Switched from Anomali to a SOAR platform - our automation success rate tripled.

avag2 · 2026-07-31T01:26:00Z

We ran Anomali for nearly two years, primarily for alert correlation and threat intel ingestion. Our initial hypothesis was that a dedicated TIP would streamline our Tier 1 analyst workflow. The reality, after extensive instrumentation and logging, was underwhelming. Our measurable automation success rate—defined as alerts processed to resolution without human intervention—hovered around 15-18%. The bottleneck wasn't the intelligence, but the actionable response. The core issue was the gap between "seeing" and "doing." Anomali would correlate and enrich alerts effectively, but the jump to containment, ticketing, or even simple host isolation required manual scripting and API glue that became a maintenance nightmare. We tracked this by logging every alert that entered the platform and its final disposition. The data was clear. We conducted a three-month parallel run, routing a copy of our high-fidelity alerts to a SOAR platform (specifically, Palo Alto Cortex XSOAR). The difference wasn't incremental; it was foundational. By the end of the trial, our automation success rate for the same alert stream was consistently above 60%. The key differentiators were: * **Native, Low-Code Playbook Design:** The visual playbook builder wasn't just a gimmick. It allowed our senior analysts to directly map logic and decision trees. In Anomali, this required a developer to write and maintain Python scripts. * **Integrated Action Library:** The SOAR came with pre-built, vendor-supported integrations for our EDR, firewall, and ticketing system. In Anomali, we were writing and maintaining custom API connectors for every single action, which would break on vendor updates. * **Stateful Context Management:** A SOAR playbook can hold context (like a suspect IP, user, and host) across dozens of steps and conditional branches. Replicating this in Anomali's workflow was conceptually possible but brittle and complex. Here's a tangible example. For a "ransomware behavior detected" alert from our EDR: **Anomali Workflow:** 1. Alert ingested, enriched with Anomali ThreatStream intel. 2. Custom script runs to query the EDR for the host's network connections. 3. Another script attempts to isolate the host via the EDR API. 4. A third script creates a Jira ticket. 5. Each step was a separate, logged script. Failure at step 3 meant step 4 never ran, and the ticket lacked context. We had to build error handling into every script. **SOAR Playbook Equivalent (simplified):** ```markdown 1. Receive EDR alert → Extract hostname, user, process hash. 2. Enrich hash with VirusTotal (built-in integration). 3. If VT detection > 20, then: a. Isolate host via EDR (pre-built action). b. Block extracted IOCs on firewall (pre-built action). c. Create incident ticket with all enriched data (pre-built action). 4. If isolation succeeds, update ticket with status; if fails, escalate to on-call via Slack. ``` The entire process is a single, traceable, stateful container. The context (hostname, IOCs, VT score) flows through automatically. The cost analysis was the final nail. When we factored in the engineering hours spent maintaining our custom Anomali automation scripts versus the SOAR subscription, the TCO was already comparable. The tripled automation rate simply made the ROI undeniable. We've since repurposed Anomali strictly as a threat intel feed for the SOAR, which it performs adequately. For teams looking primarily at correlation and intel, a TIP has its place. But if your goal is measurable automation and reducing mean time to respond (MTTR), the data from our controlled experiment strongly suggests a dedicated SOAR platform is in a different league. The metrics shifted from "alerts correlated" to "actions taken autonomously," and that's what ultimately reduces analyst burnout and risk.

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Harper James

(@harperj)

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31/07/2026 4:01 pm

You're spot on about process engineering being the prerequisite, not the outcome. I've seen teams treat the visual builder like a blank canvas and end up recreating the same spaghetti logic they had before, just with nicer lines and boxes.

That six-month training timeline tracks with what we've observed. The teams that skip that foundational work tend to hit a wall where their automation rate plateaus because their playbooks are too brittle to handle real-world entropy.

What was the most effective part of your training? Was it formal curriculum, hands-on labs with failure injection, or something else?

Keep it constructive.

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