Founders, product leads, and engineering managers who lean on vibe coding or quick AI coding helpers to speed delivery often hit a wall when the security review starts. The pain is familiar: missing input validation, stale dependencies, weak secrets handling, and no traceable threat model. At Bill Vivino Technology we blend AI productivity with disciplined secure engineering so you can ship fast and still pass audits on the first attempt.
The hook: AI helps you ship features, not security
Tools that promise to write the app for you can be amazing for prototypes. The problem appears during audit week. Most AI-generated snippets are optimized for functional correctness and speed, not for the review questions your auditor will actually ask. That creates audit gaps that stall releases and trigger costly rework.
Common failure patterns we see in audits
- Dependency risk - AI samples often pin old libraries or pull in transitive packages with known CVEs and no SBOM.
- Auth and session flaws - Incomplete token validation, missing rotation, or non-scoped tokens that break least privilege.
- Input and output handling - Naive sanitation that fails on edge cases, creating injection paths across API, ORM, and template layers.
- Secrets management - Credentials in source, logs, or CI variables without vaulting, rotation, or environment-scoped access.
- Logging without controls - Helpful for debugging, harmful for compliance when PII is written without redaction or retention rules.
- Zero traceability - No threat model, no architecture decision records, and no evidence that controls map to a standard.
Why vibe coding increases risk
Vibe coding trades process for momentum. You can definitely get a demo live fast with it. But without guardrails your AI assistant cannot guarantee alignment with frameworks like OWASP ASVS or NIST SSDF. When auditors ask Why does this control exist and where is it verified, a code dump is not enough.
Solution: AI with a security system behind it
We pair AI coding with a repeatable secure SDLC that passes enterprise and startup audits in the NYC metro market. Our approach:
- Design first - Lightweight threat modeling and data-flow diagrams before code generation.
- Policy-as-code - Repo templates with mandatory checks, secrets scanning, IaC drift detection, and SBOM generation.
- Standards mapping - Controls mapped to OWASP ASVS with evidence artifacts.
- Continuous verification - SAST, DAST, dependency review, and container scan gates in CI with fail-closed rules.
- Human review - Senior engineers validate AI output and document risks, compensating controls, and exceptions.
Proof from the field
Teams come to us after internal audits or pen tests flag issues like hardcoded secrets or insecure auth flows. We harden the stack, write missing tests, and produce clean audit evidence. See examples in our portfolio, then contact us for specifics under NDA.
Checklist: ship AI-assisted code that passes audits
- Define a one-page threat model and data classification for the feature.
- Generate code with AI, but require PRs to reference controls and tests.
- Add SBOM, license scan, and dependency review to CI.
- Enforce secret scanning and vault integration for all environments.
- Run SAST and DAST for every merge to main and release branch.
- Log with redaction, trace IDs, and retention policy alignment.
- Document compensating controls and store evidence in the repo.
People also ask
Why AI-generated code usually fails security audits interview
Interviewers want a structured answer: lack of standards mapping, insufficient input validation, weak secrets handling, and missing evidence. Tie your response to controls and how to close gaps with CI policies and code review.
Do AI-generated code outages really happen
Yes. Outages occur when generated code hides fragile assumptions. Typical triggers include unbounded retries, unexpected input shapes, or race conditions created by naive async patterns. Proper testing and SRE guardrails reduce this risk.
Frequently Asked Questions
Can AI code a website that passes a security audit
AI can help produce components quickly, but passing an audit requires a process: secure design, vetted dependencies, tests, CI scan gates, and human review. We combine AI productivity with a hardened SDLC so your site clears review.
What standards should we map to for web apps
Most teams use OWASP ASVS for app controls and add NIST SSDF practices for development lifecycle. Map each control to code, tests, CI checks, and evidence so auditors can verify without guesswork.
How fast can we remediate AI coding risks
Typical remediation takes one to three sprints depending on scope. We prioritize dependency risk, secrets, and auth first, then round out logging, tests, and documentation so you can pass the next review.
Key Takeaways
AI coding boosts speed, but vibe coding without guardrails increases audit risk. Pair generation with secure design, standards mapping, CI gates, and senior review. The result is software that ships fast and passes the first time.