Make vs Zapier for MSSPs Triaging Alerts and Client Evidence

Most SIEM alerts aren't incidents, they're noise that needs a first pass of filtering before anything reaches a human. A simple severity threshold is easy to set up in either tool, but severity alone misses context, like an alert that's routine for one client's environment but unusual for another's.

Make's router can weigh severity alongside client-specific baselines pulled from a lookup table, so the same alert type gets treated differently depending on whose environment it came from. Zapier can approximate this with a chain of filter steps, but each added client exception makes the chain longer and more fragile, which is a real problem in a business where a missed filter means either alert fatigue or a missed real incident.

Once an alert is confirmed as a real incident, the escalation path, who gets paged, what severity gets assigned, what the client is told and when, needs to happen fast and needs to be logged accurately, since that timeline often ends up in a post-incident report.

Both tools can trigger a page and open a ticket. Make's advantage shows up in capturing the full context at each branch point, so the eventual incident report can show exactly what data triggered escalation and when, rather than someone reconstructing the timeline from memory and scattered Slack messages after the fact.

Clients increasingly want evidence that specific controls are actually running, log retention, patch cadence, access reviews, not just a claim that they are. Automating the collection of that evidence, pulling a report from a client's own systems on a schedule and packaging it for their auditor, is valuable work, but it means your automation is now touching client data for a purpose beyond security monitoring itself.

Scope this narrowly. An automation built to collect evidence should only be able to read the specific fields needed for that report, not have broad access to a client's environment just because it was convenient to reuse an existing connection. Document exactly what each evidence-collection automation touches, since that documentation is itself something an auditor may ask to see.

The consequences of a routing mistake in this business are worse than in most others on this list. An incident detail routed to the wrong client's Slack channel or ticket queue isn't just an internal inconvenience, it's a confidentiality breach that can end a contract and create real liability.

Build a validation step that checks the client identifier on every incoming alert against a known list before any routing happens, and fail closed, meaning route to a manual review queue rather than guessing, whenever that identifier doesn't clearly match. Test this deliberately with a handful of malformed or unexpected identifiers before trusting it with live alerts, not just with the clean examples you built it against.

Protect client separation with these safeguards:

• Validate the client identifier on every incoming alert before it routes to any channel or ticket queue.
• Keep a log of every routing decision, including the identifier check that ran and its result, so a client can review it.
• Design for the worst case: an incident detail sent to the wrong client is a confidentiality breach that can end a contract and create real liability.

Neither tool should be making the actual call on whether an incident is contained or whether to notify a client of a breach; those are judgment calls for a security analyst, informed by data the automation surfaced. Use Make or Zapier to get the right information in front of the right person quickly, not to replace the decision itself.

Time-to-fill for technical hires has been running long across the industry¹, and a security team that's short-staffed is exactly the team that benefits most from automation doing the triage legwork, freeing a smaller analyst team to spend their time on judgment calls instead of manually sorting through every alert that comes in.

Build the handoff between automation and analyst deliberately: a queue with the right context attached, not a raw alert dump. An analyst who has to open three other tools to understand why an alert fired is losing exactly the time the automation was supposed to save, so include the client baseline, the recent history for that alert type, and the confidence level the filtering logic assigned, right alongside the alert itself.