PHGoverning and Measuring GenAI Across the SDLC
Released 9/2026
By Jeff Hurd
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English + subtitle | Duration: 1h 25m 27s | Size: 207.3 MB
Software teams have rushed to adopt generative AI, but most are flying blind with no clear policy on acceptable use, no answer to who's accountable when AI-generated code ships, unresolved questions about who owns that code, and productivity claims propped up by metrics that don't hold up.
Software teams have rushed to adopt generative AI, but most are flying blind with no clear policy on acceptable use, no answer to who's accountable when AI-generated code ships, unresolved questions about who owns that code, and productivity claims propped up by metrics that don't hold up. In this course, Governing and Measuring GenAI Across the SDLC, you'll gain the ability to put real guardrails around AI use without slowing your teams down. First, you'll explore how to build a practical governance framework, including an acceptable use policy, role-based accountability, and a review process that evolves with your tools and regulations. Next, you'll discover how to manage intellectual property, compliance, and regulatory risk and how to establish audit trails that make AI-generated work traceable. Finally, you'll learn how to measure GenAI's true impact with metrics that capture both benefits and hidden costs and avoid the vanity metrics that mislead. When you're finished with this course, you'll have the governance and measurement skills needed to adopt generative AI responsibly, defensibly, and with evidence to back your decisions.
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Released 9/2026
By Jeff Hurd
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English + subtitle | Duration: 1h 25m 27s | Size: 207.3 MB
Software teams have rushed to adopt generative AI, but most are flying blind with no clear policy on acceptable use, no answer to who's accountable when AI-generated code ships, unresolved questions about who owns that code, and productivity claims propped up by metrics that don't hold up.
Software teams have rushed to adopt generative AI, but most are flying blind with no clear policy on acceptable use, no answer to who's accountable when AI-generated code ships, unresolved questions about who owns that code, and productivity claims propped up by metrics that don't hold up. In this course, Governing and Measuring GenAI Across the SDLC, you'll gain the ability to put real guardrails around AI use without slowing your teams down. First, you'll explore how to build a practical governance framework, including an acceptable use policy, role-based accountability, and a review process that evolves with your tools and regulations. Next, you'll discover how to manage intellectual property, compliance, and regulatory risk and how to establish audit trails that make AI-generated work traceable. Finally, you'll learn how to measure GenAI's true impact with metrics that capture both benefits and hidden costs and avoid the vanity metrics that mislead. When you're finished with this course, you'll have the governance and measurement skills needed to adopt generative AI responsibly, defensibly, and with evidence to back your decisions.
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You do not have permission to view the full content of this post. Log in or register now.