Building and Deploying Secure AI: Practical Strategies for Developers

The course blends actionable guidance (e.g., AI-assisted coding security) with hands-on insights into securing AI itself (e.g., protecting model endpoints, managing drift, and red teaming AI pipelines). After completing this workshop, participants will be equipped with both the technical know-how and the processes needed to embed security into every stage of their AI-driven products.

Day 1: Using AI to Build Secure Software

1. Introduction to AI Security

Topics

• Key AI security concepts and common threats
• Overview of AI-related vulnerabilities in software development
• Quick review of how attackers exploit AI-driven software

Learning Objectives

• Understand the foundational principles of AI security
• Recognize the broad threat landscape surrounding AI

2. AI for Code Creation

Topics

• Using AI-powered tools (e.g., ChatGPT, Copilot) for code generation
• Common pitfalls and vulnerabilities (injected flaws, licensing, dependency issues)
• Secure usage patterns and safe prompts

Learning Objectives

• Assess the security implications of AI-driven code generation
• Adopt best practices to minimize vulnerabilities introduced by auto-generated code

3. Secure AI Development Lifecycle

Topics

• Integrating security throughout AI/ML system development (planning, data ingestion, modeling, deployment)
• Secure DevOps and MLOps practices (CI/CD with security gates)
• Handling sensitive data used to train AI models

Learning Objectives

• Incorporate security gates into each phase of AI software development
• Properly manage and protect training data and models

4. Threat Modeling for AI Systems

Topics

• Unique threat modeling considerations for AI/ML pipelines (data poisoning, model extraction)
• Differences between typical web-app threat modeling vs. AI-centric threats

Learning Objectives

• Identify critical assets and attack vectors in AI workflows
• Apply threat modeling to preempt attacks unique to AI pipelines

5. OWASP Top 10 for Large Language Model (LLM) Applications

Topics

• Top vulnerabilities specific to LLM-based applications
• Prompt injection, data exfiltration, malicious chat manipulation
• Recommendations, mitigations, and real-world case studies

Learning Objectives

• Recognize and remediate LLM-specific security flaws
• Secure LLM-based features in a production environment

Day 2: Building Secure AI Systems (75%)

1. Supply Chain Security in AI

Topics

• Third-party libraries, datasets, and pre-trained models
• Checking trustworthiness of external dependencies (data lineage, library patch levels)
• Dependency scanning tools and strategies

Learning Objectives

• Understand how AI supply chain vulnerabilities can undermine entire systems
• Implement best practices to vet and protect external model/data sources

2. Adversarial Machine Learning

Topics

• Types of adversarial attacks (evasion, poisoning, model inversion)
• Techniques to detect and mitigate adversarial inputs
• Gradient masking pitfalls and robust model training

Learning Objectives

• Recognize adversarial attacks and how they manifest
• Integrate detection and defense strategies into AI models

3. Red Teaming AI Systems

Topics

• How to structure AI-specific penetration tests
• Tools and frameworks for adversarial testing
• Red vs. Blue Team exercises for AI pipelines

Learning Objectives

• Conduct targeted security assessments on AI systems
• Operationalize red teaming to continuously improve defenses

4. AI Model Updates and Patching

Topics

• Lifecycle management for AI models in production
• Security-driven versioning and rollback strategies
• Responding quickly to emerging threats or discovered vulnerabilities

Learning Objectives

• Implement robust model update/patching workflows
• Reduce downtime and exposure during patch cycles

5. AI Model Interpretability and Security

Topics

• Why interpretability is critical for detecting malicious behavior or biases
• How more interpretable models can reveal vulnerabilities (model extraction, inversion attacks)

Learning Objectives

• Balance interpretability with security concerns
• Use interpretability tools to spot anomalies and potential threats

6. Access Control Design for AI

Topics

• Securing AI workloads and vector databases with strict role-based and zero-trust principles
• Credential management for AI model endpoints (API tokens, ephemeral credentials)

Learning Objectives

• Architect AI systems with principle-of-least-privilege at every layer
• Apply zero-trust approaches to protect critical AI resources

7. AI Model Drift and Security Monitoring

Topics

• How models degrade over time (concept drift, data distribution shift)
• Continuous monitoring and anomaly detection in production
• Mitigating risks introduced by drift (potential new vulnerabilities)

Learning Objectives

• Set up effective monitoring to detect model performance and security anomalies
• Know when/how to trigger model retraining or security re-evaluations

8. Course Conclusion & Next Steps

Topics

• Recap of key takeaways
• Linking Day 1 (secure use of AI) and Day 2 (secure AI systems)
• Resources for continued learning, tool recommendations, community best practices

Learning Objectives

• Solidify an actionable checklist for secure AI development and usage
• Encourage ongoing improvement with guidelines and resources

This two-day curriculum ensures attendees leave with practical AI-assisted secure software development strategies and a robust understanding of how to build and maintain secure AI systems. Feel free to adjust session lengths and scheduling to match your conference format.