Responsible AI for Engineering Leaders

Class Duration

7 hours of live training delivered over 1-2 days to accommodate your scheduling needs.

Student Prerequisites

Engineering management or technical leadership experience
No prior AI/ML background required

Target Audience

Engineering managers, tech leads, architects, and VPs of Engineering responsible for setting AI tool policy for their teams. Equally relevant for legal and compliance stakeholders who need to understand the technical reality behind AI-generated code, and for learning and development leaders designing responsible AI upskilling programs.

Description

This course equips engineering leaders with the knowledge and frameworks to govern AI-assisted development responsibly. We cover the legal landscape for AI-generated code (copyright, IP ownership, and open-source license contamination), data privacy and confidentiality risks, procurement evaluation criteria, team policy design (acceptable use, review requirements, prompt libraries), honest productivity measurement, and organizational change management for AI tool rollouts. The course is heavier on policy templates and decision frameworks than on code labs.

Learning Outcomes

Explain the current legal landscape for copyright and IP ownership of AI-generated code.
Identify open-source license contamination risks and the tooling available to mitigate them.
Evaluate AI tool vendor contracts for data retention, training use, and confidentiality provisions.
Draft a team acceptable-use policy for AI coding assistants.
Design a prompt library governance process for standardizing team AI usage.
Define honest productivity metrics for AI-assisted development programs.
Build a phased rollout plan for AI tool adoption with appropriate checkpoints.

Training Materials

Comprehensive courseware is distributed online at the start of class. All students receive a downloadable MP4 recording of the training. Policy template library included.

Software Requirements

No special software required. Labs use policy templates and decision-framework worksheets.

Training Topics

The Current AI Tool Landscape for Leaders

Which tools teams are using and why it matters for policy
Autonomy spectrum: assistants to fully autonomous agents
Risk profile changes by autonomy level

IP, Copyright, and Licensing

Copyright status of AI-generated code: current legal landscape
Open-source license contamination risk (copyleft, permissive)
Vendor policies: GitHub Copilot, Cursor, Anthropic, OpenAI
Tooling for license scanning AI-generated output

Data Privacy and Confidentiality

What data each tool transmits to vendor servers
Training data use clauses in vendor agreements
Enterprise and business tier data protections
Handling regulated data (HIPAA, PCI, GDPR) with AI tools

Procurement Evaluation

Due diligence checklist for AI tool vendors
Security review criteria: SOC 2, penetration test history
Data processing agreements and DPA requirements
Multi-tool vs. standardization strategy

Team Policy Design

Acceptable use policy framework and template
Review requirements for AI-generated code
Prompt library governance: creation, approval, versioning
CLAUDE.md / .instructions.md team standards
Escalation path for policy edge cases

Productivity Measurement

Why simple "lines of code" metrics mislead
Leading indicators: cycle time, review iteration count
Lagging indicators: defect rate, time-to-production
Developer experience surveys and NPS for AI tools
Communicating results to executives and boards

Change Management and Rollout

Phased adoption: pilot → broad rollout → optimization
Identifying and enabling AI champions on the team
Addressing developer resistance and concerns
Training and onboarding requirements

Governance Frameworks

NIST AI RMF for engineering organizations
EU AI Act applicability to developer tooling
Building an AI governance committee
Incident response when AI tooling causes a production issue

Workshop

Draft an acceptable-use policy for participant's organization
Productivity measurement plan exercise
Q&A session