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Configuration Management: Import, Validate, Version, Deploy

Configuration management matters most when you are changing a live policy under pressure. Keeptrusts treats that work as an operational workflow, not a text-editing exercise: import an existing policy-config.yaml, edit it in the Configurations workbench, use draft lint and review panels to catch mistakes, save a version with a change detail, deploy to one or more gateways, then verify the result in runtime surfaces like Events and Escalations.

Use this page when​

  • You need to turn a local YAML file into a managed configuration record.
  • You want a repeatable path from draft edits to production rollout.
  • You need to preserve a clean audit trail for who changed what, when, and why.

Primary audience​

  • Primary: Technical Engineers
  • Secondary: Technical Leaders and Governance reviewers

The problem​

Policy changes often fail in ordinary, unremarkable ways. The YAML parses, the gateway accepts it, and only then do you notice that a review queue doubled, a policy became broader than intended, or a routing change sent traffic somewhere you did not expect.

That is why a plain text editor is not enough. The risky part of policy management is not just syntax. It is the gap between an edited file and the running system. Teams run into the same failure modes repeatedly:

  • A file on a laptop is newer than the version enforced by the gateway.
  • A saved draft has no useful explanation for why it changed.
  • A rollout is assumed to be successful because the save action worked, even though live traffic has not been checked.
  • A rollback happens, but nobody is sure whether they are restoring the previous saved version or the last known-good deployed behavior.

Keeptrusts is opinionated here because governance work needs provenance. The goal is not to make YAML authoring fancy. The goal is to make a policy change observable and reversible.

The solution​

The Configurations area gives you a versioned policy workflow instead of a loose file-handling workflow.

At the list level, you can search the inventory by configuration name, gateway, version, change detail, or related deployment context. That matters because a large deployment rarely has only one configuration in flight.

At the detail level, the console gives you a YAML workbench with three practical helpers while you edit:

  • Problems to surface parse errors, semantic findings, and advisories.
  • Outline to navigate large YAML documents without losing structure.
  • Recipes to insert supported building blocks for providers, policy sections, secret references, routes, and testing snippets.

The workflow does not stop at editing. You can import an existing file, review the normalized draft, save a new version with a meaningful change detail, inspect expert diagnostics or raw YAML diff when needed, then roll the change out to one or more gateways.

That sequence is the real value. Importing gets old policy code into a managed surface. Validation reduces accidental errors. Versioning gives you traceability. Deployment connects the reviewed draft to the actual runtime.

Implementation​

The safest way to use the page is to treat it as a short release pipeline.

  1. Start in Configurations and locate the target record, or open /configurations/new when you are creating a managed version from an existing file.
  2. Use Import Configuration if your current source of truth is a local policy-config.yaml.
  3. Review the imported content in the editor and fix anything the Problems panel raises before thinking about rollout.
  4. Use Outline to confirm the file shape and Recipes to insert only supported YAML blocks.
  5. Open review, read the semantic summary first, and use Expert diagnostics or Raw YAML diff only when you need deeper detail.
  6. Save a version with a change detail that another engineer can understand later.
  7. Deploy to the intended gateways.
  8. Verify the running behavior in Gateways, then inspect representative traffic in Events and queue impact in Escalations.

For a simple baseline, the declarative config reference uses this valid config-document shape:

pack:
  name: support-guardrails
  version: 1.0.0
  enabled: true
  description: Customer support production baseline

policies:
  chain:
    - audit-logger
    - pii-detector

policy:
  audit-logger:
    retention_days: 365
  pii-detector:
    action: redact

If the imported file includes provider credentials, move those out of literal YAML and into supported secret references before rollout. Keeptrusts supports secret_key_ref using environment-backed or store-backed values rather than raw keys in the config.

When you want a final command-line check before deployment, run the same lint step that the docs recommend for local policy work:

kt policy lint --file policy-config.yaml

That command is useful even when you primarily work in the console. It gives you a second, explicit validation point before you publish a new managed version.

One practical rollout pattern looks like this:

  1. Import the current production YAML into /configurations/new.
  2. Make the smallest necessary change, such as tightening a PII rule or adding an audit-logger setting.
  3. Save the version with a change detail like Redact account numbers in support replies.
  4. Deploy during a monitored window.
  5. Open Events with a short time range and inspect verdict mix and reason codes.
  6. If escalations rise unexpectedly, pivot into the review queue before widening the change.

The page also handles degraded editing conditions sensibly. If Monaco is temporarily unavailable, Keeptrusts falls back to a plain-text editor while keeping Problems, Outline, Recipes, and formatting available. That matters during incidents: you are not blocked from editing just because one enhanced editor feature failed to load.

Two habits make the workflow materially better.

The first is writing real change details. Update config is almost useless three weeks later. Add redaction to outbound responses for ticket summaries is useful.

The second is verifying after rollout in runtime surfaces, not just in the editor. A saved version is a record. It is not proof that real traffic behaves as expected.

Results and impact​

Used properly, Configurations turns policy changes into governed releases. You get a searchable inventory, version history, change details, explicit rollout targets, and clear post-deploy verification points. That improves both incident response and ordinary maintenance.

It also changes how teams collaborate. Engineers can review a version and its rationale. Reviewers can correlate configuration changes with queue volume. Leaders get a cleaner operational history for audits and retrospectives. Most importantly, rollbacks become a controlled action instead of a scramble to find a file from yesterday.

This is the right level of rigor for AI policy work. Not heavyweight process for its own sake, but enough structure that a bad rollout is easier to catch and a good rollout is easier to prove.

Key takeaways​

  • Importing YAML into Configurations is only the first step; the real value is the managed workflow around validation, versioning, and rollout.
  • Use Problems, Outline, and Recipes while drafting, then rely on review before saving or deploying.
  • Save every meaningful change with a specific change detail.
  • Validate with kt policy lint when you want an explicit pre-deploy check outside the UI.
  • Always verify the rollout in Gateways, Events, and Escalations instead of assuming success from the save action.

Next steps​