> Markdown version of [/videos/100052-spot-squash-secure-fighting-security-bugs-with-github-copilot](https://www.wearedevelopers.com/videos/100052-spot-squash-secure-fighting-security-bugs-with-github-copilot). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

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# Spot, Squash, Secure: Fighting Security Bugs with GitHub Copilot

Pure AI security scanning is costly and highly unreliable. Learn how pairing deterministic CodeQL tests with GitHub Copilot Autofix creates a flawless, scalable vulnerability remediation workflow.

- **Speakers:** [Malte Lantin](https://www.wearedevelopers.com/@malte-lantin), [Marie Theresa Brosig](https://www.wearedevelopers.com/@marie-theresa-brosig)
- **Event:** World Congress 2026 Europe
- **Published:** July 9, 2026
- **Duration:** 22:56
- **URL:** https://www.wearedevelopers.com/videos/100052-spot-squash-secure-fighting-security-bugs-with-github-copilot

## Summary

Developers increasingly rely on AI to build software rapidly, leading to the temptation to use those same AI agents to spot code vulnerabilities. However, relying purely on large language models for security analysis introduces significant challenges: the results are non-deterministic, false positives are difficult to isolate, and application programming interface token costs scale unsustainably when running deep scans on every commit. By testing an intentionally vulnerable application against various custom AI agent models, it becomes clear that an AI-only security strategy lacks the reliability required for enterprise deployment.\n\nThe most scalable approach pairs deterministic static application security testing with an intelligent, agent-driven workflow. Using github advanced security powered by the codeql engine, developers can reliably and cost-effectively map potential vulnerabilities from source to sink across every pull request. Once codeql deterministically highlights an exploit, github copilot autofix takes over, spinning up an iteration loop to draft a secure, contextual code patch. This architecture ensures that AI is applied where it excels in remediation, while leaving the concrete validation to standard continuous integration checks.\n\nTransitioning to this hybrid model solves the dual problem of cost and accuracy. An automated feedback loop prevents faulty AI hallucinations from entering production by demanding successful build processes and deterministic re-scans on newly generated pull requests. Furthermore, managing these actions through a unified repository view provides essential structural accountability, explicitly logging whether a human or a copilot agent proposed the security configuration. Ultimately, development teams should actively leverage AI to write and fix code, but entirely rely on deterministic validation to secure it.

**Keywords:** github copilot autofix, github advanced security, codeql scanner, ai-driven vulnerability scanning, deterministic static analysis, automated security remediation, continuous integration security checks, api token cost management, hallucination prevention, agentic software engineering, pull request agent, custom copilot agents, secret scanning patterns, application security posture

## Chapters

1. **Building a vulnerable application for security testing** (00:03) — Demonstrating the security risks of generating code with artificial intelligence tools.
1. **Challenges of using AI for standalone security scanning** (03:02) — Discovering why pure artificial intelligence code scanning is potentially costly and non-deterministic.
1. **Combining static analysis with AI scanning tools** (07:58) — How combining deterministic code analysis with artificial intelligence improves security scanning capabilities.
1. **Automating vulnerability remediation with Copilot Autofix** (12:04) — Using agentic tools to automatically generate and validate pull requests for identified security vulnerabilities.
1. **Validating AI generated code fixes in CI pipelines** (15:27) — Creating a robust continuous integration loop where deterministic checks validate machine generated solutions.
1. **Finding contextual secrets and managing enterprise AI costs** (16:51) — Balancing advanced capabilities like secret scanning with application token costs at an enterprise scale.
1. **Tracking AI agents in commit history and auditing** (18:31) — Achieving end-to-end transparency by viewing artificial intelligence agents as independent actors in repository change logs.
1. **Addressing deterministic exceptions and custom agent availability** (20:40) — Exploring ways to combine external API results with agent instructions and non-native platform compatibility.

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