specialized training data and prompt inputs remains your responsibility.

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Module Breakdown

This curriculum is designed to progress from foundational threat awareness to advanced, GenAI-specific mitigation strategies.

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Learning Objectives per Module

Upon completing this curriculum, learners will achieve the following targeted outcomes:

Module 1: AI Threat Landscape

• Identify signs of system compromise using AI-powered threat detection tools like Amazon GuardDuty.
• Explain the role of vulnerability management, including code reviews, patching, and regular security assessments, in minimizing the AI attack surface.

Module 2: Infrastructure & Network

• Design resilient architectures utilizing strict access controls, IAM user groups, and network segmentation to isolate AI pipelines.
• Apply edge protection tools like AWS WAF and Amazon Cognito to safeguard application endpoints from denial-of-service (DoS) and unauthorized access.

Module 3: Data Protection

• Implement robust encryption strategies for data at rest and data in transit, treating cryptographic keys as critical operational assets.
• Evaluate data governance strategies, encompassing data integrity, privacy-enhancing technologies, and source citation.

Module 4: GenAI Vulnerabilities

• Define the mechanisms behind generative AI-specific risks, including prompt injection, prompt leakage, model poisoning, and jailbreaking.
• Develop sanitization, validation, and real-time LLM guardrails to teach systems to ignore manipulative instructions.

Module 5: Governance & Compliance

• Utilize the AWS Generative AI Security Scoping Matrix to assess and implement tailored security controls.
• Configure centralized auditing and monitoring using AWS Security Hub, AWS Config, and AWS CloudTrail.

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Success Metrics

How will you know you have mastered this curriculum? You should be able to check off the following competencies:

• Threat Mitigation Mapping: You can map specific AI vulnerabilities (e.g., data tampering, malicious prompt injection) to their corresponding cloud mitigation service (e.g., AWS WAF, Bedrock Guardrails).
• Architectural Design: You can architect a multi-tiered defense-in-depth security model for an automated generative text API.
• Incident Response Automation: You understand how to use tools like Amazon EventBridge to trigger automated responses (e.g., quarantining an EC2 instance) when an anomaly is detected.
• Regulatory Readiness: You can explain how data residency, retention, and lifecycle logging strategies fulfill broader compliance requirements.

[!TIP] A great way to test your mastery is to simulate an attack scenario: "If a user submits a support ticket with a hidden prompt instructing the system to dump sensitive training data, how does our system stop it?"

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Real-World Application

Security in AI is not a theoretical exercise; it is a critical business imperative. As the attack surface grows with the adoption of generative AI, the traditional "cat-and-mouse" game between attackers and defenders has intensified.

In the real world, an unpatched system or a poorly designed prompt interface can lead to catastrophic data exfiltration. Consider prompt leaking, where competitors reverse-engineer the carefully crafted prompts that give your customer service AI its unique personality, or model poisoning, where threat actors embed malicious instructions into the training data itself.

To combat this, enterprise environments utilize a Defense-in-Depth strategy. If one layer fails, the next layer contains the threat.

▶Deep Dive: The Generative AI Security Scoping Matrix

Developed by AWS, this real-world framework helps organizations assess security controls across five distinct scopes. Your responsibility scales based on your architectural choices:

1. Consumer Applications: Using out-of-the-box SaaS AI tools (lowest control, lowest direct security management).
2. Enterprise Applications: Integrating AI through managed APIs (requires strong input/output sanitization).
3. Pre-Trained Models: Using existing Foundation Models in your own environment (requires infrastructure and data protection).
4. Fine-Tuned Models: Adapting a model with your own data (requires rigorous data quality and poisoning prevention).
5. Self-Trained Models: Building a model from scratch (maximum responsibility across the entire lifecycle and supply chain).

By understanding these layers, AI Practitioners can right-size their security efforts and prevent adversarial attacks before they affect users.