How Relay Network Adopted AI Coding Securely and Built the Foundation for Agentic Development

2026-05-29 · Source: Blog RSS Feed | Snyk · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Software Development & Engineering · Depth: Intermediate, medium

Summary

Relay Network, a secure B2C communications platform innovator, successfully integrated AI coding assistants like GitHub Copilot into its development workflow by May 29, 2026, prioritizing security from inception. Facing the challenge of AI accelerating insecure code generation, the company adopted Snyk to embed security findings and fixes directly into the coding experience, moving beyond reactive pull request checks. This "secure at inception" approach, enhanced by custom pre-commit hooks developed by Security Engineer Esaie Batoula, ensures developers address vulnerabilities in real-time. The strategy led to rapid adoption, significantly reduced Mean Time To Remediation (MTTR) to under 24 hours for critical vulnerabilities, and fostered AI as a learning engine for engineers. Relay Network plans to transition to Claude Code, expanding beyond Copilot, Codex, and Windsurf, establishing a blueprint for secure AI adoption and agentic development security.

Key takeaway

For MLOps Engineers or Security Engineers integrating AI coding assistants, prioritize "secure at inception" by embedding security tools directly into your development workflow. Implement custom pre-commit hooks and sanctioned AI tools like Snyk with GitHub Copilot to catch vulnerabilities as code is written, not after. This approach reduces friction, accelerates remediation, and empowers your developers to own security, preparing your organization for future agentic development securely.

Key insights

Embedding security "secure at inception" into AI-assisted development prevents vulnerabilities and accelerates remediation.

Principles

• Security must be embedded, not a separate function.
• Provide sanctioned AI tools to reduce shadow AI.
• Collaborate, don't segregate, engineering and security.

Method

Integrate security scanning (Snyk) with AI coding assistants (GitHub Copilot) and custom pre-commit hooks to identify and fix vulnerabilities in real-time during code creation.

In practice

• Implement pre-commit hooks for immediate security scans.
• Use AI to generate remediation suggestions on the spot.
• Partner with senior engineers for workflow adoption.

Topics

• AI Coding Assistants
• Secure Software Development
• Snyk
• GitHub Copilot
• Pre-commit Hooks
• Agentic Development

Best for: CTO, VP of Engineering/Data, Director of AI/ML, Security Engineer, MLOps Engineer, Software Engineer

Related on AIssential

• Fragments: April 21 — AI's rapid advancement necessitates revisiting foundational software principles and developing robust security for "permission hungry" agents.
• GitHub recognized as a Leader in the Gartner® Magic Quadrant™ for Enterprise AI Coding Agents for the third year in a row — AI coding agents are shifting software development from code generation to orchestrating outcomes across the entire SDLC.
• Protestware by open source maintainer to hinder agentic coding: The jqwik 1.10.0 Prompt Injection — Open-source maintainers can embed AI prompt injections in dependencies, hidden from humans but legible to agents, posing a novel supply chain risk.
• Vibe Coding Gets an Upgrade — AI development is converging towards multi-agent orchestration and event-driven automation, making "vibe coding" the new standard.
• Hype and Reality of the AI Coding Shift — Rapid AI coding adoption creates a critical trust and verification gap, shifting developer toil from creation to quality assurance.
• Securing the Agent Skills Registry: How Snyk and Tessl Are Setting the Standard — AI agent skills introduce unique security risks, requiring specialized scanning beyond traditional code vulnerability detection.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Blog RSS Feed | Snyk.