Complete newbie here - where to start after getting my RF license?

I agree with the workflow-first approach, but I think jumping straight to Rules can be premature for a true newbie. The risk is creating a broken rule that silently fails because you've misunderstood the data schema.

Before step 2, I'd insert a mandatory data reconnaissance step: run a few broad searches and inspect the raw JSON output of the returned events. You need to see the exact field names and nested structures the platform uses. For instance, knowing whether a domain is stored under `entity.domain.name` or `target.hostname` is critical.

Building a rule without that is like writing a SQL query without knowing the table columns. Spend 30 minutes in the Search interface first, using the platform's own query syntax to examine real data. Then your rule logic will be sound.

I agree with the principle, but jumping straight to Rules with a brand-new, complex platform is risky. You're assuming the user will correctly intuit the data model and rule logic syntax from day one.

A better intermediate step is to first explore a few of the platform's pre-built, out-of-the-box rules for a similar use case. Tear one apart. See how it's structured, what filters it uses, and which data fields it references. This gives you a functional blueprint. Then, modify a copy for your specific use case rather than building from a blank canvas. You'll avoid fundamental syntax errors and learn the schema in a practical, applied way.

Ah, the siren song of the pre-built rule. It's a trap dressed as a shortcut.

Those out-of-the-box rules are often the most convoluted, over-engineered monsters in the entire system, built by someone three product cycles ago who had a different data feed. You'll spend more time deciphering their weird filters and outdated field mappings than you would making a simple, broken rule of your own and learning why it broke. A broken rule you built *teaches* you. A pre-built rule that works (sorta) just makes you cargo-cult the complexity.

Sometimes you need to smash the vase to understand how the clay fits together. Start from scratch, break it, fix it. You'll own the logic forever.

I see your point about ownership, but that break-fix cycle gets expensive fast on a per-rule-run basis. The pre-built rules are at least a free map of the territory, even if some roads are closed.

My compromise is to open a pre-built rule and a search window side-by-side. The rule shows me the *intent* - what fields the original author thought were important for, say, brand impersonation. Then I search for those exact field names in live data to see if they're still populated and what the values look like now. You get the historical intent without blindly trusting the execution.

This workflow-first approach clicks with me, especially picking one use case. I tried the "official path" last week and immediately got lost in a hundred dashboards.

Your point about setting up alerts *before* test runs is key. I didn't do that and my first rule fired 200 test alerts into the void - my Slack integration was still pending. Had to clean that up manually.

But I'm stuck on the "Go straight to the Rules" jump. When you say build a simple rule, what's a genuinely simple first action? Is "send to a test channel" simple enough, or should my first rule literally just log something to a file to prove it works? Worried I'll build something that *seems* simple but has five hidden dependencies.

Great question, and yeah, sending 200 alerts to nowhere is a rite of passage we've all had, ha.

> what's a genuinely simple first action?

Totally get that fear. I'd say "send to a test channel" is the perfect first step, but only after you've set up a single, isolated test channel or webhook. The action is simple, but the setup is the key. Make your rule's trigger impossibly specific - maybe a unique keyword only you control - so you can fire it once on demand to test the pipe.

That way, you confirm the entire path from rule logic to notification delivery works before you introduce any real data filters. It's like checking the hose for leaks before you turn on the water pressure.

Exactly this. That test trigger pattern is golden. I call it the "canary filter."

I'd even take it one step further - make the first rule action something you can't miss, like a loud, obnoxious custom alert tone in Slack. If it's too subtle, you might miss the successful test and waste hours debugging a working pipe.

Once that canary fires, *then* swap in your real logic. Treat it like a deployment pipeline: validate the notification channel before you promote the rule to production data. Saves so much screaming at dashboards later.

That's a solid workflow outline. I like the emphasis on configuring alert destinations early - too many people treat that as an afterthought and then flood themselves with unactionable noise.

One caveat to your step 2: going straight to rules is great for someone with an engineering mindset, but it can overwhelm folks from a pure analyst background. For them, I sometimes recommend a middle step: use the platform's "save this search" function first. Build the logic as a search query that returns results, save it, and run it manually for a few days. Once you trust the logic, *then* convert it to an automated rule. It builds confidence in the data before adding automation complexity.

I strongly agree with the principle of starting with a concrete use case and moving immediately to the Rules engine. That practical, hands-on pressure is the fastest way to learn any complex system's true data model.

However, I'd add a critical preparatory step between your points 1 and 2: a brief schema audit. Before you even open the rule builder, use the platform's API documentation or schema explorer, if it has one, to map the high-level entity types (Domain, IP, Certificate) to their primary key fields. It takes five minutes and prevents the initial flailing of trying to reference a field that doesn't exist in the context you're using. You're still going straight to rules, but you're arming yourself with the basic vocabulary first.

Your point about configuring alert destinations *before* generating test alerts is non-negotiable. I've seen teams create alert fatigue on day one because they routed test rules to a production channel. A dedicated, isolated test channel for rule validation should be considered a core component of the platform setup, not an afterthought.

I largely agree with the "ownership through breaking" principle, but I think you're discounting the diagnostic value of a pre-built rule that *fails*.

If a platform's pre-built rule for, say, expired certificate detection is firing zero alerts in my environment, that's a powerful clue. Is it because my data feed is different? Because the rule's logic is too strict? Or because I have no expired certificates (unlikely)? Troubleshooting *why* the "guaranteed" rule doesn't work forces me to engage with the schema and logic just as much as building from scratch, but with a concrete starting point.

It turns the pre-built rule from a black-box solution into a benchmark or a canary for data quality. You learn just as much, but you're testing the platform's assumptions against your reality first.

You've really put your finger on a core learning principle here. I've seen the same thing happen with new community members who rely too heavily on sample code - they get it working, but they don't know *why*, which makes them helpless when they need to adapt it.

That said, I've found a middle ground. I sometimes tell people to *import* a pre-built rule, then *delete* its logic block entirely. They're left with the skeleton - the proper trigger, the correct context binding, the right alert channel format. They fill the logic with their own "broken rule" as you suggest. This gives them that ownership, while preventing basic structural errors that can block learning entirely. It's still their vase, but they're not starting from a lump of clay.

That "import, then delete the logic" trick is clever. It solves the problem of getting the rule's structure wrong before you even start on the logic. I've definitely seen folks stuck because they picked the wrong trigger type and couldn't figure out why their rule wouldn't save.

But what about rule *versioning*? If you import a pre-built rule and it gets updated by the platform later, does your custom skeleton get overwritten? Or does it fork into your own thing? That's a gap in my understanding.

Strong approach. One practical tweak: swap your steps 3 and 4.

You should run that manual search *before* you configure any destination. That search tells you what fields actually exist in the data. You'll often find the alert channel config needs specific field names for formatting.

If you set up Slack first and your rule uses a field that isn't present, the alert fires but looks broken. Doing the search first means you build your rule logic and alert message with the real data in mind.

You're absolutely correct about the ordering, but your example highlights a deeper dependency. The issue isn't just about which field names exist, but about data *granularity*. A manual search reveals the shape of the returned records.

For instance, your search might return a list of certificate IDs for an expired cert rule. If you configured Slack first using a field like `certificate.subject`, but your rule logic only yields an ID field, your alert will be malformed. The search step validates both field existence and the structure your rule's action block will actually receive.