The Checkov alternative that shows its reasoning
Checkov put policy-as-code in every Terraform pipeline, and it remains the widest checklist you can run. audytx does a different job. Instead of matching each resource against that checklist, it builds a relationship graph of your AWS estate, works out what is reachable, invocable, and exposed, and suppresses the findings the graph proves benign — with the reason written next to each one. Measured on a public corpus, that comes out to the same 100% IAM privilege-escalation recall as Checkov, with ~36× fewer false positives.
audytx vs Checkov at a glance
Measured rows come from the pinned public benchmark — full tables, methodology, and reproduction steps on the comparison page.
| Dimension | audytx | Checkov |
|---|---|---|
| IAM privilege-escalation recall (31 paths) | 100% | 100% |
| False positives, 21 clean modules | 33 | 1,193 |
| IAM-privesc precision | 23% | 12% |
| Alert volume on iam-vulnerable | 135 | 269 |
| False-positive handling | context-proven suppression, rationale shown | manual (checkov:skip comments, skip-lists) |
| Delivery | GitHub App (60-second install) + hosted MCP server + offline CLI | CLI / CI step / Prisma Cloud platform |
| Engine | deterministic Rust, no AI in the pipeline | Python policy engine |
| Design center | AWS depth: cross-resource reasoning over one cloud | breadth: 1,000+ policies across AWS, Azure, GCP |
Checkov row facts: policy count and multi-cloud scope per the Checkov repository (actively maintained by Prisma Cloud). Benchmark rows pinned to Checkov 3.2.520 — versions, dates, and scorer in the methodology.
Why teams go looking for a Checkov alternative
The pattern repeats everywhere: a team adopts a policy scanner, the PR annotations pile up, and within a quarter the output is muted, skip-listed, or scrolled past. The checks aren't wrong. A single-resource pattern matcher simply can't tell a real risk from a benign one. It flags a Lambda for a missing dead-letter queue when nothing invokes that Lambda asynchronously. It flags the dead-letter queue for not having its own dead-letter queue. Each finding is defensible; the sum is noise, and noise trains reviewers to ignore the one finding that matters.
audytx bets the other way: one finding you can trust beats ten you have to triage. Before any rule fires, the engine maps how your resources relate (invocation, trust, encryption, exposure, lifetime) across the whole repository, then lets that context decide what survives. Suppressed findings aren't dropped; they're listed with the reasoning, so you can audit the engine's judgment the same way you'd audit a colleague's. How the suppression works →
Two different missions
The two tools are optimizing for different things, and it shows in how each is best used.
audytx — the precision layer
- One cloud (AWS), reasoned about deeply: 250+ checks that fire only when cross-resource context says they're real
- Full IAM attack-path coverage — privilege-escalation chains, trust-graph reasoning, role chaining
- Every suppression carries its rationale — reviewable, deterministic, the same verdict every run
- Built for the AI-agent era: a hosted MCP server lets the coding agent that wrote the Terraform get a review before the PR exists
Checkov — the breadth sweep
- 1,000+ policies spanning AWS, Azure, and GCP — the widest checklist in open source
- Backed by Prisma Cloud, with native platform integration for teams already on it
- A fit when the goal is "inventory everything potentially wrong" and triage capacity exists to absorb it
- The two can run side by side: Checkov as the wide sweep, audytx as the reviewer whose verdicts gate the merge
There's nothing to migrate
No config to port, no CI pipeline to rewrite. audytx installs as a GitHub App
and reviews the next pull request: one comment, inline annotations, SARIF to GitHub Code
Scanning. Keep your existing scanner running if you want, compare the two side by
side on a few PRs, and see which comment your reviewers actually read. Suppressions
you've accumulated as checkov:skip comments translate to a single
.audytx-baseline.yaml with expiry dates — and most turn out to be unnecessary,
because the context engine already suppresses them, with the reason written out.
If you'd rather see numbers before installing anything: the benchmark hub has all five tools on 28 corpora, the seven-real-repo run shows audytx vs Checkov on live repositories, and the neutral Checkov vs Trivy field guide covers the rest of the market.
Frequently asked questions
Is audytx a good Checkov alternative?
For AWS Terraform, yes — audytx matches Checkov's 100% recall on the 31 documented IAM privilege-escalation paths while firing ~36× fewer false positives on clean production modules (33 vs 1,193, measured on a public corpus). Teams that need Azure or GCP coverage keep a multi-cloud tool alongside it.
What does audytx do that Checkov doesn't?
Cross-resource context suppression. Checkov's graph finds more misconfigurations; audytx's graph also proves fired findings benign and suppresses them with a written rationale. Checkov's suppression is manual (checkov:skip comments); audytx's is automatic and auditable.
Can I run audytx and Checkov together?
Yes: Checkov as the broad multi-cloud sweep, audytx as the high-precision AWS reviewer whose findings gate the merge. audytx posts one PR comment and uploads SARIF, so the two don't collide.
Run the comparison on your own repo
Install the GitHub App and audytx reviews your next pull request with this exact engine — free on every repo during the beta, public or private.