Introduction
Ship it safe: Secure AI and ML pipelines
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1. AI and ML Security: The Basics
AI and ML security challenges
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Building security into AI and ML development projects
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2. AI and ML Development Processes
How AI and ML products get built
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From DevOps to MLOps
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MLOps: Roles
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MLOps: Phases of development
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MLOps: Key elements and outcomes
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3. Security Techniques
Program-level standards
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Technical standards
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Integrating security into MLOps via MLSecOps or AISecOps
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Threat modeling AI and ML systems
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SBOM and AIBOM
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Testing and red team assessment
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4. Putting It All Into Action
An architectural approach: TOGAF
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A process approach: MLSecOps
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Ad hoc and informal security practices
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Integration case study: Introduction
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