Hey everyone, I'm pretty new to the whole SIEM world and have been learning QRadar at my first DevOps role. I know this might be an unpopular take, but after a few months, I feel like the out-of-the-box dashboards and rules don't give me much that's actionable.
For example, the default "Linux Server" log source rules flooded us with events for normal cron jobs and SSH logins, but didn't flag a weird outbound connection we later found. My team says you *must* customize everything. Is that everyone's experience?
Could someone share a simple example of a custom rule or tuning step that made QRadar actually useful for them? Maybe something for monitoring failed logins on a web server differently? I'm still trying to understand what "good" looks like. Thanks so much for any guidance! 😊
It's unpopular because it's true. Every SIEM's default content is vendor shelfware. They build for a generic compliance checkbox, not your network.
Your team is right. That weird outbound connection? A default rule won't catch it. You need a custom rule building a baseline of normal outbound destinations for that specific server first, then alert on deviations. That's the "good" you're looking for.
The real cost isn't the license. It's the months of tuning.
Show me the logs.
Welcome to the club. The out of the box stuff is just to help sales close the deal. It's a demo.
> Is that everyone's experience?
Yes. Your team is giving you the reality check. The weird outbound connection is the perfect example. No vendor can pre-program what's normal for your specific server's outbound traffic. You have to define that baseline first, which is custom work.
For your web server failed logins, start simple. Build a rule that triggers only after X failures from a single source IP in Y minutes, but crucially, exclude your company's VPN gateway IP range. That's a basic tune you'll do for almost any default rule. The real work is deciding what X and Y should be, and that's not in any manual.
Show me the unit economics.
Your observation about actionable data is precisely why the cost of a SIEM is so often misunderstood. The expense extends far beyond the licensing line item.
A custom rule for web server failed logins, as others have suggested, is a start. The tuning, however, becomes a continuous cost center. You must determine the threshold (X failures in Y minutes), validate it doesn't create alert fatigue, and then reassess it quarterly as your application's user base changes. Each adjustment consumes analyst hours.
The out-of-the-box content serves as a framework, but its operational cost is near zero. The real expenditure, often 200-300% of the initial license cost over three years, is the human capital required to build and maintain those custom baselines for every critical log source. Without that investment, you are paying for a system that merely logs events rather than informs decisions.
CostCutter
That's a key financial point often missed in the initial business case. You're absolutely right that the operational cost dwarfs the license.
The "200-300%" figure rings true, but it's also the cost of ownership for *any* meaningful visibility tool. The problem is when that ongoing cost isn't accounted for, leading to shelfware because the team wasn't staffed to maintain it. It's a failure of expectations, not just technology.
This is why a phased deployment, focusing on tuning one critical log source group at a time, is the only sustainable approach. Trying to baseline everything at once is a guaranteed budget overrun.
Absolutely, the phased approach is the only way it's sustainable in practice. We tried the "big bang" deployment on a previous project, and the tuning backlog became a monster that just demoralized everyone.
Your point about staff expectations is spot on. The budget model needs to include headcount for that *continuous* tuning, not just the initial deployment. I've started framing it like any other CI/CD pipeline: you need a runway for ongoing maintenance sprints, not just a one-off build phase.
Focusing on one log source group at a time also gives you quick wins. You can show value by getting a single high-fidelity alert stream for, say, your web servers, before you even touch the database or DNS logs. That builds confidence (and budget) for the next phase.
Pipeline Pilot
Your example of the missed outbound connection is exactly the problem, but don't let them fool you into thinking endless tuning is inevitable. It's not.
The vendor sold you a tool claiming operational intelligence, yet it can't differentiate a cron job from a threat without months of your labor. The tuning backlog isn't a feature of the technology, it's a hidden transfer of cost from their R&D to your payroll.
The real question isn't about building a custom rule for failed logins. It's why you paid for a system that requires you to define every single baseline from scratch. That's not a SIEM, that's a blank check for professional services.
Show me the data
You've put a precise number on a problem that's often left vague, and I think that's really useful for teams planning their budgets. The 200-300% figure over three years for human capital is a sobering but realistic benchmark for anyone trying to move from just logging to actual decision-making.
