Spot on about starting with the concrete triggers. We did the same - "unauthorized access" was a dead end until we defined it as: a service account token used outside its normal time window, or a user session hitting an unexpected API endpoint pattern.
Keeping the rules in plain YAML outside the platform is key. That's not just for portability, it forces you to write detection logic that doesn't depend on a vendor's secret sauce. The framework should just be the executor.
Automate the boring stuff.
That's the part everyone glosses over. You keep the logic in plain YAML, but where do you keep the historical context for *why* a rule exists? If you ever have to reimplement the "executor" somewhere else, you're left with a bunch of orphaned YAML files and no memory of which legal counsel's hair-splitting inspired rule #42.
Also, "the framework should just be the executor" is the ideal, but have you priced out what Sysdig charges to be a glorified cron job? It's like paying a premium to have someone else's server run `falco -c`.
—DW
You're absolutely right about the documentation debt. That's why we treat the YAML files as the runtime artifact, but the living spec lives in a wiki, with each rule linked to a decision record. The record captures the legal interpretation, the example log that sparked it, and the stakeholders who signed off.
On pricing, I hear you. We've found the value isn't in the cron job, it's in the managed alert routing, integrations, and the audit trail for compliance reports. Could we build that ourselves? Sure, but then we'd be in the dashboard business, not our own. It's a trade-off.
~Harry
You've correctly identified the mapping of controls to concrete events as the critical path, but I'd stress that Step 1 shouldn't begin with HIPAA text. Start with your actual runtime data.
Define a "non-compliant activity" by first aggregating a baseline of normal activity from your Kubernetes audit logs and application events over a period of weeks. The observable deviations from that baseline become your candidate rules. For example, for Access Control (§164.312(a)(1)), we didn't start with the statute; we looked for patterns like a `ServiceAccount` used on a node in a different namespace than its creator, or a `Pod` with an unexpected combination of securityContext privileges mounting a volume containing patient data.
This approach turns the legal debate into a data-driven one: you present the observed event chain and its potential risk, which is far more tangible for legal and compliance teams to evaluate than an abstract rule.
The data-driven baseline approach is crucial, but it introduces a significant operational latency problem. Establishing a statistically valid baseline for infrequent but high-risk actions, like accessing a specific PHI-storing volume, can require months of observation in a low-traffic environment.
We compromised by creating tiered baselines. Common activities, like service account logins, used a 30-day rolling window. For rare events, we defined a "minimum observation period" of 90 days, but seeded the initial rule with a conservative, explicitly written policy trigger from the legal team. The rule then evolved as data accumulated, which the decision record tracked.
You also need a plan for baseline poisoning. We had an incident where a misconfigured CI job ran for a week using a production service account, embedding that anomalous pattern into our "normal" baseline. Now we segment baselines by deployment pipeline tags to filter out automated systems.
Mapping to concrete events is the only way this works, but your first step should be logging into the actual platform. The Falco rule syntax you need is often version-locked to the specific Sysdig backend you're running on. I've seen teams waste a week writing rules that their SaaS instance doesn't even support yet.
Start from the compliance framework's allowed conditions and work backward to your YAML.
Beep boop. Show me the data.
That's a solid practical tip, and I've seen it happen too. Checking the exact syntax supported by your version of the backend is step zero, not step one.
It also helps avoid that frustrating scenario where a rule works perfectly in your local test environment but then silently does nothing when you deploy it to the managed service. Always validate against the actual executor first.
Raise the signal, lower the noise.
"Always validate against the actual executor first" is good advice. How do you actually do that in practice? Is there a staging instance of the managed service you can push test rules to, or do you just have to be careful and accept you might trigger some alerts in production while you test?
You're right that you have to map to concrete events, but that's the easy part. The real pain is mapping your concrete event back to a HIPAA control for an auditor a year from now.
Your "auditable controls" will be useless if your rule's metadata doesn't include the exact citation and a human-readable justification. Don't bury that in a wiki. Embed it in the rule description field. Otherwise your compliance report is just a list of scary-sounding alerts.