Vibe Coding Cleanup: How to Decide What Needs Fixing Before Your AI-Built Software Goes Live

The demo worked. The stakeholders liked it. The prototype was built in days instead of months. Now someone has to decide whether it is actually ready to go live, and that is the question most teams are not prepared to answer.

Vibe coding, building software by describing what you want in plain language and letting AI tools like Cursor, Claude Code, Lovable, Replit, or Bolt generate the implementation, has fundamentally changed how quickly software can go from idea to working prototype. What it has not changed is what production software actually requires: security, stability, maintainability, test coverage, and the ability to handle real users, real data, and real edge cases without breaking.

The gap between a working prototype and a production-ready application is where vibe coding cleanup lives. It is not a single task or a quick pass of automated tooling. It is a structured engineering assessment that determines what in an AI-generated codebase is structurally sound, what can be refactored, and what needs to be rebuilt before any of it is exposed to real users or integrated into live enterprise systems.

The data on what that gap looks like is no longer theoretical. Veracode tested over 100 large language models on security-sensitive coding tasks and found that 45 percent of AI-generated code samples introduce OWASP Top 10 vulnerabilities, a rate that has not improved across multiple testing cycles from 2025 through early 2026 despite vendor claims to the contrary. A large-scale scan conducted by Escape.tech of 5,600 publicly deployed vibe-coded applications found 2,000 highly critical vulnerabilities, 400 exposed secrets including API keys and access tokens, and 175 instances of personally identifiable information including medical records and payment data. Georgia Tech's Vibe Security Radar tracked 35 new CVEs directly attributable to AI-generated code in March 2026 alone, up from six in January.

These were not test environments. They were production applications with real users.

If your organization is working through this assessment now, or if you have AI-generated code in your development pipeline and need an engineering team to evaluate it, Marka's engineers work with enterprises across healthcare, manufacturing, finance, and public administration on exactly this kind of code quality and production readiness work. Reach the team at marka-development.com/contacts.

What Vibe-Coded Software Actually Looks Like Under the Hood

Understanding why AI-generated code requires cleanup is the prerequisite for knowing what to look for. The failure modes are consistent and specific, not random.

AI coding tools optimize for working code in the context of the prompt they received. They do not optimize for the operational context the code will live in, for security requirements the prompt did not specify, for the edge cases that will occur when the hundredth user tries something the demo never tested, or for the maintainability needs of the developer who will modify the code six months later. The result is code that passes every test visible to the person who built it and fails in ways that are entirely predictable to an experienced engineer who has seen the pattern before.

The architecture problem. Because AI models have no persistent memory of a project's architectural decisions across sessions, they generate code that solves each prompt in isolation. Functions grow too large and do too many things. Responsibilities that should be separated across distinct modules end up tangled together. The same logic appears in multiple places because the model did not know it had already been written. The result is what practitioners describe as spaghetti code: a codebase where changing one thing reliably breaks another, where no single module has a clear purpose, and where adding a new feature requires understanding the entire system because the dependencies go everywhere.

Refactoring dropped from 25 percent of code changes in 2021 to under 10 percent by 2024 as AI adoption surged, according to Iterasec's 2026 research. When design thinking is replaced with prompt-driven acceleration, architectural coherence is the first casualty.

The security problem. When you ask an AI to build a login page, you probably do not think to explicitly request input validation, password hashing, rate limiting, protection against SQL injection, or proper session management. The AI does not volunteer these things without being specifically prompted. It builds what you asked for: a login page that works. The security controls that production software requires are outside the scope of the prompt and therefore outside the scope of the output.

The specific vulnerability classes that appear most consistently in AI-generated code are hardcoded credentials and API keys embedded in source files rather than environment variables, SQL injection and command injection vulnerabilities from AI models following input patterns without applying sanitization logic, broken access controls in CRUD applications where the model generated the happy path without implementing authorization checks, and hallucinated dependencies where the AI references packages that do not exist or installs packages with names similar to legitimate ones that are actually malicious.