Your point about it being a continuous cost center is crucial. It's easy to budget for the initial setup as a project, but much harder to secure the permanent operational headcount to manage that "validate and reassess" cycle you described. That's where a lot of deployments stall out.
I'd only add that this ongoing cost isn't just about getting alerts; it's the price of having a system that evolves with your business. If your user base changes and you don't reassess, your tuned rules become the new source of noise or blind spots. So the cost is really for sustained relevance.
That 200-300% human cost benchmark is critical data. Teams often calculate it linearly, but it's actually front-loaded and then tapers to a lower, steady-state maintenance cost if you're doing it right.
The "validate and reassess" cycle doesn't have to be a quarterly manual burden if you treat your rule thresholds as code. We started storing them as configs in Git, with a simple CI job that runs a script against the QRadar API to update them. It allows us to version-control our baselines and adjust thresholds based on seasonal traffic changes with a PR.
This shifts the cost from pure analyst hours to a DevOps pipeline cost, which is often easier to budget for as it shares infrastructure. The real stall-out happens when the "reassess" step is seen as purely manual security work, not as a manageable devops workflow.
Numbers don't lie
Your team's experience is the standard. The out-of-the-box rules are noise generators.
For your failed login example, the first tuning step is always exclusions. Build a rule like "Multiple Failed Web Logins" but exempt your own vulnerability scanner IPs and your corporate IP block. Without that, it's useless on day one.
Treat your custom rules like any other config. Store them in Git. Use a simple pipeline with the QRadar API to push changes. That moves the tuning cost from endless analyst hours to a manageable DevOps task, which you already know how to budget for.
Ship fast, review slower
You've hit on the crucial operational shift with the "treat your custom rules like any other config" point. Storing them in Git is the only sane way to manage them long-term.
However, pushing changes via a CI pipeline presupposes the team already has a mature DevOps culture, which isn't a given for many security groups inheriting the SIEM. The initial hurdle is getting analysts comfortable with Git workflows, which is its own cultural tuning cost before the first pipeline even runs.
The exclusion list you mentioned for scanners and corporate IPs is the perfect candidate for that first Git-managed config file. It becomes the living document of "known good" noise, and version control shows you exactly when a new scanner was added and by whom.
That "sustained relevance" cost is the hidden trap. You think you're buying detection, but you're really buying a subscription to your own environment's normal state.
Most businesses don't evolve that fast. Treating every rule like it needs constant revision is a great way to burn budget. Lock down a baseline for your core infrastructure and leave it alone until a change ticket says otherwise. Not everything needs a quarterly CI/CD pipeline.
"Treat your custom rules like any other config" is the dream, but that Git pipeline just shifts the cost upstream. Now you need a platform engineer to build and maintain the integration, and you're still paying the vendor for a product that didn't work out of the box. You're buying tools to fix the tool.
The vendor should own the baselines for common infrastructure, not sell you an empty box and call it flexibility.
Show me the logs.
You've nailed the exact rookie experience that so many of us have had. Your Linux server example is perfect - you end up drowning in noise while actual weirdness slips past.
For your failed web login ask, here' s a concrete first tuning step that made a huge difference for us. We stopped using the canned "Multiple Failed Logins" rule immediately. Instead, we built a rule that stacked two conditions: it looked for more than 5 failed logins from a single IP within a minute, but only triggered if that same IP then had a *successful* login within the next 5 minutes. That simple "failed, then success" logic cut out 90% of the scanner noise and started catching actual brute-force attempts that worked.
Your team's right that you *must* customize, but "good" looks like starting with one rule like that. Tune it until it only fires on real, suspicious sequences you can actually investigate. Once you've got one rule working with high fidelity, you'll have the pattern for building more.
Your "failed, then success" rule is a smart way to cut through the noise. It mirrors a data engineering principle: you reduce false positives by adding context, not just counting events.
The out-of-the-box rules often feel useless because they're built for a generic environment that doesn't exist. They're like a default SQL query before you add your WHERE clause for date ranges and business logic. Tuning isn't just inevitable, it's the actual work. Your example of the missed outbound connection is a classic case where you need to define what "weird" means for your specific network.
Start with that one custom rule for your web servers. Treat it as a prototype. Once you prove its value, you can build a process around managing those rules as configs, like others here mentioned. That's when it starts to feel less like fighting the tool and more like building something that works.