Training course · OpenAI Codex · Security & DevSecOps

OpenAI Codex for Security and DevSecOps Teams

Delegate development work with OpenAI Codex: CLI and cloud agent, AGENTS.md, sandboxing, approvals and team PR workflows.

Duration
2 days (14 hrs)
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

“OpenAI Codex for Security and DevSecOps Teams” is a hands-on course for roles such as Application security engineers and security champions in development teams, DevSecOps and platform engineers responsible for CI/CD pipelines and Penetration testers and red team members. Rather than learning OpenAI Codex in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from Security & DevSecOps.

OpenAI Codex is a coding agent that runs both locally, as a command-line tool and IDE extension, and as a cloud agent. Locally, Codex reads your repository, proposes changes and runs commands inside a sandboxed environment. In the cloud, it works on delegated tasks in parallel inside isolated containers and returns results as traceable diffs or pull requests. Over two days you will learn how to combine both modes effectively and how to scope tasks so that the agent can complete them reliably and with minimal back-and-forth.

Control and security sit at the heart of the course. With AGENTS.md you hand Codex project knowledge, build and test commands, and conventions. You configure approval modes and sandboxing so that the agent only does what you permit, and you set up cloud environments with suitable setup scripts. You will also integrate Codex into your pull request workflows, use automated code review, and define team rules on which tasks are delegated and how results are checked before they are merged into the main branch.

AI coding assistants and agents affect application security from two directions. They speed up threat modelling, code review and detection engineering, yet they also produce new code faster than traditional security gates can absorb. Security and DevSecOps teams therefore use AI to triage SAST findings, propose fixes for vulnerabilities, harden CI/CD pipelines and scan infrastructure as code for misconfigurations. What matters is a controlled setup: agents running with least privilege, no secrets in the prompt context, every change traceable through a pull request and human sign-off on security-critical decisions. In this course you learn to use AI as a force multiplier for your security programme while keeping the new risks of generated code, prompt injection and insecure tool integrations firmly under control.

The course is delivered live – either online in a virtual classroom or on-site in our training rooms in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run it exclusively for your team.

Who is this course for?

  • Application security engineers and security champions in development teams
  • DevSecOps and platform engineers responsible for CI/CD pipelines
  • Penetration testers and red team members
  • SOC analysts and detection engineers
  • Developers accountable for secure software supply chains

Typical challenges we address

  • A flood of SAST, DAST and SCA findings with a high share of false positives
  • Security review becomes a bottleneck as AI-assisted teams ship far more code
  • Misconfigurations in Terraform, Kubernetes manifests and cloud policies go unnoticed
  • New attack surfaces from LLM integrations, prompt injection and over-privileged agents

What you will be able to do

  • You use Codex locally and in the cloud for the development tasks each mode suits best.
  • You write an AGENTS.md that guides the agent reliably through your project.
  • You configure approval modes and sandboxing to match your security requirements.
  • You delegate tasks to the cloud agent and review the results systematically.
  • Use AI agents for triage, review and fix generation without giving up control over security-critical changes.
  • Integrate AI-assisted checks into CI/CD pipelines alongside your existing SAST, SCA and IaC scanners.
  • Assess and mitigate the specific risks of AI-generated code, prompt injection and agent tool access.

Course outline

Day 1

Module 1 Codex at a glance: CLI, IDE and cloud
  • Comparing how Codex works locally and in the cloud
  • Installing the Codex CLI, signing in and starting it in a repository
  • Interactive sessions, non-interactive execution and model choice
  • Distinguishing tasks that suit a coding agent from those that do not
Module 2 Project knowledge with AGENTS.md
  • Building an AGENTS.md with build, test and lint commands
  • Stating conventions, architectural notes and prohibitions precisely
  • Using nested AGENTS.md files for monorepos and sub-projects
  • Checking effectiveness and refining instructions iteratively
Module 3 Sandboxing, approvals and security
  • Choosing approval modes deliberately, from read-only to largely autonomous
  • Understanding sandbox boundaries for file system and network access
  • Handling secrets, dependencies from the internet and prompt injection
  • Configuration files for team standards and safe defaults
Module 4 Delegating tasks to the cloud agent
  • Configuring cloud environments with setup scripts and dependencies
  • Writing tasks with a clear goal, context and acceptance criteria
  • Launching several tasks in parallel and comparing the results
  • Using logs and test output to understand the proposed solutions

Day 2

Module 5 PR workflows and code review in the team
  • Turning results into pull requests and requesting follow-up changes
  • Using automated Codex code review on GitHub
  • Embedding Codex in scripts and CI pipelines
  • Setting team rules for delegation, review duties and accountability
Module 6 Practice lab: Security & DevSecOps
  • Faster threat modelling
  • SAST finding triage
  • Secure code review
  • Hardening IaC and containers
  • Detection engineering
  • Securing the supply chain
Module 7 Data protection, quality & transfer to daily work
  • Source code, vulnerability data and logs are highly sensitive: you learn which data may be shared with AI services, how to keep secrets out of the context and how AI use aligns with NIS2, the Cyber Resilience Act, the GDPR and the EU AI Act.
  • Ground rules for safe AI use in your team
  • Checking results: facts, sources, tests and the four-eyes principle
  • Personal action plan for the next 30 days
  • Open questions, outlook and resources for further learning

Practical scenarios from your field

Faster threat modelling

Derive STRIDE-based threat analyses from architecture descriptions and data flow diagrams, identify trust boundaries and turn countermeasures into testable requirements ready for the backlog. The model is updated rather than rebuilt when the architecture changes.

