Case Study: How a 50-Engineering Team Standardized AI Security Across Copilot, Cursor, and ChatGPT

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📋 Note: This is a composite case study built from realistic deployment scenarios using our testlab infrastructure. It is not a real customer. The names, numbers, and quotes below are representative of the customer segment and are not tied to any specific organization. Real customer case studies will be published with explicit written consent.

Executive Summary

Background

“TechFlow” is a 50-person engineering team at a Series-A SaaS company. Their developers use a mix of AI coding tools — GitHub Copilot in VS Code, Cursor as a standalone editor, ChatGPT in the browser for research, and a few engineers running local LLMs via Ollama.

The engineering manager, “Sarah,” had a growing concern: developers were pasting code into AI tools multiple times per day, and she had no visibility into what was being sent. After a near-miss where a junior engineer almost sent a Stripe API key to ChatGPT, she mandated an AI security policy — but had no tools to enforce it.

The Challenge

Sarah needed:

1. Coverage across all AI tools — Copilot, Cursor, ChatGPT, local LLMs. A browser-only solution wouldn’t help.
2. No productivity impact — engineers are already resistant to security tooling. It had to be invisible.
3. Centralized reporting — she needed to show the CTO that the policy was working.
4. Low cost — Series-A budget. No enterprise contracts.
5. Open source — the security team wanted to audit the detection logic.

The Deployment

Step 1: Rampart IDE Plugin (all 50 engineers)

Each engineer installed Rampart in their editor:

• 35 VS Code / Copilot users: Installed the AegisGate Rampart extension from the VS Code marketplace
• 10 Cursor users: Same extension (Cursor is VS Code-compatible)
• 5 Neovim / local LLM users: Installed the rampart-lsp binary and added it to their LSP config

Time: ~2 hours (distributed via Slack, engineers self-installed)

Step 2: Platform Developer Tier (central policy)

Sarah deployed AegisGate Platform on a single Docker container:

docker run -d -p 8080:8080 \
  -v ./aegisgate-data:/data \
  ghcr.io/aegisgatesecurity/aegisgate-platform:v4.4.0

She configured:

• Detection policy: Block secrets (API keys, database passwords, SSH keys), warn on PII
• Audit logging: Enabled with PII redaction
• Web UI: Accessed at http://platform.internal:8080/ui/

Time: 30 minutes

Step 3: Rampart Proxy Mode (optional, for API calls)

The 5 engineers using local LLMs and direct API calls configured Rampart’s proxy mode to route through Platform for centralized logging:

./aegisgate-rampart --port=8443 --upstream=http://platform.internal:8080

Results (First 90 Days)

Breakdown of 47 caught incidents:

Engineer Feedback

Sarah surveyed the team after 90 days:

“It’s just like lint warnings. I don’t even notice it until it catches something, and when it does, I’m always glad it did.” — Senior backend engineer

“I was skeptical about another security tool, but Rampart doesn’t slow me down at all. The one time it caught a Stripe key I was about to send to Copilot, it paid for itself.” — Full-stack engineer

“As someone who uses Cursor, I was worried the plugin wouldn’t work. But it installed just like any VS Code extension and works perfectly.” — Frontend engineer

Would recommend to other teams: 47/50 (3 said “neutral” — they hadn’t triggered any warnings yet)

Key Takeaways

1. Multi-tool coverage is essential — a team using Copilot, Cursor, ChatGPT, and local LLMs needs protection at every surface. Browser-only tools miss 60% of AI interactions.
2. Invisible security works — engineers adopted Rampart because it didn’t change their workflow. ~5ms detection is imperceptible.
3. Centralized reporting changes the conversation — instead of “I think we’re okay,” Sarah can show the CTO exact numbers: 47 caught incidents, 0 leaks, $79/month.
4. Free + low-cost is viable — Rampart is free for all 50 engineers. Platform Developer at $79/mo provides centralized policy and audit. Total cost is less than one engineer’s coffee budget.
5. Open source builds trust — the security team audited the detection patterns and confirmed they matched the company’s secret scanning policy.

Try It Yourself

• ⚡ Download Rampart — free for all editors
• 🏢 Deploy Platform — free tier or $79/mo Developer
• 📊 See Pricing — compare all tiers
• 🔧 IDE Integration Guide — setup for VS Code, JetBrains, Neovim

This is a composite case study. The scenario, metrics, and quotes are representative of the 50-person engineering team segment and were validated using AegisGate’s testlab infrastructure with 50 simulated concurrent users.