Privacy and Security: Auditing AI-Generated Code for Compliance Risks

Quick answer: Review privacy and security requirements alongside code, deployed configuration, and application behavior. SimplyScan’s MCP server exposes supported checks from an editor workflow; its findings can inform investigation, but they do not certify GDPR compliance or replace an assessment of actual data processing.

By Gabriel CA · Kraftwire Software

Review privacy and security requirements alongside functional behavior, regardless of how the code was generated. Inspect the data collected, access permissions, deployed configuration, and application-specific tests.

These live figures describe all scanned sites. They do not establish prevalence for an AI-built subgroup, compare development methods, or attribute findings to AI generation.

30% of the 119 live sites SimplyScan has scanned (36 sites) have at least one high or critical security issue.

Application security needs independent review, regardless of how the application was created. Use automated checks alongside manual review and an assessment of the standards that apply to the application.

Why Is Privacy Security Critical For AI-Built Apps?

Review both application access controls and data-handling practices. For PostgreSQL roles subject to RLS, enabled RLS without applicable policies denies row access; permissive policies can expose rows where privileges permit it. These technical states alone do not establish privacy compliance.

A compliance assessment depends on actual data processing and applicable requirements. A missing header or scanner signal alone does not establish a legal violation or predict a fine. Review technical findings as evidence within the broader assessment.

How Do You Audit AI-Generated Code For Compliance?

Auditing AI-generated code requires a multi-layered approach that looks at both the code itself and the environment where it runs. Review generated code and deployment configuration independently of whether the visible features work.

1. Check for Exposed Secrets

Keep privileged credentials server-side and inspect client bundles, repositories, logs, and responses. Use supported secret checks alongside review of credential scope and access.

2. Verify Database Authorization

Inspect privileges, applicable policies, and bypass roles in the actual backend. Test allowed and denied access with controlled accounts and resources; see RLS policies.

3. Review Technical Signals in Context

A security.txt file supplies security-contact information; it is not proof of privacy compliance. Review SPF authorization, DKIM signing, and DMARC alignment and policy separately from data-processing obligations.

25% of the 119 live sites SimplyScan has scanned (30 sites) have at least one high-severity compliance issue, such as no linked privacy policy.

The compliance category reports supported technical signals, not a legal-compliance verdict or a measure of AI-builder behavior.

Can You Automate Privacy Audits During Development?

Automated checks can supply repeatable observations during development. Use them alongside code review and tests; they do not necessarily detect a privacy leak as it is written.

The SimplyScan MCP server exposes supported scan and tool operations to clients such as Cursor and Windsurf. Its deployed-URL scan grades eight dimensions, including security, speed, and GDPR/compliance signals, in about 30 seconds. Connecting the server does not automatically run every check or verify generated fixes. See the MCP setup guide.

Which Security Risks In AI Apps Should You Check?

Review the actual findings within each category.

Speed Observations

79% of the 119 live sites SimplyScan has scanned (94 sites) have at least one medium-severity speed issue, such as render-blocking scripts or no route-based code splitting.

Measure the affected resources and investigate their performance independently.

Architecture Observations

11% of the 119 live sites SimplyScan has scanned (13 sites) have at least one medium-severity architecture issue, such as a library with a known vulnerability or no rate limiting.

Review JWT handling and CORS independently; the architecture figure does not establish their prevalence.

Security Observations

Inspect headers such as X-Frame-Options and Content-Security-Policy where relevant, without treating this aggregate category as a measure of those specific defects.

29% of the 119 live sites SimplyScan has scanned (35 sites) have at least one medium-severity web-security issue, such as a missing Content-Security-Policy or HSTS header.

Use a security checklist alongside application-specific tests and review of coverage gaps.

How Does SimplyScan Help With Privacy And Security?

SimplyScan reports supported public findings, including detectable secret exposure, headers, and anonymously readable sensitive backend data. Its URL scan does not infer how the code was created or verify every policy and authenticated operation.

A free SimplyScan URL scan grades eight dimensions in about 30 seconds with no signup: security, speed, SEO, AI visibility, accessibility, GDPR/compliance signals, domain health, and email security. Pro Monitoring includes uptime monitoring and Slack, GitHub, and Linear integrations. Findings and alerts remain limited to supported observations.

Is Your AI-Built App Ready For Production?

Before launch, review privacy, security, and operational requirements against the actual application. Neither the development method nor a scan grade establishes release readiness.

The SimplyScan MCP server and security scanner can identify detectable technical risks. Their results do not certify compliance with applicable requirements.

Verify changes and document the findings, tests, and remaining coverage limitations relevant to the application.

A verified security badge links to a dated scan’s score and eligibility state, subject to the badge criteria. It does not prove the absence of vulnerabilities.

Pro Monitoring costs $24/month.