The data model problem. AI tools often generate database schemas that work for the data they were shown during the prompt but do not hold up under real workloads. Denormalizations that make sense for a demo become performance liabilities at scale. Missing indexes make queries that ran instantly on 100 rows unacceptably slow on 100,000. Foreign key constraints that were omitted because they were not mentioned in the prompt create data integrity failures when the application runs in production and users do things the demo never anticipated.

The test coverage problem. AI-generated code frequently ships with no tests, or with tests the AI generated alongside the code that test only the happy path the AI itself implemented. Negative scenarios, edge cases, error states, and integration behavior under load are almost never covered. The result is a codebase where engineers cannot safely change anything because there is no test suite to tell them whether the change broke something.

The First Decision: Audit Before Anything Else

The most expensive mistake in vibe coding cleanup is starting remediation work before completing the audit. Teams that begin refactoring immediately, fixing the most visible problems first, consistently discover that the problems they fixed were symptoms of architectural issues they did not surface until later, and that the refactoring work needs to be partially redone once the deeper problems are understood.

A cleanup audit has a specific scope. It is not a general code review or a security penetration test, though it informs both. It is a structured assessment that produces three outputs: a map of what is structurally sound and can be shipped or retained with minor fixes, a map of what can be remediated through refactoring without structural change, and a map of what requires rebuilding because the existing implementation is incompatible with production requirements. The third category is the one that changes the economics of the project, and it is the one that needs to be identified before any cleanup work begins.

The audit runs across five domains. Security is first because security findings can change the entire go-live timeline. Architecture is second because architectural problems determine which parts of the cleanup are refactoring projects and which are rebuilds. Data model integrity is third because data model problems discovered after launch are significantly more expensive to remediate than before. Test coverage is fourth because the absence of tests affects every subsequent change to the codebase. Documentation and maintainability is fifth because undocumented AI-generated code creates knowledge concentration risk that affects every future development decision.

Running the audit in this order is not arbitrary. A security finding that requires architectural change to remediate, such as a broken access control pattern that is embedded throughout the codebase rather than in a single module, changes the scope of the architecture phase. A data model problem that requires a migration to fix changes the go-live timeline. Discovering these sequencing dependencies early is what keeps cleanup projects from expanding scope mid-engagement.

The Security Audit: What to Check First

Security is the non-negotiable starting point because it is the domain where vibe-coded software most consistently creates risks that are invisible to non-security-trained engineers and catastrophically visible in production after a breach.

Secret and credential scanning. The first pass of any vibe coding security audit is automated secret scanning across the entire codebase and its git history. Hardcoded API keys, database credentials, and authentication tokens appear in AI-generated code with documented regularity because the AI is optimizing for functional code and the developer did not think to specify environment variable handling in the prompt. The Moltbook breach, which exposed 1.5 million API authentication tokens within 72 hours of launch, is the most cited recent example of this failure mode. The fix is not complicated. The discovery has to happen before go-live.

Authentication and access control review. Every authentication and authorization flow in the codebase needs manual review by a security-trained engineer. Automated static analysis tools catch some access control vulnerabilities but miss business logic flaws: authentication flows that work but can be bypassed under specific conditions, authorization checks that are implemented at the wrong layer, session management that does not invalidate tokens correctly. These are the vulnerabilities that SAST tools miss and that attackers find through systematic testing.

Dependency audit. AI-generated code pulls in dependencies without evaluating their security posture, their maintenance status, or whether they are actually the package the AI intended to reference. A dependency audit covers three things: whether each dependency has known CVEs that require patching or replacement, whether each dependency is actively maintained, and whether any dependency names are suspicious in ways that suggest package confusion or typosquatting attacks.

Input validation coverage. Every point in the application where external input enters the system needs explicit validation. SQL injection, command injection, and cross-site scripting vulnerabilities all originate at input boundaries where the AI generated functional code without generating defensive code. A complete map of input boundaries and the validation logic applied at each one is the output of this check.

