Secure AI Coding Assistant Adoption for Engineering Teams
This Skill Sprint teaches enterprises how to safely adopt AI coding assistants such as Cursor, GitHub Copilot, Claude Code, Gemini Code Assist, Amazon Q Developer.
Course Purpose
This Skill Sprint teaches enterprises how to safely adopt AI coding assistants such as Cursor, GitHub Copilot, Claude Code, Gemini Code Assist, Amazon Q Developer, JetBrains AI, Windsurf/Codeium, Replit, and similar tools. The course makes clear that safe adoption is not merely a vendor security review. A vendor review is necessary, but it is only one control within a broader adoption system that includes governance, developer guardrails, source code confidentiality, intellectual property protection, secrets protection, secure SDLC integration, AI-generated code review, testing, tool configuration, monitoring, and agentic coding risk management.
Target Audience
The course is designed for developers, engineering managers, AppSec teams, product security teams, DevSecOps teams, software architects, R&D leaders, compliance teams, and security governance teams. It is especially useful for organizations that are piloting or scaling AI coding assistants across private repositories, regulated products, customer-facing applications, infrastructure-as-code environments, or agentic coding workflows.
Prerequisites
Learners should understand basic software development practices, code review, and secure SDLC concepts. Security specialists do not need to be expert programmers, but they should be comfortable reading code examples and reasoning about common vulnerabilities. Developers do not need prior AI governance experience, but they should be willing to apply policy, review, and documentation controls to AI-assisted engineering work.
Learner Outcomes
By the end of the course, learners will be able to assess whether an AI coding assistant is appropriate for a specific enterprise engineering environment, classify safe, risky, and prohibited use cases, define secure prompting rules, prevent source code and secrets leakage, integrate AI-generated code into secure SDLC evidence, review AI-generated code for common vulnerabilities, assess vendor security posture, design agentic coding guardrails, and produce a secure rollout plan.
Business Outcomes
The course helps enterprises capture productivity benefits from AI coding tools without creating unmanaged security, privacy, compliance, intellectual property, or engineering-quality risks. It gives security and engineering leaders a common operating model for tool approval, developer enablement, exception management, and rollout governance.
Security and Governance Outcomes
The course produces concrete security outcomes: an acceptable use policy, AI coding assistant risk register, vendor questionnaire, secure SDLC integration map, developer safe prompting guide, AI-generated code review checklist, agentic coding risk assessment, and secure rollout plan. These artifacts create evidence for governance, security review, audit readiness, and secure engineering accountability.