I'll admit, I rolled my eyes a bit when the Tenable sales rep started waxing poetic about the "power and flexibility" of their compliance frameworks. My experience has been that "flexibility" in these platforms usually means you get to choose from their pre-baked list and like it. However, while digging through some documentation to prove a point about a missing CIS check for a specific cloud service, I stumbled into the actual custom check builder. To my genuine surprise, it's not completely terrible.
It turns out you can define a custom check using a YAML structure that is, while verbose, fairly logical. It's not some magical no-code wizard; you need to understand the resource query language and the actual property you're inspecting. For example, I needed to verify that all our Cloud Storage buckets in a particular project had a specific, internal-only label for cost allocation—something no standard framework would ever cover. Instead of writing a one-off script and trying to run it on a schedule myself, I defined it in Tenable.
The YAML looks something like this:
```yaml
name: "Custom: GCP Bucket Cost Center Label Check"
description: "Ensures all GCS buckets have the 'cost-center' label applied."
scope:
cloudProviders:
- gcp
serviceTypes:
- storage.googleapis.com/Bucket
query: |
resources
| where cloud.service.name == 'Google Cloud Storage'
| where labels['cost-center'] == ''
policy:
condition: ${count} == 0
verdicts:
pass: "All buckets have the required 'cost-center' label."
fail: "${count} bucket(s) are missing the 'cost-center' label."
```
You plug this into the right section of the UI (buried, but it's there), and it gets rolled into your existing compliance scans and dashboards. The real value isn't in the YAML itself, which is just a declarative wrapper, but in the fact that it leverages Tenable's existing data ingestion and graph. You're not paying for additional API calls or setting up a separate collector.
Of course, the "gotchas" are plentiful. The query language is its own beast and can be frustratingly limited for complex joins across resource types. The documentation for the custom check schema is, charitably, sparse. Error messages when you mess up the YAML are cryptic. And you still have to fight the UI to find where to put this and how to slot it into a custom framework.
But, credit where it's due: this moves the tool from being a rigid compliance checkbox exerciser to something you can actually use for internal governance. I've started building checks for things like mandatory architecture diagram links in resource tags, or ensuring development resources are only in specific regions. It's actual FinOps and internal policy enforcement, not just auditing for the standard benchmarks.
Has anyone else pushed this feature beyond trivial examples? I'm particularly curious about attempts to create checks that correlate resources across services, like ensuring a database instance with a public IP is *actually* behind a cloud firewall rule that blocks all external traffic. The query language seems like it might fall apart there.
-- Cam
Trust but verify.
That's a great find. The YAML approach for custom checks is interesting, but I'm always wary about where the actual evaluation logic runs. Is it agent-based on the resource, or does it pull data back to Tenable's backend for analysis? The network latency and load on their system for a large-scale query could be non-trivial.
Also, for something like a missing label, you'd need to consider the remediation path. Does the framework just report a fail, or can it trigger an automated action? In my experience, detection is only half the battle; you need a closed loop.
sub-100ms or bust
Oh wow, thanks for sharing that YAML snippet! I've been wanting to do something similar for our AWS S3 buckets, but I never realized the platform could be this flexible. Could you maybe show a bit more of the actual check logic part? Like, how do you define what makes the check pass or fail?
That's the key part - the pass/fail logic is defined in the `compliance_check` section. You essentially write a query to find non-compliant resources. The check passes if the query returns zero rows.
For your S3 use case, a simplified version targeting the `aws_s3_buckets` resource might look like this in the check body:
```yaml
compliance_check:
query: |
SELECT arn
FROM aws_s3_buckets
WHERE tags ->> 'CostCenter' IS NULL
expected_result: []
```
So it runs that query. If any buckets lack the 'CostCenter' tag, their ARNs are returned. Since the result set isn't empty, the check fails. The verbosity comes from all the surrounding metadata (platform, schedule, severity), but the core logic is just a SQL-like filter.
Yeah, user109 nailed it with that pass/fail explanation. That `expected_result: []` bit is the real key.
One thing I'd add from my testing: the query's WHERE clause needs to be spot on. If you accidentally write `WHERE tags ->> 'CostCenter' = NULL`, the check will always pass (and wrongly!). Gotta use `IS NULL`.
Works great for tagging stuff. Saves a ton of manual review.
Let's build better workflows.
Yeah, the "SQL-like filter for pass/fail" pattern is a solid one. It's similar to what you can do in tools like Cloud Custodian or even some Prometheus alert rules, where you define what a *bad* state looks like and alert on its presence.
The main thing I'd watch for with checks defined like this is alert fatigue. If you suddenly have 500 buckets without that label, you're going to get 500 findings in the console. That's why I always pair these detection checks with a separate dashboard that tracks the *count* of non-compliant resources over time. Lets the on-call see if a new deploy caused a spike, or if it's just a steady background issue that needs a cleanup project.
Sleep is for the weak
You're absolutely right about the alert fatigue, and that's a key operational consideration often missed in the design phase. The dashboard for tracking counts over time is an excellent pattern.
One addition I've found necessary is implementing some form of severity dampening or grouping. For example, a check might be configured to only create a single "aggregate" finding if more than, say, 20 non-compliant resources are detected within a five-minute window, rather than one per resource. This turns a flood of 500 identical alerts into one actionable ticket stating "500 S3 buckets lack CostCenter tag," which is far more manageable for on-call.
The challenge is that this logic often has to live outside the compliance check definition itself, in the alert routing or ticketing system it feeds. It highlights that the YAML check is just the detector; you still need to thoughtfully design the entire notification pipeline.
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