DevSecOps Enablement for Federal Contractors.

InfoMagnus built an SSDF-aligned DevSecOps framework for Drive System Design, connecting regulatory compliance requirements to repeatable GitHub workflows and turning compliance into a competitive differentiator.

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Drive System Design: Enabling Secure DevSecOps in a Regulated Environment.

Drive System Design, a federal contractor providing engineering and technical services to government and defense-adjacent clients, engaged InfoMagnus to modernize their development practices while maintaining strict alignment with regulatory and security expectations. The organization needed to move beyond incremental tool implementations to embrace a holistic DevSecOps posture grounded in secure software development frameworks. The challenge was not finding a vendor to install GitHub — it was finding a partner with sufficient depth in regulated DevSecOps to guide development teams through the strategic, architectural, and operational dimensions of secure software supply chain management.

Business Challenge: Modernizing While Maintaining Compliance.

Development practices were fragmented across teams, lacking standardized secure coding, version control discipline, or consistent automation frameworks. Regulatory expectations from government clients required evidence of secure development processes, supply chain traceability, and audit-ready controls — but the path from compliance requirements to daily engineering work was unclear. Existing tool ecosystems lacked cohesion and did not generate the artifacts required for defensible compliance postures. Internal teams possessed strong domain expertise in defense technology but needed external validation and structured guidance on DevSecOps best practices aligned to frameworks like SSDF.

Solution Delivered: Strategic Advisory & Practical DevSecOps Design.

SSDF-Aligned Practice Framework.

Rather than adopting SSDF as a compliance checklist, InfoMagnus mapped each SSDF practice to specific, repeatable engineering workflows. Secure practices were connected to pull request workflows, code review requirements, and branch protection rules. Secure supply chain practices were framed as dependency scanning, artifact provenance, and SBOM generation. Continuous hardening leveraged advanced security scanning and CI/CD pipeline integration.

Platform Capabilities & Evidence Generation.

InfoMagnus mapped how GitHub’s native capabilities generate audit evidence and compliance artifacts: code review and merge audit trails support evidence of human review controls; GitHub Advanced Security provides continuous vulnerability visibility; GitHub Actions create traceable, repeatable deployment workflows with full execution history; and integration with third-party tools extends supply chain visibility and cross-system traceability.

Advisory Sessions & Working Approach.

Security and compliance architecture workshops clarified how GitHub components map to SSDF practice requirements. DevSecOps workflow design sessions produced actionable templates and checklists. A platform modernization roadmap outlined phased enablement prioritizing regulatory impact, and an evidence collection strategy defined artifacts for government audits and compliance assessments.

Outcomes & Strategic Positioning.

Drive System Design is now positioned to confidently discuss secure development practices in client proposals and government security assessments, scale development practices across teams with repeatable evidence-generating workflows, and attract talent by offering a transparent approach to secure development aligned with regulatory rigor. This engagement demonstrates the strategic value of leading with advisory expertise rather than tool configuration, helping Drive System Design transform compliance from a cost center into a differentiator in federal contracting.

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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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