Overview
Before the coaching starts, we’ll have a scoping call to figure out where your pipeline stands today and what matters most for your team. This shapes what we focus on during the engagement.
I help you build security pipelines for your CI/CD infrastructure. Whether you’re on GitHub Workflows, Jenkins, or something else, we’ll wire automated security scanning and AI-based code review directly into your build process.
What we’ll build together
Security scan integration
We’ll integrate multiple layers of security scanning into your pipeline. SCA (Software Composition Analysis) catches known vulnerabilities in your third-party libraries before they reach production. SAST (Static Application Security Testing) analyzes your source code for security weaknesses before it even runs, the kind of issues that are cheap to fix now and expensive to fix after they ship.
For runtime testing, we’ll add DAST (Dynamic Application Security Testing) that exercises your running applications and APIs to find vulnerabilities that only surface during execution. The point is that all of this runs on every build, automatically. Not as something someone remembers to kick off before a release.
Integrating AI into your security pipeline
Every SAST vendor now has an AI feature, and there’s a growing category of standalone AI review tools that analyze pull requests, flag security issues, or suggest fixes. The market is moving fast, and your team is probably already experimenting with some of these. The question isn’t whether to use AI-based security review. It’s how to integrate it so it actually helps rather than adding another source of noise.
In practice, AI-powered review tools fall into a few categories: PR review bots that comment on pull requests with security observations, AI-enhanced static analysis that uses LLMs to reduce false positives and add contextual explanations to findings, and custom LLM-based review pipelines where you run code through a model as a dedicated step in your CI workflow. Each has different strengths and blind spots, and they overlap with traditional SAST in ways that aren’t always obvious.
What I help with is figuring out where AI review fits into the pipeline you’re building. Which category of tool covers gaps your existing SAST leaves open? Where does AI review duplicate what you already have? How do you wire it into your workflow so findings land in the same triage process as everything else instead of creating a parallel notification stream your team learns to ignore? And what are the limitations, because AI review tools can hallucinate findings, miss things that pattern-based scanners catch reliably, and struggle with project-specific context that a human reviewer grasps immediately. Getting value out of them means understanding what they’re good at and where you still need traditional tooling or human eyes.
Securing AI-assisted development
There’s a flip side to AI in the pipeline that’s easy to overlook: your developers are increasingly writing code with AI assistants. Whether they’re using coding copilots, chat-based code generation, or full agentic coding workflows, AI-generated code is entering your codebase, and it doesn’t always come in clean.
AI coding assistants can introduce subtle vulnerabilities: insecure defaults, hallucinated dependencies that don’t exist (or worse, that an attacker registers after the hallucination becomes common), outdated API usage patterns with known security issues, or logic that looks correct but misses edge cases a human developer would catch. The code passes casual review because it’s syntactically clean and often well-commented — it just happens to be insecure.
Your security pipeline needs to account for this. That means making sure your SAST and AI review steps catch the patterns that AI-generated code tends to get wrong, and that your team knows what to look for during code review when a commit comes from an AI-assisted workflow. If your organization uses agentic coding setups where AI agents have direct access to your repository and CI/CD system, the security considerations go deeper: these are autonomous agents operating in your development infrastructure, and the Agentic AI Security assessment covers that threat model in detail.
Approach: two options
Blueprint workshop
For teams new to DevSecOps, I run a hands-on workshop with a pre-configured training environment. Each participant gets their own cloud-based server with a complete CI/CD setup, so everyone can work independently without stepping on each other’s toes. I use a purpose-built training application with real security vulnerabilities baked in — your team can break things and learn from it without any production risk.
During the workshop, we walk through integrating security tools into GitHub Workflows step by step: Actions that run scans at the right pipeline stages, false positive handling, result interpretation. By the end, your team knows how to read scan output and decide what actually needs fixing versus what they can safely ignore.
Custom implementation
For teams ready to implement security directly in their production pipelines, we skip the training environment and work with your actual infrastructure. I look at your existing CI/CD setup (GitHub Workflows, Jenkins, whatever you’re running) and design security scans matched to your applications and stack.
We wire AI-powered review steps in alongside traditional scanners, set up false positive handling, and configure reporting that fits how your team actually works. The goal is a pipeline that catches real issues without becoming a bottleneck. Your team walks away with a fully functional security pipeline, traditional and AI layers, running against your real codebase from day one.
Tool arsenal
For the traditional pipeline layers (DAST, SCA, SAST) I work with open-source tools that do the job without locking you into a vendor. Your team can operate, customize, and extend them without worrying about license renewals.
For the AI layer, the approach depends on what fits your stack and budget. There are open-source options for LLM-based code review, commercial PR review bots, and AI-enhanced versions of established SAST tools. I help you evaluate which category makes sense for your situation and where a custom LLM-based review step in your CI pipeline might cover gaps that off-the-shelf tools miss.
Beyond tool selection, I build the glue: custom automation scripts and integrations that connect everything into your GitHub Workflows or CI system, so security scans, traditional and AI-powered, execute at the right stages and results are properly formatted and routed to your team through a single triage process.
Deliverables
By the end of the coaching, you’ll have a fully configured security pipeline in your CI/CD system with automated security scans running on every build: traditional SAST, SCA, and DAST alongside AI-powered review steps, each covering what the other misses.
You’ll also get an AI tool evaluation: which categories of AI security review tools fit your stack, where they overlap with your traditional tooling, and where the gaps are. Not a product comparison spreadsheet, but a practical call on what adds value in your pipeline and what would just add noise.
I’ll set up false positive handling and result triage that unifies findings from all sources (traditional scanners and AI review tools) into one workflow your team can actually manage. You’ll get documentation and runbooks covering both the traditional and AI components, so your team can maintain and evolve the pipeline on their own as tools and models improve.
This service also supports technical security requirements commonly referenced in modern cybersecurity regulations.
Questions about this DevSecOps coaching? Let’s talk