You built fast with AI. The code works. Now you need to know if it's secure. Tekk.coach reads your actual codebase — not sampled files, not isolated functions, the whole thing — and runs a security review that finds what coding agents missed: missing input validation, broken auth patterns, hardcoded credentials, exposed endpoints, and more. You get specific findings with file locations, not a generic checklist.
How Tekk.coach Does an AI Security Audit
Traditional security tools scan files in isolation. They find a suspicious pattern in users.ts but don't know how the data flowing into that file arrived there, or what happens to it afterward. That's how SQL injection vulnerabilities hide across three files while SAST tools miss them — the problem is in the interaction, not the individual file.
Tekk reads your entire codebase before starting the review. Semantic search, file search, regex search, directory browsing, repository profiling — Tekk understands your stack, your frameworks, your service boundaries, and your data flows before it generates a single finding. When it flags an issue, it's grounded in how your specific code actually works.
Security reviews in Tekk are triggered through review mode — the same workflow used for architecture reviews, performance reviews, and agent improvement reviews. You request a security review, Tekk reads the codebase and searches for current best practices on your specific stack, then produces a structured list of findings with file references and actionable recommendations. The output lands in the living document editor — editable, shareable, and tied to the codebase context that generated it.
What you get isn't "possible injection vulnerability detected in users.ts." It's "your POST /api/users endpoint at src/routes/users.ts:47 passes req.body.email directly to the Drizzle ORM query without parameterization — here's the fix." That's the difference between a finding you can act on and a finding you have to investigate.
Key Benefits
Codebase-wide data flow analysis Tekk traces data from HTTP request handlers through validation layers, transformation logic, and database interactions. Vulnerabilities that span multiple files — the ones pattern-matching tools miss — are visible when you read the whole codebase.
Current best practices, not training data During a security review, Tekk searches the web for current best practices on your specific stack. Your Next.js auth implementation gets checked against current JWT security guidance, not patterns from two years ago. Your Node.js API gets reviewed against current OWASP guidelines.
Findings with file references and specific fixes
Every finding includes the file path, relevant code, and a specific recommendation. Not "consider validating inputs" but "validate and sanitize the email field at src/lib/auth.ts:23 before passing it to the magic link generator."
The senior security engineer you don't have A full-time security engineer costs $200k+. A professional security audit costs $5,000–$50,000 and gives you a point-in-time snapshot. Tekk gives you an expert review grounded in your actual codebase, on demand, for the specific areas you're worried about.
Especially valuable for AI-generated code Coding agents optimize for functionality — they write code that works. Security is a separate concern that requires threat modeling, not just implementation. A security review catches what the coding agent didn't optimize for.
How It Works
Step 1: Connect your repository Connect GitHub, GitLab, or Bitbucket. Tekk runs a full repository profile — identifying languages, frameworks, services, packages, and architectural patterns — before touching any review.
Step 2: Request a security review In the Tekk interface, request a security review. You can scope it: "security review the authentication module" or "review the entire API layer for vulnerabilities" or "check our payment integration for security issues."
Step 3: Tekk reads the codebase Before generating a single finding, Tekk searches your codebase using semantic search, file search, and regex lookups. It builds a picture of how your application handles data — where it enters, how it's validated, how it's stored, how it's returned.
Step 4: Tekk searches current best practices Tekk searches the web for current security best practices relevant to your stack. Your specific framework, ORM, auth library, and deployment pattern inform the review — not generic guidance.
Step 5: Findings land in the living document editor The security review output is a structured document in the task editor. Findings include file references, code snippets, severity context, and specific fixes. You can edit, annotate, and share the document. It's the artifact you work from to address issues.
Step 6: Fix, re-review, iterate After addressing findings, request a follow-up review on the affected areas. Tekk reads the updated code and confirms fixes. The living document reflects what's been resolved.
Who This Is For
Vibe coders who shipped fast with AI You used Cursor or Claude Code to build quickly. The product works. But you know "works" and "secure" are different requirements. A security review before you expose this to users — especially users with real data — is the responsible next step.
Solo founders pre-launch or pre-funding You're about to put this in front of real users, or in front of investors doing technical due diligence. You need to know what's in your codebase. You can't afford a $20,000 security audit. You need expert findings grounded in your specific code.
Small teams without a security engineer Security engineering is a specialization. Your 5-person team doesn't have one. You have a smart developer who reads security articles. That's not the same as a dedicated security review of your actual codebase. Tekk fills the gap.
Developers adding new attack surface You just integrated a payments API, an AI agent layer, a webhook system, or a third-party auth provider. New integrations bring new attack surface. A targeted security review of the new integration catches issues before they're in production.
What Is an AI Security Audit for Code?
An AI security audit for code uses artificial intelligence to analyze a software codebase for security vulnerabilities — going beyond pattern-matching to reason about code behavior, data flows, and architectural risk. Unlike traditional Static Application Security Testing (SAST) tools, which check code against rule libraries and known patterns, AI-powered review can understand context: how a function is actually called, where data actually flows, and whether the combination of individually-reasonable choices creates a security problem.
The category emerged as AI capabilities matured enough to reason about code semantically. Snyk's documentation describes the shift from "lengthy scans producing false positives" to "real-time semantic code analysis delivering actionable suggestions." The practical difference: a SAST tool reports "possible injection in users.ts"; an AI security review explains exactly how the injection occurs and provides the specific fix.
The urgency increased with the rise of AI-generated code. Coding agents like Cursor and Claude Code produce functional code at high speed — but they optimize for functionality, not security. Code that passes tests and works in development may be wide open to injection attacks, have hardcoded credentials, or skip input validation that an experienced security engineer would have added reflexively. AI security review is the check that catches what AI generation missed.
Ready to Try Tekk.coach?
Your AI-generated code probably works. The question is whether it's secure. Tekk reads your codebase, finds what your coding agents missed, and gives you specific findings with file references and fixes — not a generic checklist. Connect your repo and request a security review today.