Curriculum Overview: Securing AI Systems

This curriculum provides a comprehensive overview of the strategies, tools, and best practices required to secure Artificial Intelligence (AI) and Generative AI (GenAI) systems within the AWS ecosystem. It aligns with Domain 5: Security, Compliance, and Governance of the AWS Certified AI Practitioner (AIF-C01) exam.

## Prerequisites

Before beginning this curriculum, learners should have a foundational understanding of the following:

• Foundational AWS Knowledge: Familiarity with core services like Amazon EC2, Amazon S3, and basic networking (VPCs).
• AI/ML Fundamentals: Understanding the machine learning development lifecycle (build-train-deploy) and basic definitions of Foundation Models (FMs).
• General Cybersecurity: Basic concepts of encryption, identity management (IAM), and the difference between "security of the cloud" and "security in the cloud."

## Module Breakdown

## Learning Objectives per Module

Module 1: The Shared Responsibility Model for AI

• Differentiate between AWS responsibilities (infrastructure, physical security) and customer responsibilities (data, model tuning, application logic).
• Identify how the model shifts when using managed services like Amazon Bedrock versus self-managed models on EC2.

Module 2: Infrastructure & Network Protection

• Implement AWS PrivateLink to ensure traffic between VPCs and AI services stays within the AWS network.
• Utilize AWS Shield and AWS WAF to protect AI application endpoints from DDoS and common web exploits.

Module 3: Data Security & Engineering

• Apply Encryption at Rest and In Transit using AWS KMS (Key Management Service).
• Automate the discovery of PII (Personally Identifiable Information) in training datasets using Amazon Macie.
• Document data origins and transformations using Amazon SageMaker Model Cards and data lineage tools.

Module 4: Securing Generative AI & LLMs

• Define and mitigate Prompt Injection and Jailbreaking using Amazon Bedrock Guardrails.
• Implement input validation and sanitization to prevent malicious instructions from reaching the Foundation Model.

Module 5: Governance, Risk, and Compliance

• Configure AWS CloudTrail for logging API calls and AWS Config for monitoring resource configuration changes.
• Use AWS Audit Manager to simplify the process of mapping AI workloads to regulatory frameworks (e.g., GDPR, HIPAA).

## Success Metrics

To demonstrate mastery of this curriculum, a learner must be able to:

1. Architect a Secure Pipeline: Design a data flow that includes encryption, IAM role-based access, and automated PII scanning.
2. Threat Identification: Correctly identify at least three AI-specific attack vectors (e.g., training data poisoning, prompt injection, model inversion).
3. Service Selection: Choose the correct AWS tool for a specific security need (e.g., choosing SageMaker Clarify for bias detection versus Amazon Inspector for vulnerability scanning).
4. Policy Writing: Draft a least-privilege IAM policy for a Data Scientist role using Amazon SageMaker Role Manager.

[!IMPORTANT] Security in AI is not a "set and forget" task. Success is measured by the implementation of Continuous Monitoring and the ability to pass automated compliance audits using AWS Artifact.

## Real-World Application

Why this matters in professional roles:

• AI Security Engineer: You will be responsible for building the "defensive perimeter" around Foundation Models, ensuring that company secrets are not leaked through model prompts.
• Compliance Officer: You will use tools like AWS Audit Manager and SageMaker Model Cards to prove to regulators that your AI systems are fair, transparent, and follow data residency laws.
• Data Engineer: You will implement privacy-enhancing technologies (PETs) to ensure that sensitive data used for fine-tuning models remains confidential.

▶Click to expand: Encryption Formula Note

While AWS manages the underlying math, the security of data in transit often relies on the TLS protocol, where the strength of the encryption can be represented conceptually by the key length $n$: $P(\text{crack}) \propto 2^{-n}$ Increasing $n$ via AWS KMS customer-managed keys ensures exponential difficulty for unauthorized decryption.