What to Ask a Static Analysis Vendor When Your Team Uses AI Code

Why Generic Static Analysis Falls Short on AI-Generated Code

Generic static analysis tools have a coverage gap specific to AI-generated code: BrassCoders’s controlled benchmark found that Bandit — the most widely deployed Python static analyzer — catches 6 of 12 AI-generated bug categories, the six it misses being the ones that emerged specifically from AI coding assistant patterns in the years after most rule sets were written.

This is a training data problem at the scanner level, not a tool quality problem. A well-maintained static analyzer built before AI coding assistants became widespread was trained on historical developer bug patterns. AI assistants generate code with different systematic errors — specific to how language models predict completions, not how developers make mistakes by hand. The scanner that catches a developer’s SQL injection variant may miss the variant an AI assistant generates because it doesn’t match any trained rule.

The practical question for any vendor evaluation: does the tool know what AI-generated bugs look like, and can it demonstrate coverage in controlled, reproducible testing?

Questions That Separate AI-Aware Tools From Legacy Tools

BrassCoders was built after AI coding assistants were already in wide use — its 12 scanners include six custom detectors designed specifically for patterns that appear in AI-generated Python and TypeScript code, including a phantom-API scanner that cross-references imports against the live PyPI index.

These are the questions to ask any vendor:

Coverage benchmark: “What percentage of AI-generated bug categories does your tool catch in controlled testing, and can you share the methodology?” BrassCoders publishes this at /blog/ai-coder-bug-benchmark/ — 11 of 12 AI-generated bug categories caught in controlled testing of 15 AI-generated Python files. If a vendor can’t produce an equivalent number with a reproducible corpus, their coverage claim is unverifiable.

Phantom import detection: “Does your tool detect imports of libraries that don’t exist on PyPI?” AI assistants hallucinate package names that look valid but fail at runtime with ImportError. This is one of the six categories Bandit misses; BrassCoders’s AI-pattern scanner catches it by cross-referencing the PyPI index.

Rules written after 2022: “Do you have rules specifically written for patterns that only appear in AI-generated code?” Tools with rule sets frozen before AI coding assistants were mainstream will have systematic blind spots in the categories those assistants produce most frequently.

Configuration overhead: “Does AI-specific coverage require ruleset tuning, or does it run by default?” BrassCoders requires zero configuration — pip install brasscoders && brasscoders scan . covers all 12 scanners with no setup.

JavaScript and TypeScript coverage: “Does the tool cover JavaScript and TypeScript, or only Python?” BrassCoders’s JavaScript/TypeScript scanner covers Node.js and browser-side code in the same scan as Python.

Data Handling Questions Every Vendor Should Answer

BrassCoders’s OSS core makes zero outbound network calls — all scanning happens locally against your source tree, and the results land in .brass/ on your machine. For Paid-plan users, BrassCoders sends already-redacted findings and a project signature of at most 7,500 characters of non-sensitive metadata to the enrichment gateway — never raw source code.

Ask every vendor these four questions before signing:

1. What data leaves the developer machine during a scan? The acceptable answers are “nothing” (OSS core model) or a specific, bounded description of exactly what’s sent and to whom. Vague answers (“anonymized telemetry,” “aggregated metrics”) are not adequate for codebases containing sensitive IP.

2. Is air-gapped operation supported? For regulated environments — HIPAA, SOC 2, FedRAMP — you need a scanner that works without outbound calls. BrassCoders supports this with the --offline flag, which adds hard enforcement: the scan exits non-zero if any network call is attempted at runtime.

3. Who can access stored findings? SaaS-first tools store findings on vendor infrastructure. Understand the data retention policy and whether findings are accessible to vendor staff or subprocessors.

4. Is the tool’s source code auditable? BrassCoders’s OSS core is Apache 2.0 licensed and available at github.com/CopperSunDev/brasscoders — you can read the scanner logic before running it against your codebase.

Evaluating the Answers

BrassCoders publishes its benchmark methodology and reproducible corpus — a controlled test of 15 AI-generated Python files, with findings verified against published CVE and OWASP references — so any evaluator can reproduce the results independently.

The standard for evaluating any vendor claim is reproducibility. “We catch 95% of bugs” is marketing. “We caught 11 of 12 AI-generated bug categories in this specific test against these 15 files, here’s the corpus” is a verifiable claim. If the vendor can’t point to a fixed corpus and a published methodology, the number isn’t testable.

For a proof-of-concept evaluation: install BrassCoders’s OSS core, run it against a representative internal project, and review the .brass/ai_instructions.yaml output. The findings are reproducible — run the scan again and you get the same output. That determinism is the baseline any honest vendor evaluation should meet.

Install BrassCoders with pip install brasscoders and run brasscoders scan . from your project root. The OSS core is free and Apache 2.0 licensed. BrassCoders Paid adds semantic deduplication for teams where raw finding volume makes triage impractical.