The Architecture Audit: Fix, Refactor, or Rebuild

The architecture audit produces the most consequential output of the entire cleanup process: the determination of which parts of the codebase can be cleaned up in place and which need to be rebuilt to meet production requirements.

The rebuild decision is not a failure. It is an engineering judgment that prevents a worse outcome. Rebuilding a module before launch is a controlled cost. Discovering that a module's architecture prevents the application from scaling, after users are depending on it, is an uncontrolled cost. The rebuild decision is worth making deliberately and early, not discovering under pressure.

Three questions determine the rebuild call for any given module or component, adapted from the framework that experienced cleanup engineers consistently apply.

Is the business logic clear, correct, and testable? Business logic that was generated through a sequence of prompts, where each prompt added behavior without a coherent design, frequently contains logic that works for the cases the developer tested and fails for cases that were never tested. If the business logic in a module cannot be clearly explained, fully tested, and confidently stated as correct, the module is a rebuild candidate. Refactoring code whose logic is not understood is engineering on a foundation that cannot be trusted.

Is the data model correct for production workloads? AI tools often generate data models that represent the data correctly but do not account for query patterns, write volumes, or relational integrity requirements at production scale. A data model that requires a migration to correct after users are depending on it carries significantly higher risk and cost than one corrected before launch. If the data model has denormalizations that do not reflect intentional design decisions, missing indexes on columns that will be queried at scale, or missing constraints that production data integrity requires, the data model needs to be corrected before go-live, and in many cases the code that depends on it needs to change as a consequence.

Is the code so entangled that a refactor would touch every line anyway? This is the most honest version of the rebuild question. If the architecture audit reveals that the module has no clear boundaries, that functions are doing multiple unrelated things, that there are circular dependencies that make any change risky, and that understanding the code requires reading all of it rather than the relevant part, the engineering cost of refactoring it to a clean state often exceeds the cost of rebuilding it correctly. When two of these three questions answer negatively for a given module, the rebuild is faster and cheaper than the refactor.

The Refactoring Scope: What Gets Fixed in Place

Refactoring in the context of vibe coding cleanup is not cosmetic. It is structural work that makes AI-generated code maintainable, testable, and safe to extend without breaking existing behavior.

The refactoring scope that emerges from the architecture audit typically covers the following categories.

Separation of concerns. Functions that do too many things get decomposed into smaller functions with single responsibilities. Business logic that ended up in UI components or API handlers gets moved into appropriate service or domain layers. Configuration that is hardcoded gets extracted into environment-aware configuration management. This work does not change what the application does. It changes how the application is organized so that future changes can be made safely and efficiently.

Dead code and duplication removal. AI-generated codebases consistently contain significant volumes of unused imports, dead code paths, and duplicated logic that the AI generated in separate sessions without knowledge of what was already written. Removing this reduces the cognitive overhead of working in the codebase and eliminates the maintenance burden of keeping dead code current with the rest of the system.

Error handling and resilience. AI-generated code frequently handles only the happy path. Network calls that can fail do not have retry logic or timeout handling. Database operations that can fail do not have appropriate error responses. External service integrations that can be unavailable do not have fallback behavior. Adding proper error handling throughout is cleanup work, not new feature work, and it is the category of work that most directly determines whether the application behaves gracefully under real conditions or fails in ways that users experience as outages.

Test coverage. Writing the test suite that the AI did not generate is a significant part of every cleanup engagement. The tests are not just a quality artifact. They are the safety net that makes every subsequent change to the codebase safe to make. A codebase without tests is a codebase where every change is a risk, and that risk compounds with every feature added after launch.

How This Connects to SDLC Governance

For enterprise teams, vibe coding cleanup is not a standalone project. It is the intersection of AI adoption and the software development lifecycle governance that enterprise software quality and compliance require.

