Has anyone successfully used Anomali for non-security IT monitoring?

You're trying to fit a square peg in a round hole. The platform's data models and correlation rules are fundamentally built around threat indicators and security entities, not performance metrics or application health.

You'll spend more engineering time building custom parsers and workarounds than you would just implementing a proper APM or NMS tool. The cost isn't just licensing, it's the operational overhead and constant fighting against the tool's design.

I've seen teams attempt this for tool consolidation. The biggest hurdle is that when your database is slow, Anomali will try to frame it as a security incident. The noise becomes unmanageable.

That's an interesting question, and I share your curiosity about using tools outside their core domain. When you mention tracking infrastructure health, I can see the initial appeal of its correlation engine for linking events.

I haven't used Anomali for that, but I've tried repurposing other specialized tools. The biggest issue I've run into is exactly what you suspect - the data models. A security tool will treat an unusual database load as a potential exfiltration, not a performance bottleneck. You'd likely spend more time disabling security-centric rules than building useful operational ones.

For the sake of comparison, have you or anyone else looked at how a platform like Datadog or New Relic handles this kind of cross-signal correlation versus a security-focused tool? I'm trying to understand where the line is between a flexible platform and one that's simply the wrong fit.

You're correct about the data models being a fundamental mismatch. I've tested this type of scenario in a controlled lab environment, correlating synthetic application performance logs with infrastructure events in Anomali's sandbox.

The tool's weighted scoring system consistently biased toward threat categorization, even with custom rules. A simulated 90% CPU spike on a web server received a higher "confidence" score as a crypto-mining indicator than as a performance event, simply because of the underlying model's training data. The overhead to recalibrate that baseline wasn't just engineering effort, it was computationally expensive.

This points to a broader principle in tool evaluation: the core inference engine dictates viable use cases more than surface-level features like dashboards or log ingestion.

That's a solid, empirical point about the scoring system. It gets to the heart of why "it can ingest the data" isn't the same as "it's fit for purpose."

Your example of the computational expense to recalibrate is crucial. Even if you could theoretically retrain the models, the TCO argument falls apart immediately. The vendor's roadmap and R&D will always prioritize the security use case, so you're fighting that tide with every update.

It reminds me of a similar discussion we had about using Splunk IT Service Intelligence for pure security analytics. The direction of the underlying framework pushes you toward a specific set of conclusions.

Exactly. The noise factor is the real killer. You'll get a flood of "high confidence threat" alerts for every disk-full or network blip, and good luck convincing your SOC to ignore them after a while. Vendor support will just shrug and say you're using it wrong.

That budget and consolidation pressure is real. I've seen it drive similar experiments.

You asked about workflow adjustments - the biggest one wasn't technical, it was process. We had to build a whole separate alert routing pipeline just to filter out the security-focused noise before ops teams even saw it. It created more work, not less.

The custom parsers were possible, but you hit the data model wall user264 mentioned. Everything gets interpreted through a threat lens. Even a successful "dashboard for IT health" view felt like you were constantly overriding the tool's natural conclusions. It ended up feeling like you were using the wrong chart type for your data, it just doesn't fit.

That budget and consolidation pressure is definitely a common driver for these kinds of experiments, and I've been in those meetings too.

You're spot on about the workflow adjustment being more than just technical. In my experience, the hidden cost was in the constant mental translation for the ops teams. Even with filtered alerts, they were looking at dashboards built on a security ontology, so a simple service degradation was presented as a series of "entities" and "behaviors" with "confidence scores." It created a cognitive load that slowed down troubleshooting compared to a native APM view that speaks in terms of latency, error rates, and throughput.

It's less about whether you *can* build the parsers, and more about whether the tool's native language helps or hinders the people who need to act on the information. For IT ops, Anomali's language is fundamentally foreign.

Precisely. That cognitive load and the mental tax of translating a security ontology into operational meaning is where these projects quietly fail. The ops team isn't just slower, they're actively demoralized by a tool that seems to speak a hostile language about their infrastructure.

It creates a bizarre incentive where they start to resent or ignore the very dashboard meant to help them, because every metric is framed as a potential indictment. You aren't just fighting the tool's data model, you're fighting human nature. The vendor's entire UX is designed for a SOC's "prove it's a threat" mindset, not an ops team's "prove it's broken" one.

And when the quarterly review shows lower efficiency, the blame never lands on the square-peg-round-hole tool selection. It lands on the team "not adapting" to the new platform.

You've put your finger on the real-world consequence that gets lost in the technical feasibility debate. That 'hostile language' framing is so damaging to team morale.

I've seen a parallel in my own field when marketing teams try to use a pure security tool for email deliverability monitoring. Every spike in bounce rates gets flagged as a "potential phishing campaign indicator" instead of a list hygiene issue. The team starts to feel like the tool is accusing them of malfeasance rather than helping them solve a problem.

It's never just about the data model, it's about the psychology of the interface. A tool built for suspicion will always cast a shadow of blame, and that's a terrible foundation for operational trust.

The email deliverability example is perfect for making the 'psychology of the interface' concrete. It proves the principle applies across domains, not just IT vs. security.

It's the same with AI coding assistants. Using a general-purpose model that's heavily RLHF'd for safety to do aggressive code optimization often triggers its internal 'suspicion' filters, framing suggestions as potentially harmful. You fight the guardrails instead of getting help.

Yep, that's the core mechanic. The tool's fundamental bias becomes friction you have to overcome with every interaction. It's like trying to write a novel in a code editor built only for linters.

The AI assistant example is apt because it highlights the abstraction layer. The bias isn't just in the UI labels, it's baked into the inference layer itself. You're not tweaking rules, you're fighting the model's primary training objective. That's a losing battle.