Imagine building software by simply describing it – no coding, just vibes.
That’s the idea behind vibe coding, an AI-driven software development approach where natural language prompts replace writing code. You express what you want, “create a secure login system with two-factor authentication,” and the AI takes it from there. Instead of focusing on syntax, loops, or APIs, your role shifts to guiding the output, evaluating its results, and iterating conversationally.
Coined by Andrej Karpathy in early 2025, vibe coding quickly gained momentum. Described as “giving in to the vibes and forget the code exists,” it reflects the core idea: AI writes the code, humans guide the direction. For IT and security leaders, the key question is whether vibe coding delivers strategic value or introduces risks the organization isn’t ready to manage.
Why Now for Vibe Coding?
Vibe coding has exploded in popularity, thanks to maturing language models and new platforms like Replit, Cursor, and Lovable that let anyone, from engineers to entrepreneurs, turn text into working code. The appeal lies in four key promises:
- Accessibility: Non-technical users can build software with no coding background.
- Speed: Prototypes emerge in hours, not weeks.
- Creativity: Conversational iteration invites experimentation.
- Democratization: Individuals and small teams can compete with tech giants.
But as hype builds, so do questions about risk, highlighted by recent hard lessons.
The Hype and the “Hard Lessons”
SaaS founder Jason Lemkin fell under the vibing spell, calling Replit “the most addictive app I’ve used since I was a kid.” In his words, “That moment when you click ‘Deploy’ and your creation goes live? Pure dopamine hit.”
But then things went sideways. Despite explicitly instructing Replit not to touch production, the AI deleted his entire database, fabricated test results, and claimed rollback wasn’t possible (spoiler: it was). His reflection? “The [AI] safety stuff is more visceral to me after a weekend of vibe hacking. I explicitly told it eleven times in ALL CAPS not to do this. I am a little worried about safety now.” This incident was a wake-up call. The promise of instant deployment is exciting, but it must be balanced with trust and control.
As one executive notes, “I don’t think it’s a bad thing (vibe coding) but we’re trying to deliver enterprise-grade software that holds up and works well. We still have to be heavily accountable and responsible for the code that we’re using and generating.”
How Vibe Coding Changes the Software Lifecycle
Vibe coding reshapes the entire software lifecycle. Instead of moving through distinct phases like design, build, test, and deploy, it blends them into a fluid, conversational loop. This requires new behaviors across teams:
- Architects must go beyond system design to create safe, reusable scaffolds that guide AI to prevent insecure builds.
- Developers shift from coders to reviewers and curators.
- QA teams must validate not just functionality but AI logic paths.
- Security and risk must assess generated code like third-party software.
The impact is organizational, not just technical. Vibe coding requires more collaboration, stronger oversight, and a shift from building in silos to shared responsibility across teams.
Adopting a Risk-Based Approach
As the vibe coding ecosystem matures, AI coding platforms are rolling out safeguards like dev/prod separation, backups/rollback, single sign-on, and SOC 2 reporting, yet audit logging is still not uniform across tools. But until these enterprise-grade controls become standard, organizations must proactively build their own guardrails to ensure AI-generated code remains secure, scalable and trustworthy.
This calls for a risk-based approach, one that adjusts oversight based on the likelihood and impact of potential risks. Not all use cases carry the same weight. Some are low-stakes and well-suited for experimentation, while others may introduce serious security, regulatory or operational risks. By focusing controls where they’re most needed, a risk-based approach helps protect critical systems while still enabling speed and innovation in safer contexts.
Putting Risk-Based Thinking Into Practice
To effectively manage the risks of vibe coding, teams need to ask targeted questions that reflect the unique challenges of AI-generated code. These questions help determine how much oversight is needed and whether vibe coding is appropriate for the task at hand. For example:
- What could go wrong? Could the AI generate insecure code, skip key validations, or introduce unstable logic?
- What’s the potential impact? Would failure lead to data exposure, system downtime, reputational damage or non-compliance with regulations?
- How likely is failure in this scenario? Consider the complexity of the task, how critical the system is and the team's familiarity with AI-generated output.
- Will issues be visible and caught in time? Is there proper testing, logging and monitoring to detect errors before deployment?
- Who’s accountable for the output? Is there a clear owner responsible for reviewing, validating and approving the AI-generated code?
- What safeguards are already in place? Are prompts tracked, changes logged and code reviews built into the process?
- Is the value worth the risk? Does the potential speed or innovation justify the level of uncertainty introduced?
Once these questions are considered, teams can categorize the use case by risk level and apply the appropriate controls, balancing innovation with responsibility.
Segmenting Use Cases by Risk Level
This framework helps teams assess when vibe coding is appropriate and when stricter controls are required. Categorizing use cases by risk level ensures that AI-generated code is deployed with the right level of review, traceability and accountability.
| Risk Level | Typical Use Cases | Recommended Approach |
|---|---|---|
|
✅ Green light |
Internal tools, non-production scripts, prototypes, dashboards, test data generators, documentation tools |
Safe to experiment. Use vibe coding to boost speed and creativity. Apply lightweight human review, track prompts, and log outputs. Avoid use of real customer data. |
|
⚠️ Caution Zone |
Data integrations, customer-facing UI components, business logic, third-party API connectors, internal admin tools |
Use with guardrails. Require human review of generated code, enforce version control and test in staging environments. Check for performance, stability and security gaps. |
|
⛔ Red Light |
Regulated workloads, financial systems, healthcare platforms, identity/authentication systems |
Restrict or prohibit. Favor manual development or deeply reviewed code. Require full traceability, audit logs and compliance with secure coding standards before deployment. |
Tip: Look for Hidden Risk Factors
Even simple apps can carry elevated risk if they involve personal information, sensitive business logic, integrate with third-party platforms and serve regulated industries (e.g. finance, healthcare, education).
Final Thoughts: Not Yet Prime Time, But Definitely Showtime
Vibe coding unlocks new ways of thinking for software development. However, it also shifts risk upstream. The speed of code generation doesn’t eliminate the need for review, control and accountability. In fact, it makes those even more important.
So, is vibe coding ready for the enterprise?
✔️ Yes: for prototyping, learning and low-risk applications.
❌ No: for mission-critical systems, unless paired with strong governance.
The vibes are real. But controls still matter.
