Rabbit CI
Declare infrastructure in YAML. Deploy with git push. And when the audit arrives, the answers are already written down: one source, one reviewed path to production, evidence generated on every run.
Compliance Is a Delivery Problem
Every compliance program eventually hits the same wall. The policies are signed and the controls are documented, yet proving what actually happens in your infrastructure still means screenshots, spreadsheets, and a scramble before every assessment.
The evidence exists. It is just scattered across cloud consoles, chat threads, and the memory of whoever clicked the button.
Rabbit CI attacks the problem at the root. When every change arrives through one door - a pull request, planned before merge and applied by machinery after - proof stops being an archaeology project. It becomes something your delivery system produces as a side effect of working.
Most of a technical control baseline is asking one question: can you show that everything reaches production from source, through review, by machine identity? Rabbit CI makes that sentence true and keeps it true. Teams that adopt it for delivery speed keep discovering they built their audit story by accident.
Compliance platforms are the system of record. They collect evidence, track controls, and manage the human workflows around them. Rabbit CI is the system of proof underneath: it makes your delivery produce the facts those platforms report. One source of truth, one path to production, evidence generated by the machinery itself.
Declare the environment you want. The proof writes itself.
Everything as Code, Not Just Infrastructure
The jump from half-covered to three-quarters happens when configuration stops living in consoles. Rabbit stores everything an environment is - CDN, WAF, DNS, workloads, secrets wiring - as YAML in the application's repository, applied by one GitHub Actions step. When an assessor asks who approved a firewall change and what exactly changed, the pull request is the answer.
Drift Is a Control Violation, Caught Nightly
Every pull request posts a plan of exactly what will change. Merges apply it. Deleted branches clean up after themselves. Then, every night, Rabbit re-plans production against what is actually running. A console click that bypassed the pipeline surfaces in the morning report as an unauthorized change, diff attached - not in an assessor's findings two years later.
Evidence Is a Build Artifact
Workloads run on UDX Worker, our open-source execution boundary: non-root containers, declared services, secrets resolved at runtime and never baked into images. Every run returns evidence by default - a redacted release record, an SBOM on each immutable release, deployment markers in your monitoring. Records generated at the moment of change, not screenshots assembled before an audit.
Read the Worker GuideLifecycles Enforce the Paperwork
A lifecycle policy marks production as protected: manual applies refused, destroys blocked, pull requests limited to planning. The branch decides the environment - not whoever holds the credentials. Short-lived identity via GCP Workload Identity Federation and AWS OIDC replaces long-lived keys. Least privilege, satisfied structurally: there is no compliant mode and fast mode. There is one path.
Your GRC Platform Reports It. Rabbit CI Makes It True.
Platforms like Secureframe, Anecdotes, and Vanta have genuinely automated the top of the compliance stack: evidence collection through hundreds of integrations, continuous control monitoring, and the personnel workflows no pipeline will ever touch. They are the system of record, and we recommend running one.
But an integration can only read what your systems already do. If deployments happen from four different laptops and configuration lives in consoles, the finest GRC data pipeline in the world will faithfully document your gaps.
That is the layer Rabbit CI exists for. It changes what is true underneath - one source, one reviewed path, machine identity, nightly reconciliation - so the platform on top reports green because the substrate is green. It is the same discipline that lets an AI agent work on production safely: guardrails structural enough that intentions stop mattering.
How Much of a FedRAMP Baseline Can You Actually Automate?
Mapped against the NIST SP 800-53 control families, coverage climbs in bands. Most teams sit at the bottom. GRC platforms sell the top. The decisive middle is the layer Rabbit CI builds.
A standard secure SDLC
Branch protections, reviews, and a governed pipeline cover the acquisition, configuration management, and supply chain control families. This is where most engineering organizations already are.
Full pipeline automationRABBIT CI
Infrastructure as code, policy as code, and automated security scanning validate technical system state programmatically instead of by interview.
Everything as codeRABBIT CI
Cloud and business system configuration living in Git under pull request gates. Traditionally human-centric controls become auditable text files with review history.
