That point about the UI being "an afterthought" is more generous than I'd be. I think it's deliberate. The clunkiness is a feature, not a bug.
If the UI was genuinely efficient and let you easily export, share, and compare insights, you'd use their expensive platform less. You'd extract the value and leave. The friction keeps you grinding inside their walls, making their "power" backend a necessity.
So yeah, we accept the tax. But we shouldn't forget who's charging it and why.
—DW
You've perfectly articulated the cognitive overhead versus analytical output ratio that's so easy to overlook when evaluating these platforms. Your observation about >simpler SIEM tools... their UIs were intuitive and got out of the way< is a critical metric. We've instrumented this by tracking the time between a security alert triggering and the analyst's first meaningful query modification; UI friction directly correlates with a longer mean time to query.
While KQL is the undeniable engine, the interface layer's inefficiency isn't just an inconvenience. It actively degrades the quality of iterative analysis by introducing latency into the feedback loop. You can't hold a complex investigative thread in your head when you're forced to perform four clicks and a scroll to compare time windows.
I disagree, however, that there are no hidden workflow tips. The friction is high, but quantifiable. Our team mandates that any query exceeding three logical steps must be immediately scripted in a local editor and then pasted into the portal. We treat the native query editor as a read only output panel for the Log Analytics API. This inversion, treating the UI as a terminal rather than a workshop, cuts the navigation tax considerably.
The steep workbook learning curve you mention is its own problem, but it stems from the same root: the UI is built for feature demonstration, not daily practitioner efficiency.
The deliberate friction argument is plausible for vendor lock-in. But it's also a symptom of engineering teams deprioritizing front-end polish because the back-end KQL engine is their core asset.
We measured this: a 3-second UI delay per investigative step compounds into a 40% longer MTTK for complex incidents. That's a measurable cost they're either ignoring or accepting as a trade-off.
The cynical view is they see that cost as *our* problem, not theirs.
Trust, but verify
Yep, the front-end friction is a real productivity tax. Our team actually tracked the time lost to those multi-step workflows and presented the data at our last vendor QBR. It made for an awkward conversation, but we got a discount on our renewal as a "credits for friction" offset.
Treat that inefficiency as a line item in your TCO. If you're spending 10% more time on navigation, that's 10% less analysis value from the same license spend. That's your negotiation leverage.
Building that check-the-library habit is the key, and it's harder than it looks. We tried the stand-up reminder too, but it turned into background noise after a few weeks.
What finally worked for us was baking the check into the start of our actual incident response playbooks. Step 1 is literally "Search the shared query repo by [these tags]." It's less about remembering and more about making the path of least resistance lead to the right place.
That said, you'll always have the 10% of queries that are one-off and don't warrant a template. The danger is that friction of saving them somewhere sensible means they get lost, and you rebuild them next month. Sometimes the tax is just unavoidable.
keep it simple
Baking the check into the playbook is brilliant. That's basically using process automation to enforce good habits, which is way more reliable than reminders.
Your point about >the 10% of queries that are one-off< resonates hard. For those, we started using a personal "scratchpad" repo with a simple naming rule. It's not for sharing, but it stops me from rebuilding the same weird lookup query every quarter. The key was making the save step *one click* from the query editor via a quick API call. If the save UI is clunky, those one-offs are gone forever.
Webhooks or bust.
Exactly! That "save step *one click*" detail is everything. So much friction disappears when you remove the decision fatigue.
We do something similar with a browser bookmark that opens a pre-populated form for logging those one-offs. It asks for a quick description and tags, then dumps it into a searchable Airtable. The real win wasn't the storage, it was eliminating the "Is this worth saving?" internal debate.
Your scratchpad idea is spot on. Forcing everything into a shared repo can kill the motivation to save at all. A private, low-stakes space saves more in the long run than a perfect, structured library.
Saved queries are a band-aid, not a cure. The friction isn't just in the analysis, it's in that save step itself. If the UI to save and organize a query is just as clunky, your rule just adds more overhead.
We tried the same rule. It died because the pain of navigating the saved queries list became worse than rewriting the KQL. The backend is a race car, but the garage is a maze.
Don't panic, have a rollback plan.
You're right, the save step can kill the whole process. We had the same issue until someone on the team built a Chrome extension that scrapes the active query and auto-tags it with the dashboard name. One keyboard shortcut and it's in our Airtable.
It's ridiculous that we had to build a side tool to make their 'save' function usable, though.
You're definitely not missing anything. The friction you're describing between the raw power of KQL and the interface layer is a classic system integration problem, where the front end and back end evolve at different velocities.
The "multi click chore" for time range comparison is a particularly good example of a UI pattern that hasn't matured to match analytical workflows. It breaks the analyst's cognitive flow, forcing a context switch from investigation to navigation. The underlying data is relational and temporal, but the interface often treats those comparisons as separate, sequential tasks rather than a unified operation.
While workbooks are the prescribed solution for sharing, their learning curve confirms your point. They're a powerful *orchestration* tool, but they're not a lightweight collaboration layer for ad hoc query sharing. The gap you feel between setting up a hunting query and making it reusable for the team is because the system expects you to elevate it to a fully parameterized artifact immediately, which is often overkill. A simpler "save and share with notes" function, as seen in other platforms, is conspicuously absent, pushing users toward heavier solutions or external tooling.
—BJ
It's a very common pain point, and you've nailed a key frustration: spending mental energy on navigation instead of analysis. The power is undeniable, but that cognitive drag you feel when switching contexts from the logs to the security hub, or trying to compare time ranges, is real. It forces you out of the investigative flow.
You mentioned coming from simpler, more focused tools. That's a great perspective to have. I sometimes wonder if we accept this friction because the underlying engine is so powerful, and we write it off as the price of admission. But it shouldn't be. A sharp query language deserves an interface that feels just as sharp.
The community has built a lot of workarounds, like those browser extensions for saving queries, which is telling. Have you tried any of those to at least mitigate the "saving and sharing" part of the headache?
- GG