SAST finding triage

Match static analysis results against real code context, assess reachability and exploitability, dismiss false positives with a documented rationale and prepare minimal fixes for genuine vulnerabilities as pull requests.

Secure code review

Have pull requests checked specifically for injection flaws, broken authorisation, insecure deserialisation and cryptographic mistakes, with references to OWASP categories and a clear explanation for every finding.

Hardening IaC and containers

Analyse Terraform modules, Helm charts and Dockerfiles for over-privileged roles, exposed ports, missing encryption and unsafe base images, then generate hardened versions backed by policy-as-code rules.

Detection engineering

Draft Sigma rules, YARA signatures and SIEM queries from threat descriptions, validate them against test data and enrich documentation with the relevant MITRE ATT&CK techniques.

Securing the supply chain

Evaluate SBOMs, prioritise vulnerable dependencies, analyse upgrade paths including breaking changes and add pipeline steps for signing, provenance attestation and dependency pinning. Each update pull request explains the risk.

Possible hands-on projects

Finding triage bot

A script that ingests SARIF output from the pipeline, deduplicates findings, enriches them with code context and opens prioritised tickets with a suggested fix.

Secret scanning hook

A pre-commit hook with custom patterns for internal token formats, allowlist management and clear guidance on how to rotate a leaked secret.

Threat model generator

A small web app that turns a system description into a structured threat model with data flows, risks and mitigations, exportable as a document.

Prerequisites

You have hands-on programming experience, are comfortable with Git and the command line, and know pull-request-based workflows. You have hands-on experience in software development or operations and a working knowledge of application security, such as the OWASP Top 10 and CI/CD pipelines.

Technical requirements

Your own laptop with a terminal, Git and a Node.js runtime; a ChatGPT plan with Codex access or an OpenAI API key, plus a GitHub account for the cloud exercises.

Data protection & compliance

Source code, vulnerability data and logs are highly sensitive: you learn which data may be shared with AI services, how to keep secrets out of the context and how AI use aligns with NIS2, the Cyber Resilience Act, the GDPR and the EU AI Act.

Suggested dates

All dates are suggestions – your preferred date is always possible.

  • Thu, 26 November 2026 – Fri, 27 November 2026 Online Request
  • Mon, 7 December 2026 – Tue, 8 December 2026 Stuttgart Request
  • Thu, 21 January 2027 – Fri, 22 January 2027 Online Request
  • Thu, 4 February 2027 – Fri, 5 February 2027 Berlin Request
  • Thu, 18 February 2027 – Fri, 19 February 2027 Online Request
  • Thu, 4 March 2027 – Fri, 5 March 2027 Munich Request

Frequently asked questions

When should I use Codex locally and when in the cloud?

Locally you work interactively and close to the code, for example when debugging or making exploratory changes where you want to follow every step. The cloud agent suits well-defined tasks that can run independently, such as adding tests or fixing smaller bugs. You can start several of these in parallel and review each one as a pull request.

What belongs in a good AGENTS.md?

A good AGENTS.md explains how the project is built, tested and checked, which conventions apply and which areas must not be touched. It is concise, concrete and kept up to date. Notes on architecture and common pitfalls also help. During the course you write an AGENTS.md for a project of your own and test how it changes the agent’s behaviour.

How safe is it to let Codex run commands?

By default Codex works inside a sandbox with restricted file and network access and asks for approval before risky steps. You decide how much autonomy to grant through approval modes and configuration. In the course you learn to choose these settings deliberately and to limit risks such as prompt injection or running unfamiliar dependencies.

Is AI-generated code a security risk?

AI assistants sometimes reproduce insecure patterns, outdated libraries or even non-existent package names. The risk is manageable when generated code passes the same gates as hand-written code: peer review, SAST, dependency scanning and tests. In the course you set up exactly these guardrails while using AI yourself to find and fix vulnerabilities faster.

Should an AI agent be allowed to apply security fixes on its own?

We recommend a tiered model: the agent analyses, proposes fixes and opens pull requests, while humans approve the merge. For low-risk routine work such as dependency updates with passing tests, more automation can make sense. You learn to design permissions, sandboxing and audit trails so every decision remains traceable.

How much does “OpenAI Codex for Security and DevSecOps Teams” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 2 training day(s) with a live trainer, course materials, a lab environment and a certificate of attendance.

Can I suggest my own date?

Yes. Our suggested dates are just a guide. In the request form you can always describe your preferred date or period in free text – we check availability and confirm within one working day.

Is the course online or on-site?

Both are possible: live online in a virtual classroom or on-site in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart or Kaiserslautern. We can also run the course exclusively for your team.

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