Securing AI/ML Systems: Protecting the Intelligent Attack Surface

Treat your AI and machine learning deployments as systems that have to be defended, not a governance checkbox. This course walks the data, the model, the pipeline and the application in turn — the line between ordinary cybersecurity exposure and AI-specific exposure, then the lifecycle from design to retirement and where each security gate belongs. A full module covers adversarial machine learning: how training data gets poisoned, models get fooled, privacy attacks pull training information back out, and models get stolen outright, each paired with defenses, keyed to the NIST adversarial ML taxonomy (AI 100-2 E2025). Generative AI gets its own module too: prompt injection, sensitive information disclosure, insecure output handling, and what retrieval-augmented generation, plugins and outside data sources drag in.

Two more modules go where most training doesn't: treating trained models, weights and checkpoints as assets needing provenance checks, and the AI supply chain, where a dependency can be just as broken as any other — a real problem given how casually teams pull weights off public repositories. A closing workshop puts you in front of a fictional AI system to threat-model, with a hardening plan to argue for. Built for whoever secures the system, not whoever writes the policy; for the awareness tier, start with AI Security Fundamentals for the Cyber Workforce (Self-Paced).

What You Will Learn

• Identify the components of an AI/ML system architecture and distinguish models, datasets, pipelines, applications and infrastructure
• Distinguish traditional cybersecurity risk from AI-specific security risk
• Map security considerations across the full AI lifecycle from design through retirement
• Protect the confidentiality, integrity, availability and provenance of training and operational data
• Identify data poisoning and manipulation risks across collection, labeling and preprocessing
• Explain poisoning, evasion, privacy and model-extraction attacks, and apply defenses that increase robustness
• Treat models, weights and checkpoints as protected assets, and verify provenance and integrity before deployment
• Evaluate third-party models, datasets, libraries and frameworks as supply-chain dependencies
• Identify direct and indirect prompt injection, insecure output handling, and retrieval-augmented generation risk
• Secure ML pipelines, orchestration platforms, APIs and deployment processes
• Detect abnormal inputs, outputs and model behavior, and distinguish performance degradation from compromise
• Develop AI-specific incident response including containment, model rollback and post-incident validation

Course Outline

Module 1

• Know What You're Defending: AI/ML Security Foundations
• AI, machine learning, deep learning, generative AI and foundation-model concepts
• System architecture components
• Assets, trust boundaries, interfaces and dependencies
• Traditional versus AI-specific risk
• Security, resilience, robustness, privacy and trustworthiness

Module 2

• Follow the Model: The AI/ML Security Lifecycle
• Security across design, development, training, testing, deployment, operation and retirement
• Responsibilities across developers, operators, users and service providers
• Risks in experimentation and model development environments
• Protecting development, testing, staging and production
• Security gates through the lifecycle

Module 3

• Data Is the Fuel: Securing Training and Operational Data
• Training, validation, testing, inference and operational datasets
• Confidentiality, integrity, availability and provenance
• Access control for sensitive datasets
• Data poisoning and manipulation
• Protecting collection, labeling, transformation and preprocessing
• Monitoring quality, integrity and lineage

Module 4

• Attack the Learning Process: Adversarial Machine Learning
• Attacker objectives
• Poisoning attacks against training data and learning processes
• Evasion attacks against deployed models
• Privacy attacks targeting models and training information
• Model extraction and information disclosure
• Defensive concepts for robustness and resilience

Module 5

• Guard the Model: Model Integrity & Intellectual Property
• Models, weights, parameters and configurations as protected assets
• Securing repositories and storage
• Access control for model files, checkpoints and artifacts
• Provenance and integrity verification before deployment
• Theft, substitution and tampering
• Secure versioning, approval and change management

Module 6

• Secure the AI Supply Chain: Models, Libraries & Dependencies
• Third-party models, datasets, libraries, frameworks and services as dependencies
• Evaluating externally sourced components
• Verifying provenance and integrity of acquired artifacts
• Malicious or vulnerable dependencies in development environments
• Controlling imports, updates, plugins and integrations
• Ongoing supplier monitoring

Module 7

• When AI Starts Talking: Generative AI & LLM Security
• Security implications of large language models
• Direct and indirect prompt injection
• Sensitive information disclosure and unintended exposure
• Insecure output handling and downstream risk
• Retrieval-augmented generation, plugins, tools and external data sources
• Isolation, access control, validation and least privilege for generative AI applications

Module 8

• Secure the Pipeline: MLOps, APIs & Deployment
• Protecting pipelines, orchestration platforms and automation workflows
• Securing repositories, build environments and deployment
• Protecting secrets, keys, tokens and credentials
• Authentication and authorization for AI services and APIs
• Separating development, training, testing and production privileges
• Detecting unauthorized change

Module 9

• Watch the Machine: Monitoring, Detection & Incident Response
• Security logging and telemetry for AI environments
• Monitoring model access, administrative actions, API activity and configuration change
• Detecting abnormal inputs, outputs, behavior and usage
• Distinguishing performance degradation from compromise
• AI-specific incident response
• Containment, model rollback, recovery and post-incident validation

Module 10

• Govern, Test, Defend: AI Security Risk Workshop
• Identify assets, trust boundaries and dependencies in a fictional AI system, develop AI-specific threat scenarios and attack paths, find data, model, pipeline, infrastructure and generative AI vulnerabilities, evaluate likelihood and impact, then select, prioritize and defend a risk-based hardening plan

This course is available for 365 days. You can choose when to start your access period. Once you activate, you will have 365 days to complete it (access the course materials, quizzes, projects and videos). You may request one extension of seven (7) days. Other extension requests will be evaluated on a case-by-case basis. Videos are not downloadable.