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Training & Security Leadership
AI Assistants Accelerate Coding But Can Create Huge Risks for the Inexperienced
There’s a new approach to app development on the rise, and it sounds almost magical. You describe what you want in natural language such as, “Build me a dashboard with a login form and dark mode,” and an artificial intelligence assistant writes the code. You test it, tweak it, ask for a few style changes, and before long, you’ve got a working prototype.
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Welcome to vibe coding.
The term, coined by Andrej Karpathy in early 2025, describes a new rhythm of development powered by AI copilots. It’s informal, improvisational and highly iterative: “I just see stuff, say stuff, run stuff and copy-paste stuff – and it mostly works,” Karpathy quipped on X. He’s not wrong. When used well, vibe coding can unlock astonishing productivity. It lowers the barrier to getting ideas off the ground and reduces boilerplate to background noise.
But here’s the problem: Too many newcomers are mistaking it for a replacement for a deep understanding of coding and software development principles.
What Is Vibe Coding, Technically?
At its core, vibe coding is a loop. You issue a natural language prompt, the AI outputs code, you run it, and then you respond – by modifying the prompt, fixing bugs, or asking for enhancements. While the workflow may seem more conversational and dynamic, it relies on the same critical skills and traditional development: reasoning, debugging and system-level thinking.
The tools vary – Claude, ChatGPT, Copilot, GPT Engineer and open-source agents such as Smol Developer – but the pattern is the same. Human intent in, AI-generated code out.
What AI Is Good At and What It’s Not
Vibe coding shines when you need something fast: a script to parse logs, a test harness, a UI mockup. It’s fantastic for solo developers who already know what “good” looks like. It can even help you explore unfamiliar APIs, saving hours of documentation trawling.
But the AI tool does not know your system. It doesn’t know your users, your edge cases or your security requirements. It absolutely does not know when it’s about to write something that looks fine but fails catastrophically. Large language models also have context windows or memory limitations that affect their understanding of environments and previous tasks.
If you don’t understand how your application works – how data flows through it, what assumptions it makes and how it handles errors – you’re not programming. You’re pasting from a black box.
Vibe Coding Without Foundation Is Fragile
AI-produced code can be fraught with errors, and using it within a production environment can introduce significant risks. Security misconfigurations, performance issues and architectural anti-patterns are common when developers rely solely on vibe coding. AI might give you a perfectly functional API endpoint, but it might also hard-code credentials, forget to sanitize input, or violate REST conventions entirely.
When something breaks in production, you need someone who understands the system, not just the surface of the code.
Learn Your Craft – Then Use the Tools
If you’re entering cybersecurity today, you’re stepping into a field that increasingly blurs the lines between defender and developer. AI-assisted coding – and vibe coding in particular – can feel like a shortcut to unlocking new capabilities. For security professionals who don’t come from a traditional software background, it may seem like a welcome “easy button” to write scripts, build tooling or automate tasks.
But here’s the truth: it only works if you already understand what the code is doing and what it might do if it fails.
In cybersecurity, your job isn’t just to make things function. It’s to make them secure. That requires foundational skills that AI cannot substitute:
- Network fluency. Know what normal traffic looks like, how packets move and what it means to handle input across trust boundaries.
- Security architecture. Understand concepts such as privilege escalation, authentication flows and secure session handling.
- Code hygiene. Spot hardcoded secrets, insecure dependencies or unsanitized inputs, even if the AI doesn’t warn you.
- Threat modeling. Recognize how a seemingly functional feature can become an attack surface.
Generative tools are helpful assistants. They can scaffold your ideas, accelerate boring tasks, and free you to focus on logic and outcomes. However, they won’t teach you why a solution is fragile, how a script could be abused, or what edge cases will become your next incident.
Use these tools to move faster but do not skip the fundamentals. Vibe coding may help you build. Security knowledge ensures what you build is defensible.
Where Vibe Coding Fits in Cybersecurity Workflows
That said, vibe coding is already finding real traction inside security teams and rightly so. Examples include:
- Red teams use it to prototype payloads, obfuscate scripts or generate social engineering lures quickly.
- Blue teams automate log parsing, write detection rules and generate Sigma or YARA templates on demand.
- Threat analysts craft enrichment tools or connectors to APIs such as Shodan, VirusTotal and MISP without deep software engineering backgrounds.
- GRC teams even use it to draft policy documents or translate frameworks into checklist tools.
This isn’t theoretical. It’s happening in real-world SOCs, MSSPs and research environments. When wielded by someone who knows their environment, threat landscape and operational constraints, vibe coding becomes a serious force multiplier.
Use With Caution
For every security win enabled by AI-generated code, there’s a risk: overreliance without oversight, poorly written automations, or tools that leak more than they log. The most effective cybersecurity professionals will be those who use AI as an extension of their expertise, not as a substitute for it.
Vibe coding changes how we interact with code, but it doesn’t eliminate the need to understand what that code does, how it fits into the broader system or how it can be misused.
Whether you’re a developer exploring security or a security analyst trying to build your own tools, AI can help. However, it can’t think critically, evaluate trade-offs, or anticipate threat actors. That’s your role.
Don’t settle for being someone who runs code. Become someone who understands it deeply enough to know when it’s helping, when it’s hiding risk, and when to step in and fix it yourself.
In cybersecurity, skill isn’t optional. It’s what keeps tools from becoming threats.
Tools evolve. Craft endures.