When to Get a Code Audit: Startup, Scale-Up, or Pre-Acquisition
The right moment for a code audit depends on stage. What to audit at seed, at scale-up, and before diligence. And the triggers that override stage.
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Knowledge base
Everything we know about auditing code: the same lens we apply to yours. 28 articles, newest first.
The right moment for a code audit depends on stage. What to audit at seed, at scale-up, and before diligence. And the triggers that override stage.
8 min read →
Code reviews inspect this week's diff; audits inspect the whole system. Why having one doesn't mean you have the other, and when you need both.
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Slipping estimates, one irreplaceable engineer, deploys everyone fears: seven concrete signs it's time for an independent look at your code.
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Unaudited code doesn't cost nothing: it costs velocity, incidents, hiring, and valuation. Here's where the money actually leaks out.
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A plain-English guide to code audits: what auditors actually examine, what they find, and when paying attention to your codebase pays off.
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Realistic timelines for a code audit, what makes them longer or shorter, and how to prepare your repo so the review starts fast.
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Scanners find pattern-shaped bugs; humans find logic-shaped ones. What each method actually catches, misses, and costs, and how to combine them.
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A practical pre-audit checklist: access, docs, and context that make an audit faster and deeper, and what not to waste time polishing.
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The anatomy of a useful audit report: severity rankings, evidence, business impact, and remediation order, plus the red flags of a bad one.
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From scoping and read-only access to the findings report: the actual mechanics of a professional code audit, stage by stage, minus the mystique.
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Most of your app is other people's code. How an audit triages CVEs by reachability, spots abandoned packages, and checks supply-chain hygiene.
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Where injection still hides in ORM-era codebases (raw queries, dynamic ORDER BY, NoSQL operators), plus the input-validation checklist we audit against.
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How an audit reviews authn and authz: session handling, token pitfalls, object-level checks, and the tenant-isolation bugs that hide in plain sight.
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The five ways API keys and credentials end up in git, why deleting the file doesn't help, and how an audit hunts them down.
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The ten vulnerability classes that show up again and again in real code audits, why scanners miss half of them, and what fixes look like.
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What auditors actually look for in documentation and maintainability, and why a missing README costs more than a missing test suite.
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Why the coverage percentage is the least interesting number in your test suite, and how auditors judge whether tests would actually catch a bug.
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The recurring code smells auditors flag in real codebases (God objects, shotgun surgery, dead code) and which ones actually predict incidents.
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Auditors don't grade style. The signals that actually matter: boundaries, consistency, error handling, and code that reveals intent fast.
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AI tools ship working demos fast. Here are the failure modes we keep finding when we audit Lovable, Bolt, Cursor, and v0 codebases.
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An audit report with 60 findings is useless without an order. Here's a practical framework for deciding what to fix first, later, and never.
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Technical debt is a metaphor until someone puts numbers on it. Here's how a code audit turns vague dread into a prioritized, costed list.
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Most scaling failures are query problems, not database problems. How to audit your queries, indexes, and access patterns before growth does.
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Slow apps rarely have one big problem. Here are the bottlenecks a code audit finds: N+1 queries, bundle bloat, sync-blocking work, and more.
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What SonarQube catches, what static analysis misses, and how to pair an automated scanner with a manual code audit for real coverage.
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A practical self-audit checklist (secrets, auth, dependencies, tests, error handling, performance) you can run on your own codebase this week.
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Not all code audits are equal: some are a human reading your code, some are a scanner with a logo. How to tell the difference before you pay.
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What insecure code actually costs: breach, technical debt, dependency, and AI-generated code statistics from IBM, Verizon, OWASP, and other named sources.
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