Security Readiness for AI

Security Readiness for AI Platforms - Built-In from Day One

CodeWheel AI is a DBA of Servers Connect LLC. References to “we,” “our,” or “CodeWheel AI” throughout this policy refer to Servers Connect LLC doing business as CodeWheel AI, the legal entity responsible for delivering the services described here.

Security readiness isn’t something you bolt on later. After 15 years building production systems, we build AI platforms with security baked into every layer-from RAG pipelines to identity and billing. You work directly with our team, and I stay on the hardest programming so we always know how every layer behaves.

We’ve shipped 200+ AI products across fintech and enterprise SaaS. Every project gets penetration testing during development, not after launch. Finding a prompt injection vulnerability in production is expensive. We catch it before it hits users.

Our Security Readiness Process

Every project follows our security-first methodology. We don’t just run checklists-we build platforms that pass scrutiny.

During Development

Threat modeling tailored to your AI use case (RAG vs chatbots vs agents)
Code security reviews at each sprint milestone
AI security testing covering prompt injection, context poisoning, model extraction
Infrastructure security scanning (Vercel, AWS, Supabase, Cloudflare)

Technical Implementation

OWASP methodology plus AI-specific testing frameworks. We cover traditional web vulnerabilities and the weird LLM stuff. Vector database security (pgvector with proper access controls). API rate limiting that actually works. Input sanitization that doesn’t break your embedding pipeline.

Audit-Ready Artifacts

You get real deliverables: security architecture docs, penetration testing reports, and evidence matrices tailored to your auditors. Auditors get evidence; your team understands the security model.

AI Platform Security Specifics

AI platforms have unique attack surfaces. Traditional pen testing misses half the vulnerabilities.

RAG system security: vector DB access controls, context window injection prevention, source attribution validation.
Model protection: API auth, rate limiting per tenant, model extraction prevention.
Data pipeline security: secure ETL flows, data classification, audit logging.

Our penetration testing for AI platforms covers these attack vectors. Standard scanners do not.

Technology Stack & Compliance Alignment

We primarily build on Next.js, Vercel, Supabase, and AWS. Configured correctly, these stacks align with enterprise security requirements.

Vercel edge functions with strict env management
Supabase Row Level Security for multi-tenant data
Cloudflare WAF tuned for AI attack patterns
Next.js middleware for request validation + rate limiting
MCP server implementations with sandboxed tools

Monitoring includes security event logging, anomaly detection for model usage, and audit trails your compliance team can actually use.

Working with CodeWheel AI

Based in San Francisco. Direct access to our team. 15 years of production engineering. No agency overhead.

Security review process: 1-2 week assessment with prioritized fixes and technical reports.
Implementation timeline: most AI platform development projects run 8-16 weeks with security implemented during each sprint.
Ongoing security: optional retainer for continuous audits and incident response, or a documented transition to your internal team.

Ready to build an AI platform that passes security reviews on the first try? Contact us to discuss your security readiness requirements.

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Security Readiness for AI | CodeWheel AI