Marka's SDLC audit work increasingly includes an explicit assessment of how AI-generated code is reviewed, attributed, and treated within the development process, because the SDLC audit frameworks that enterprise teams operate under were designed before AI tools generated a significant portion of the codebase. A quality gate that was designed to catch human-written code with known failure modes does not automatically catch the specific failure modes of AI-generated code, including hardcoded secrets, missing input validation, and hallucinated dependencies.

For organizations subject to NIS2, DORA, ISO 27001, or SOC 2, AI-generated code that ships without a cleanup audit is a compliance risk, not just a technical risk. The access control vulnerabilities, unpatched dependencies, and missing audit logging that appear in AI-generated code are exactly the control gaps that regulatory frameworks require organizations to address and evidence. A codebase that cannot be audited because it has no tests, no documentation, and no clear module boundaries is a codebase that cannot demonstrate compliance.

The practical integration of vibe coding cleanup into an enterprise SDLC is a policy and process question as much as a technical one. Which development workflows permit AI code generation? At what gate does AI-generated code receive security scanning and manual review before merging? Which categories of functionality, authentication, authorization, data storage, external integrations, require mandatory security-trained review regardless of how the code was generated? ISACA's 2026 framework study documented a 36 percent reduction in remediation time for organizations that implemented structured AI code governance controls, without meaningful reduction in developer velocity. The governance investment pays for itself in reduced cleanup cost downstream.

The Go-Live Decision Framework

Before any AI-built software goes live, the following questions need answered answers, documented with evidence, not estimates.

Has a secret and credential scan been run across the full codebase and git history, and have all findings been remediated? This is the non-negotiable minimum before any production deployment. The cost of a credential scan is hours. The cost of a credential breach is measured in regulatory penalties, customer notification requirements, and reputational damage that does not have a ceiling.

Has every authentication and authorization flow been reviewed by a security-trained engineer? Automated scanning does not substitute for this. Business logic vulnerabilities in authentication flows require a human who understands both the attack patterns and the application's intended behavior to evaluate correctly.

Has the data model been validated against production query and write patterns? Running a load test against a staging environment that uses production-scale data is the minimum standard. A data model that performed acceptably under demo conditions and fails under production load is a production incident waiting to happen.

Is there a test suite that covers the critical paths and can be run against every subsequent change? If the answer is no, every change after go-live is a risk with no safety net. The test suite does not need to cover every line of code before launch. It needs to cover every path that a production failure would trace through.

Has the codebase been reviewed against the compliance requirements that apply to your organization? For regulated industries, this question is not optional. If the answer is no, the go-live decision is a compliance decision that needs to involve the compliance function, not just the engineering team.

What to Do Next

The most important timing point in vibe coding cleanup is that it is significantly cheaper before launch than after. An audit that discovers a rebuild requirement before go-live means a controlled timeline. The same discovery after go-live, under user pressure and with live data to migrate, means an uncontrolled timeline with significantly higher risk and cost.

Three actions are worth taking before your next AI-built software goes into production.

Run the security scan now, before anything else. Secret scanning and dependency auditing are automated, fast, and inexpensive. They are the findings that have the most immediate and severe consequences if discovered by someone else first. Every AI-generated codebase should have these run before any other cleanup decision is made.

Assess the architecture against the three rebuild questions for each major module. The rebuild determination is the output that most affects the cleanup project timeline and budget. Getting it right before starting remediation work prevents scope expansion mid-project.

Map your compliance requirements against the codebase's current state. If your organization is subject to NIS2, DORA, ISO 27001, or sector-specific regulations, the compliance gap analysis tells you which cleanup items are required by your regulatory framework and which are quality improvements. That distinction matters for prioritization and for the business case.

Marka's engineering team delivers this assessment and the remediation work that follows for enterprise clients across healthcare, manufacturing, finance, and public administration. For organizations on the Microsoft stack, the Cloud and Platform Modernization practice includes AI code governance as part of every modernization engagement. You can reach the team directly.