ISMS Copilot
Engineering

Software Engineering, Not Vibe Coding

ISMS Copilot is built using professional software engineering practices, not "vibe coding" tools like Lovable or similar AI-driven no-code platforms. While AI-assisted development has a role in our workflow, we rely on structured processes, rigorous testing, and production-grade infrastructure to ensure security, reliability, and scalability for compliance-critical workloads.

This article addresses questions about our development methodology and explains why vibe coding isn't suitable for production compliance software.

What Is Vibe Coding?

Vibe coding refers to AI-powered no-code/low-code platforms like Lovable that let users build apps through natural language prompts and visual editors. These tools prioritize speed and ease of use, enabling non-engineers to prototype quickly by describing what they want rather than writing code.

While valuable for rapid prototyping and simple apps, vibe coding tools have critical limitations:

  • Frontend-focused: Most generate React/TypeScript UI code but lack sophisticated backend architecture

  • Limited control: Developers can't enforce comprehensive security scanning, custom CI/CD pipelines, or environment separation

  • Testing gaps: Unit tests, regression tests, and security scans are often minimal or absent

  • Production readiness: Instant deployment bypasses critical pre-production validation needed for compliance software

Vibe coding is a trap for production applications. The speed advantage disappears when you need to refactor, secure, test, and maintain complex systems handling sensitive data.

How ISMS Copilot Is Built

We use a disciplined software development lifecycle (SDLC) with environment separation, automated testing, and security scanning at every stage. Here's how our process differs:

Branch-Based Development

Every change starts on a feature branch. Engineers never commit directly to staging or production. This ensures:

  • Code review before merge

  • Isolated testing of changes

  • Rollback capability if issues arise

  • Traceable change history via pull requests

Environment Separation

We maintain distinct environments with identical configurations:

  • Development branches: Local and isolated feature testing

  • Staging: Pre-production environment mirroring production infrastructure (same database schema, services, and security policies)

  • Production: Live environment serving users, deployed only after staging validation

Staging is as close to production as possible. We test database migrations, API changes, and third-party integrations here before any production deployment.

CI/CD Pipeline

Our continuous integration and deployment pipeline runs automated checks on every pull request and deployment:

  • Unit tests: Vitest-based tests validate UI components and business logic

  • Security scanning: Static analysis (SAST) with Semgrep detects vulnerabilities before merge

  • Regression testing: Automated tests ensure new changes don't break existing functionality

  • 100% pass requirement: Deployments fail and roll back if any test fails

GitHub Actions orchestrates these workflows, enforcing quality gates that vibe coding platforms can't provide.

Change Planning and Impact Analysis

Before implementing features, we analyze:

  • Backend impact: How will database schema, API contracts, or third-party integrations change?

  • Security implications: Does this introduce new attack surfaces or data exposure risks?

  • Performance: Will this affect query times, LLM response latency, or user experience?

  • Compliance alignment: Does this maintain GDPR, SOC 2, and ISO 27001 readiness?

This structured planning prevents the "move fast and break things" mentality that vibe coding encourages.

For compliance software handling audit-critical data, structured planning isn't overhead—it's risk mitigation.

Security and Testing Practices

Our commitment to security goes beyond what AI-generated code provides:

  • Annual penetration testing: Third-party experts audit for vulnerabilities

  • Dynamic Application Security Testing (DAST): Runtime vulnerability scanning

  • Prompt injection testing: AI-specific security tests for adversarial inputs

  • Regression test suites: Validate AI outputs, framework detection, and policy generation accuracy

  • Monitoring: Track hallucination rates, response accuracy, and system performance

These practices are documented in our AI System Technical Overview and align with our path to ISO 27001 certification (see Why we're not ISO 27001 certified yet).

AI-Assisted, Not AI-Generated

We do use AI in development—but as a tool, not a replacement for engineering discipline:

  • Code assistance: AI helps write boilerplate, suggest refactorings, and generate test cases

  • Human verification: Every AI suggestion is reviewed, tested, and validated by engineers

  • Structured prompts: We use AI within controlled workflows, not freeform "vibe" prompts

The difference: AI accelerates development, but humans enforce architecture, security, and quality standards.

AI-assisted engineering combines speed with rigor. Vibe coding sacrifices rigor for speed.

Why This Matters for Compliance Software

ISMS Copilot handles sensitive data for ISO 27001, SOC 2, GDPR, and other high-stakes frameworks. Users trust us with:

  • Proprietary policies and security documentation

  • Risk assessments and audit evidence

  • Client-specific compliance data in Workspaces

Vibe coding's rapid iteration model conflicts with the stability, auditability, and security compliance professionals require. Our engineering approach ensures:

  • Predictable releases: Staged rollouts with tested changes

  • Audit trails: Version-controlled code, documented deployments, traceable changes

  • Security guarantees: MFA, row-level security, end-to-end encryption, no training on user data

  • Reliability: Comprehensive testing prevents regressions that could corrupt audit-ready outputs

Was this helpful?