Copilot and Advanced Security Enablement.

InfoMagnus delivered dual-track enablement for a global media services company, combining GitHub Copilot champion training with a GitHub Advanced Security quick-start program to embed AI and security practices across the organization.

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Building Secure, AI-Assisted Development Practices Through Champion-Led Enablement.

A global media services company engaged InfoMagnus for strategic enablement on GitHub Enterprise Cloud (GHEC) to build internal expertise, establish best practices, and secure their development operations. The engagement spanned two complementary tracks: GitHub Copilot champion enablement and a GitHub Advanced Security quick-start program.

Business Challenge.

The organization needed to scale GitHub adoption strategically across their teams while embedding security into developer workflows. Key challenges included establishing internal capability for GitHub Copilot adoption without institutional knowledge or best practices, building a champion network to evangelize and sustain Copilot adoption, implementing GitHub Advanced Security features to proactively identify and mitigate vulnerabilities, integrating security scanning and dependency management into developer workflows without disrupting productivity, and creating governance frameworks and rollout plans for enterprise-wide enablement.

Solution: Dual-Track Enablement.

GitHub Copilot Champion Enablement.

Strategic planning sessions aligned Copilot adoption with business goals and security policies. Champion training delivered foundational knowledge, best practices, and workflow integration guidance. Advisory hours provided hands-on support for real-world scenarios and extended organization-wide enablement.

GitHub Advanced Security Quick Start.

Deep-dive enablement sessions covered GHAS features, CodeQL workflows, secret scanning, push protection, and Dependabot configuration. Integration demonstrations showed third-party tool compatibility, including Trivy for Docker container scanning. Security governance and reporting guidance established roles, permissions, audit logs, policy enforcement, and automation strategies for enterprise-wide rollout.

How the Work Was Delivered.

Strategic alignment sessions with stakeholders defined champion profiles, success metrics, and adoption roadmaps. Formal enablement sessions were delivered to a core group of champions, establishing foundational knowledge and best practices. Hands-on configuration workshops for GHAS features demonstrated real-world workflows and security integration. Advisory support sessions extended expertise across the organization, and governance frameworks and policy recommendations were developed for scalable, compliant enterprise-wide adoption.

Outcomes & Impact.

  • Champions trained and equipped across both Copilot and GHAS programs, ready to sustain adoption momentum.
  • Security features embedded into developer workflows, shifting left on vulnerability detection and management.
  • Hands-on configuration and adoption support delivered, reducing implementation barriers and accelerating time to value.
  • The organization is positioned for scalable GitHub adoption with a proactive security posture and internal enablement framework.

Why Dual-Track Enablement Outperforms Either Approach Alone.

  • Champion-led adoption strategies accelerate enablement and create sustainable momentum for AI and security tooling across the organization.
  • Hands-on configuration and real-world scenario support are essential for moving beyond theoretical knowledge to practical implementation.
  • Security governance frameworks tied to developer workflows ensure policies are adopted, not resisted, and compliance becomes a natural outcome.
  • Dual-track enablement combining Copilot and GHAS amplifies value by combining developer productivity gains with proactive risk mitigation.
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InfoMagnus is an AI-native engineering company and GitHub Advanced Partner and GitHub Platform Channel Partner of the Year (AMERS) helping enterprises build intelligent software systems, modernize applications, and turn AI execution into measurable outcomes.

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