Vibe Coding for Security Operations
Becoming an Agentic SOC
We all have that list. The mental inventory of things that should exist but don’t.
The hunt that would catch lateral movement. The automation that enriches alerts with threat intel in real time. The dashboard that finally makes sense without seventeen manual steps.
You can describe these ideas in five minutes. But they’re stuck in the graveyard of good intentions—buried in backlogs, deferred to “next quarter,” or killed in prioritization meetings.
We’ve been there. Every analyst has.
The problem isn’t that we lack good ideas. It’s that turning ideas into reality has been someone else’s job.
That’s changing.
The Old Reality
Building anything meant navigating the dev resource lottery:
Write up requirements, submit to the backlog, hope they surface in six months
Spend forty-five minutes justifying your idea, only to hear about technical debt and competing priorities
Your five-minute idea becomes a six-month project that delivers something adjacent to what you needed
Or the vendor route: request forms, three quotes, business case justification, procurement approval. By the time you got approval, the threat landscape had moved on. Meanwhile, attackers weren’t waiting for your procurement process. They were adapting, evolving, building new tools daily.
The mismatch was brutal. Analysts who understood the threat couldn’t build solutions. Developers who could build didn’t understand the threat. That gap wasn’t a minor inconvenience—it was a strategic vulnerability.
The Shift
Something fundamental changed in the last year, and most analysts haven’t noticed yet. We call it vibe coding. You describe what you want in plain language and iterate with AI until it works. This isn’t “learn to code” in the traditional sense. You’re not memorizing syntax or debugging memory leaks. This is “learn to BUILD.” There’s a difference.
Our aha moment came a few months ago. We were struggling with a complex investigation—correlating Sentinel data with external threat intelligence. The kind of analysis that normally meant exporting data, manipulating it in three different tools, and manually connecting the dots.
Instead of accepting the tedious workflow, we opened Vs Code Copilot and described what we needed: “Take these Sentinel query results, enrich them with threat intelligence from this API, and identify patterns that suggest coordinated activity.” Fifteen minutes later, we had working Python code that automated the entire process. Not perfect code. Not enterprise-ready code. But working code that solved our problem right now.
The barrier between idea and implementation had collapsed.
What makes this possible
Several technologies converged to create this opportunity:
Data lakes that make your security data accessible
MCP servers that connect AI to your actual environment
Multi-LLM approaches that let you use the right AI for each task
Tools like VS Code, GitHub Copilot, Claude, and MCP servers aren’t just developer productivity enhancers anymore. They’re force multipliers for anyone who can describe what they want clearly. We’ll walk through the technical setup next week. The infrastructure is ready. The tools are mature. The only thing missing is analysts who know how to use them.
Your first superpower
The most underrated tool in your security stack: Sentinel Notebooks. Most analysts treat notebooks like fancy query runners. Execute some KQL, maybe add a chart, done. That’s like using a Ferrari to commute at 25 mph.
With vibe coding, notebooks become your investigation superpower:
Correlate Sentinel data with external threat intel—describe the logic, let AI write the code
Apply machine learning to detect anomalies—explain the patterns, iterate until it works
Analyze attack chains across multiple data sources automatically
Generate investigation reports that actually tell a story
We’ve built notebooks that do all of this. Not because we became data scientists overnight, but because we learned to clearly describe what we wanted and let AI handle the implementation.
The notebook environment is perfect for this approach. Run code in chunks, see results immediately, iterate quickly. When something doesn’t work, paste the error back to your AI assistant and ask for a fix.
The Permission
Here’s what we wish someone had told us sooner: if you can write KQL, you can do this. You already understand data structures, logical operators, and filtering conditions. You know how to break down complex problems into smaller queries. You think in terms of inputs, transformations, and outputs.
That’s the hard part. The syntax is just details.
Building useful code is no longer a developer skill. It’s becoming a core analyst skill, like writing effective queries or understanding network protocols. And it opens doors:
Prompt engineering becomes natural when you’re used to iterating on queries
Agentic automation makes sense when you’ve built your own tools
Security engineering roles that seemed out of reach become accessible
More importantly, you stop being dependent on other people’s priorities. You stop explaining why your idea matters to people who don’t understand the threat. You just build it.
The Stakes
The adversaries figured this out before we did. They’re using AI to generate malware, automate reconnaissance, and adapt techniques in real time. They’re building and deploying at the speed of ideas. Meanwhile, we’re still debating whether AI can be trusted to help write detection rules.
The analysts who learn vibe coding in the next six months will 10x their impact. They’ll build tools that catch threats others miss, automate workflows that free up time for high-value analysis, and actually keep pace with evolving threats. The ones who don’t will keep waiting for someone else to build their solutions. Keep watching good ideas die in prioritization meetings.
The window to get ahead is open right now. The tools are ready, the techniques are proven, and most analysts haven’t realized what’s possible yet. That won’t last.
What this really means
This isn’t about replacing developers. Enterprise systems still need architecture, testing, and maintenance. This is about eliminating the gap between having an idea and testing whether it works. Moving from “wouldn’t it be nice if...” to “let me build a prototype and see.” The best analysts have always been builders—custom queries, hunting workflows, analysis methodologies. Now they can build actual software.
That changes everything.
Stay Tuned
Next, we’ll show you exactly how to set up your environment. VS Code, GitHub Copilot, Claude, and the MCP servers that make your AI actually useful for security work.
No theory. No philosophy. Just the step-by-step setup that turns your laptop into a security automation factory.
The graveyard of good ideas doesn’t have to stay full. It’s time to start building.