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Most compliance evidence still gets collected by hand. Someone opens a console, takes a screenshot, drops it in a folder, and hopes it still means something when the auditor reviews it four months later. While that approach can survive an initial assessment, it does not survive continuous monitoring.
Paramify's evidence automation closes that gap. Instead of collecting proof on a deadline, you pull it directly from the systems that already hold it, validate it the moment it lands, and let failures surface as tracked issues.
Here's how the pipeline works, end to end.
Watch to see Paramify's evidence collection for yourself
How does evidence get from your cloud into Paramify?
The full path has four steps:
- A fetcher pulls data from a resource like AWS or CrowdStrike.
- The fetcher parses the response and stages a JSON artifact.
- Paramify's REST API pushes the artifact into an evidence set.
- A validator checks the artifact. If it passes, the evidence is recorded against every requirement it supports. If it fails, Paramify creates an issue automatically.
No one uploads a screenshot. No one updates a spreadsheet to say the upload happened. And because each artifact arrives with the configuration and scope it was collected under, you get something an assessor can actually work with (which is the difference between an artifact and real evidence.)
What runs where, and what you control
What are evidence fetchers?
Evidence fetchers are lightweight scripts. Each one connects to a single resource, retrieves a specific piece of configuration or state data, and prepares an artifact for upload. That's the whole job.
They live in a public GitHub repo, and the list of supported services grows as new ones are added. Because the scripts are open, you can read what each fetcher queries before you run it.
Why do fetchers run in your environment, not ours?
Fetcher scripts and the secrets required to run them are not configured and deployed in Paramify’s environment, but in an environment you designate.
You decide which fetchers run, what they query, and which artifacts get uploaded.
That means you keep complete control of the data flowing into Paramify. If a fetcher pulls something you'd rather not share, you don't run it. The compliance platform sees the artifact you chose to send.
It's a security-first design, and it's the right one. A tool that demands standing access to your production environment in order to document your security posture has introduced a new risk in exchange for a report.
How do you run fetchers on a schedule?
Fetchers run anywhere you can run a script. A Docker container is enough to get started — clone the repo, configure your secrets, select the fetchers you want, and run them. A terminal interface handles fetcher selection and orchestrates the upload.
For a real deployment, move them somewhere that runs on its own: AWS Lambda, EKS, or any scheduler you already trust.
Once fetchers run on a schedule and upload through the REST API, the manual steps disappear entirely. Evidence arrives whether or not anyone remembers to collect it.
How do evidence sets validate what arrives?
Every artifact lands in an evidence set, and evidence sets map to specific solution capabilities. Those capabilities, in turn, satisfy the requirements and controls of whatever program you're pursuing: FedRAMP, CMMC, FISMA, or several at once.
Under FedRAMP 20x, where Key Security Indicators replace prescriptive control narratives, that mapping is how you show continuous validation rather than a point-in-time snapshot.
Validation uses simple regex expressions against the JSON. A validator on an AWS Config evidence set, for example, checks whether the artifact indicates the configuration recorder is active. It’s plain, readable, and easy to adjust when your expectations change.
The mapping is what makes this scale. One artifact can satisfy many controls, and one failure propagates to every control that depends on it.
What happens when validation fails?
A failing validator generates an issue automatically, and that issue appears in the issues dashboard.
The failure is tracked against every solution capability and control it's mapped to, so you see the full blast radius immediately instead of discovering it during an assessment.
Triage starts from the dashboard: assign it, work it, close it, or document why it's acceptable.
Issues form the basis of Paramify's continuous monitoring. Instead of assembling a ConMon package from scratch each cycle, you work down a queue that stays current on its own.
→ See how Paramify handles POA&M and continuous monitoring
How does this fit your infrastructure?
Nothing in this pipeline is fixed. You choose which fetchers to run, where they run, how often, what the validators check, and how evidence sets map to your solution capabilities. The process is built to be customized around your infrastructure and your compliance goals rather than the other way around.
You get a live signal from your actual environment, checked against your actual requirements, with a tracked issue the moment something drifts. Good documentation falls out of that as a byproduct.
Get started: Automate your evidence collection
Manual evidence collection works right up until the moment you have to do it continuously. Screenshots in a folder can carry an initial assessment; they cannot carry monthly reporting, and they cannot tell you when something has drifted.
Paramify's answer is to move collection to the source and validation to the moment of arrival. Fetchers pull from the systems that already hold the data, evidence sets check what arrives against expectations you define, and failures become tracked issues mapped to every control they touch. Documentation stops being a deliverable you assemble and becomes a byproduct of a pipeline that runs on its own.
Paramify is a risk management platform with continuous visibility into whether your controls are actually working, with the authorization artifacts falling out the other end.
Next steps:
- Watch the evidence automation walkthrough to see the pipeline run against a live AWS account
- Review automated evidence collection and validation for the full capability set
- Read FedRAMP Evidence Collection: Stop Gathering Artifacts, Start Closing Arguments if you're building a pipeline and hitting resistance from assessors
- Request a demo to see evidence automation running against your own stack
FAQ
What is an evidence fetcher? A lightweight script that connects to one resource, retrieves a specific piece of configuration or state data, and prepares a JSON artifact for upload to Paramify. That's its entire job.
Does Paramify need access to my cloud environment? No. Fetcher scripts and the secrets they need are configured and deployed in an environment you designate, not in Paramify's. You decide which artifacts get uploaded.
Where can fetchers run? Anywhere you can run a script. A Docker container works for getting started. For production, teams typically move them to AWS Lambda, EKS, or an existing scheduler.
How does Paramify validate the evidence that arrives? Each evidence set uses regex expressions against the incoming JSON. A validator on an AWS Config evidence set, for instance, checks whether the artifact shows the configuration recorder is active.
Can one piece of evidence satisfy multiple controls? Yes. Evidence sets map to solution capabilities, and those capabilities satisfy requirements across programs. One artifact can support many controls, and one failure propagates to every control that depends on it.
What happens when a validation check fails? Paramify creates an issue automatically and displays it in the issues dashboard, tracked against every solution capability and control the failed check is mapped to.
If an issue is closed and the same failure comes back, does Paramify reopen it or create a new one? Either, depending on how you configure it. Paramify supports reopening the original record to preserve its full history, or generating a new finding instance for the recurrence.
Does Paramify keep a history of changes to an issue? Yes. Every change is logged with the user who made it and a timestamp, giving you a full activity record across the lifecycle of a finding.
Can issues be routed into Jira? Yes. Paramify integrates with Jira and can create remediation tickets in the appropriate project, assigned to the owner responsible for the affected asset.
Can I customize how scan data maps into Paramify? Yes. Ingestion mappings are customizable, support pattern-based parsing, and can be reused across workspaces.
Which frameworks can automated evidence support? Any program whose requirements and controls are represented in your workspace, including FedRAMP, CMMC, and FISMA. The same artifact can serve several frameworks at once.
Does Paramify have an API for this? Yes. Evidence upload runs through Paramify's REST API, and the same API supports pulling data out for external automation and custom reporting.
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