When a Terraform pull request opens at 9 a.m. and Snyk IaC flags an S3 bucket for missing encryption, that's the default rule set doing its job. But what happens when a platform team wants a stricter bar — say, blocking any bucket without a specific KMS key alias, or requiring a cost-center tag on every resource — and that requirement doesn't exist in Snyk's out-of-the-box policies? This is where custom policy sets come in. Snyk IaC lets organizations write their own rules in Rego (the Open Policy Agent language), package them into a bundle, and layer them on top of — or in place of — the built-in checks. Understanding exactly how that layering works, including which rules win when a custom check and a default check disagree, matters for any team trying to make IaC scanning reflect its actual internal standards rather than a generic baseline. This post walks through the mechanics.
What Is a Snyk IaC Custom Policy Set, Technically?
A Snyk IaC custom policy set is a bundle of Rego rules, compiled with Open Policy Agent (OPA) tooling, that Snyk's scan engine evaluates alongside its built-in rule catalog. Snyk provides an SDK-style toolkit — the snyk-iac-rules package — for authoring these rules against a documented schema. Each rule file defines metadata (a unique ID, title, description, remediation text, and severity) plus a deny block written in Rego that inspects the parsed IaC resource graph (Terraform HCL, CloudFormation, Kubernetes manifests, or ARM templates converted into a common JSON representation) and returns a violation when a condition is met. Once written, the rule files are compiled into an OPA bundle — a .tar.gz archive — using the snyk-iac-rules build command. That bundle is the artifact Snyk actually consumes at scan time, whether run locally through the CLI or centrally through org settings.
How Do Custom Rules Interact With Snyk's Built-In Rules?
By default, custom rules are additive: they run alongside Snyk's several hundred built-in cloud configuration checks rather than replacing them outright. A custom rule only "overrides" default behavior in the sense that it introduces a new, independently-numbered finding (custom rule IDs are defined by the author, separate from Snyk's internal SNYK-CC-XXXX namespace) that appears in the same scan output. To actually suppress a default check — for example, if an organization disagrees with a specific built-in rule or wants to replace its logic entirely — Snyk IaC provides a separate control in organization settings where individual default rules can be toggled off. The practical override pattern is therefore two-step: disable the default rule you don't want, then ship a custom Rego rule that encodes the behavior you do want. Snyk does not currently offer in-place editing of a default rule's underlying logic; the built-in rules are treated as fixed, versioned content shipped by Snyk, while customization happens through this toggle-plus-bundle mechanism.
How Do You Build and Test a Custom Rule Before It Runs in CI?
You build and test a custom rule locally with the same snyk-iac-rules CLI used to package it, before it ever touches a pull request. The typical authoring loop looks like this: write the Rego file defining the rule's metadata and deny logic, write one or more test fixture files (sample Terraform or Kubernetes snippets representing both a passing and failing configuration), and run snyk-iac-rules test to confirm the rule fires exactly where expected. Only after the rule passes its own test suite does the author run snyk-iac-rules build to produce the distributable bundle. This test-first loop matters in practice — a Rego rule with an inverted boolean or a mistyped resource-type match can silently fail to fire (a false negative) or fire on every resource (a false positive flood), and unlike a missed unit test in application code, a broken IaC policy rule doesn't fail loudly; it just produces a scan result nobody double-checks until an incident forces a review.
How Does the Custom Policy Set Get Applied Across an Organization's Scans?
A custom policy set gets applied organization-wide by uploading the compiled bundle once into Snyk's org-level "Custom Rules" settings rather than distributing it to every developer's laptop. There are two distribution paths documented by Snyk: an ad hoc path, where a developer passes --rules=./bundle.tar.gz to a local snyk iac test invocation for one-off testing, and a centralized path, where an org admin uploads the bundle through the Snyk web UI (or API) so it's automatically pulled into every subsequent scan — CLI, CI/CD pipeline, or PR check — run under that organization, without each pipeline needing to reference the file explicitly. This centralized path is what makes the mechanism usable as an actual governance layer: a security team can update the bundle once and have the new rule enforced the next time any repository under that org is scanned, rather than relying on every team to update a local flag.
What Happens When a Custom Rule and a Default Rule Disagree on the Same Resource?
When a custom rule and an enabled default rule both evaluate the same resource, both findings are reported independently — Snyk does not merge or arbitrate between them, since each rule (default or custom) declares its own severity and produces its own line in the results. This means severity conflicts are resolved by whichever mechanism gates the build, not by the rule engine itself. Most teams enforce a fail threshold (for example, failing a CI job on any high or critical severity finding via the CLI's --severity-threshold flag or a .snyk policy file), so if a custom rule assigns "high" severity to a misconfiguration that the default rule set only flags as "medium," the pipeline fails because of the custom rule's classification, not because the two results were reconciled. A .snyk policy file can also be used to explicitly ignore a specific finding by rule ID and path, with an optional expiration date — a separate, narrower override mechanism from the org-wide custom-rules toggle, scoped to a single project rather than the whole organization.
What Are the Practical Limits of Overriding Defaults This Way?
The practical limit is that "override" here means suppress-and-replace, not edit-in-place, which puts the maintenance burden entirely on the customizing team. Because default rule logic isn't directly editable, every customization beyond a simple on/off toggle requires writing and maintaining Rego — a declarative, non-mainstream language that most application and platform engineers haven't used before, which raises the bar for who on a team can safely author or review a policy change. Bundles also need explicit version control and re-testing discipline: there's no automatic reconciliation if Snyk updates its default rule catalog in a way that would have made a custom replacement redundant, so organizations can end up running a custom rule indefinitely after the underlying default rule catches up, or drift the other way if the custom bundle isn't revisited. For organizations with many repositories and multiple scanning tools beyond just Snyk IaC, tracking which policy customizations exist, why they were added, and whether they're still needed becomes its own governance problem, separate from writing the Rego itself.
How Safeguard Helps
Custom policy sets solve the problem of making one scanner's output match an organization's internal standard, but they don't solve the harder problem most security teams actually face: knowing whether that customized policy is consistently enforced everywhere it should be, and whether it's still doing anything useful six months later. Safeguard gives security and platform teams a single place to see which repositories are actually covered by IaC scanning, which policy customizations and rule suppressions exist across the fleet, and where coverage gaps or stale exceptions have crept in — without needing every reviewer to read Rego to understand what's being enforced. By correlating IaC findings with the rest of an organization's software supply chain posture — SBOMs, build provenance, and dependency risk — Safeguard helps teams treat policy customization as an auditable, ongoing governance activity rather than a one-time configuration task that nobody revisits until an audit or incident forces the question.