AI-driven engineering management execution and governance

Master AI in your SDLC: practical governance, risk management, and delivery frameworks for engineering leaders.
What you'll learn
✓ Integrate AI tools into your software delivery lifecycle without introducing technical debt or compliance gaps
✓ Build an AI-assisted code review process that enhances delivery speed while still catching critical errors
✓ Design and implement a practical AI governance architecture that engineering teams will actually follow
✓ Apply framework controls like the NIST AI RMF to effectively manage AI risks, security, and threat modeling
Requirements
● Basic understanding of the software development lifecycle (SDLC) and modern engineering processes
● Experience leading software projects, engineering teams, or acting as a senior contributor or tech lead
Description
This course contains the use of artificial intelligence.
AI is already inside your SDLC. The question is who controls it - your engineers, or chance.
Most engineering managers today are caught in the middle: their teams are using AI tools daily, leadership is demanding faster delivery, and yet there is no clear framework for what "good" AI-augmented engineering actually looks like. Risks get ignored. Governance policies collect dust. Productivity claims go unmeasured.
This course changes that.
AI-driven engineering execution and governance is a practical, senior-level course for engineering managers, tech leads, and engineering directors who need to run AI-augmented teams with confidence - and be accountable for the results.
You will walk away knowing exactly how to integrate AI into your SDLC without losing control, how to build governance guardrails your team will actually follow, and how to measure whether any of it is working.
What you will learn
• How to integrate AI tooling into each phase of your software delivery lifecycle without introducing technical debt or compliance gaps
• How to build an AI-assisted code review process that still catches real problems - and doesn't create a false sense of security
• How to use AI in incident detection, response, and post-mortem workflows
• How to measure AI's actual impact on delivery speed, quality, and team health
• How to design a governance architecture that balances innovation with accountability
• How to apply the NIST AI Risk Management Framework (AI RMF) to map specific risks to concrete engineering controls
• How to model AI-specific threats and integrate security thinking into your team's daily workflow
• How to navigate privacy risks, shadow AI adoption, and regulatory pressure (GDPR, EU AI Act, and beyond)
• How to build and run an AI incident response plan before something goes wrong
Who this course is for
■ Engineering managers and tech leads who want to safely integrate AI tools into their team's workflows
■ Senior software engineers preparing for leadership roles who need to understand AI delivery strategy
■ Engineering directors and VPs responsible for establishing AI standards and accountability across multiple teams
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