GenAI SDLC Governance, Compliance & Pro Engineering Metrics

What you'll learn

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Deploy automated CI/CD pipeline guardrails to scan LLM-generated code for security, IP risks, and license compliance before merging.
Track DORA metrics alongside GenAI attribution to measure true developer velocity gains and net productivity ROI.
Establish Enterprise Zero Data Retention policies and proxy filters to prevent proprietary source code leaks to public LLMs.
Mitigate package hallucination, prompt injection, and hallucinated security vulnerabilities across the entire software development lifecycle.
Requirements

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Basic understanding of CI/CD pipelines, DevOps workflows, and software development lifecycle management. No advanced machine learning experience required.
Description

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This course contains the use of artificial intelligence.
Ship GenAI products with evidence, not guesswork.
GenAI projects do not fail only because the model performs poorly. They fail when teams cannot define ownership, evaluate real risks, document decisions, or prove that a release is ready.
In this hands-on course, you will build a practical governance and measurement system for GenAI applications across the software development lifecycle. You will learn how to apply governance to chatbots, RAG applications, copilots, and agentic workflows without creating a slow, bureaucratic approval process.
Who this course is for

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- AI product managers launching GenAI features
- Engineering managers and software leaders responsible for delivery quality
- AI, ML, MLOps, and platform engineers building GenAI applications
- Security, privacy, risk, and compliance professionals supporting AI programs
- Consultants and transformation leaders designing responsible AI operating models
What You Will Learn

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- Define GenAI system boundaries across models, data, prompts, retrieval, tools, and people
- Classify use cases with a practical risk-tiering model
- Create a governance RACI with clear decision rights and escalation paths
- Build a traceability record for model, prompt, data, test, and release changes
- Design evaluation datasets that reflect real user tasks and failure modes
- Measure quality, groundedness, safety, privacy, security, cost, latency, and user reliance
- Set release gates with thresholds, owners, evidence, and residual-risk decisions
- Test for prompt injection, data leakage, unsupported answers, and unsafe tool actions
- Monitor production behavior and define rollback and incident-response triggers
- Govern agentic workflows with tool permissions, human approval points, and action logs
Requirements

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Basic knowledge of software delivery, product development, or GenAI applications is helpful. No programming, legal qualification, or advanced machine-learning experience is required.
Final project
Create a portfolio-ready GenAI SDLC Governance Pack for a real or simulated GenAI application. You will submit a use-case profile, risk tier, RACI, evaluation scorecard, release gate, traceability register, and monitoring plan that can be shown to employers, clients, or internal stakeholders.
Who this course is for

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Engineering Directors, Software Architects, DevSecOps Leaders, and Tech Leads who manage teams using AI coding assistants and need to enforce security, IP compliance, and track real ROI.
Homepage

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