Cloud inheritanceRABBIT CI
Deploying into AWS GovCloud or Azure Government inherits the physical datacenter burden - in your accounts, with no intermediary platform inside your authorization boundary.
Automated POA&MsRABBIT CI
Findings flow from scanners into tickets and are tracked to closure automatically. Nightly drift plans feed this loop for infrastructure.
GRC platforms
Vanta, Secureframe, and Anecdotes automate training logs, background checks, and vendor workflows - consuming the evidence produced by every layer below.
The last few points stay human
Tabletop exercises, personnel adjudication, and the Authorizing Official signing the ATO. No one automates trust.
Percentages are approximate cumulative shares of a FedRAMP control baseline under NIST SP 800-53. Green bands are what a Rabbit CI implementation delivers.
Questions Compliance Teams Actually Ask
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Can compliance really be automated end to end?
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No, and you should distrust anyone who says otherwise. Automation has a practical ceiling around 95 to 97 percent of a FedRAMP baseline. The last 3 to 5 percent is permanently human: annual tabletop exercises, personnel adjudication, and an Authorizing Official signing the ATO.
What automation changes is the character of everything below that line - controls stop being documents someone maintains and become properties the delivery system exhibits.
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Do I still need Vanta, Secureframe, or Anecdotes if I have Rabbit CI?
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Probably yes, and Rabbit CI makes whichever one you pick considerably more useful. GRC platforms are the system of record: they collect evidence through integrations, monitor controls, and run the human workflows.
But they can only report what your systems already do. Rabbit CI is the system of proof underneath - it makes the facts those platforms collect true in the first place. Green dashboards start with a green substrate.
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What evidence does a Rabbit CI run actually produce?
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Every pull request carries the full infrastructure plan as a review comment. Every apply is a reviewed merge tied to an identity, with deployment markers posted to monitoring. Every release ships with a redacted record of exactly what went out and an SBOM. Every night, a drift plan reconciles declared state against reality.
When an assessor asks who changed what and when, the answer is already written down.
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How does drift detection help during an audit?
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A nightly plan is the strongest form of continuous monitoring for infrastructure: Rabbit re-computes the difference between what the repository declares and what is actually running. An out-of-band console change surfaces the next morning as an unauthorized change with the diff attached, instead of two years later as an assessor's finding.
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We deploy through a compliance-ready PaaS. Is that not enough?
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It can be, until the authorization boundary matters. A managed platform's guardrails live on the platform, which places the vendor inside your boundary and your workloads inside their abstractions.
Rabbit builds the same guardrails-by-default posture in your own cloud accounts, which is where inheritance from AWS GovCloud or Azure Government actually comes from - and your infrastructure remains portable YAML rather than a platform dependency.
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When do I not need Rabbit CI?
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If you run one application, deploy from a dashboard you like, and no customer or regulator has ever asked how, stay put - a PaaS is the right amount of infrastructure for that job.
Rabbit CI earns its keep when the questionnaire arrives: when FedRAMP, CMMC, SOC 2, or an enterprise security review starts asking questions whose honest answer is currently a shrug.
Array
The machinery, the practice, and the field stories built on both.
Assessment, remediation, and continuous evidence - the engagement this machinery serves.
The composite action: one uses: step, YAML in, infrastructure out.
The runtime contract: images, services, secrets, release evidence.
The deployment side of the platform: branches become environments, merges become applies.
Letting AI Agents Work on Production
The same guardrails, stress-tested by an AI agent shipping to production daily.
Shipping to the Azure Marketplace
A certification story: packaged, hardened, and sold on this machinery.
Experience-Driven Engineering
We did not build Rabbit CI as compliance software. We built it to ship production systems - including the page you are reading - and the compliance mapping came later, when we noticed the delivery machinery was already answering most of an assessor's questions.
That origin matters. Controls that fall out of good engineering survive contact with real deadlines; controls bolted on for an audit get bypassed the first busy week.
If your team already runs a clean GitOps flow, you may only need the action itself - it is free on the GitHub Marketplace, no engagement required. And when the audit is the hard part, that is the practice we run.
Talk to an Expert
Want your delivery system to answer the auditor's questions before they ask? Talk to the UDX engineers who build and run Rabbit CI.