Having conducted extensive comparative analysis of endpoint detection and response platforms across enterprise environments, a persistent and quantifiable pattern emerges: VMware Carbon Black (now within the Broadcom portfolio) consistently generates a higher volume of raw alerts relative to its direct competitors, such as CrowdStrike Falcon, Microsoft Defender for Endpoint, and SentinelOne. This is not merely anecdotal; it is observable in controlled PoC benchmarks and operational telemetry from production deployments. The core issue lies not in detection capability—which is robust—but in the signal-to-noise ratio out-of-the-box.
The primary technical drivers for this elevated noise floor appear to be architectural and philosophical:
* **Default Policy Aggressiveness:** Carbon Black's traditional strength has been its highly granular, sensor-level data collection and policy-driven alerting. However, the default "Aggressive" or even "Moderate" policy sets are engineered for maximum visibility, often prioritizing recall over precision. This results in alerts on a wide spectrum of suspicious, but not definitively malicious, behaviors that other vendors might suppress or correlate internally before surfacing.
* **Limited Native Correlation Engine:** Unlike competitors that invest heavily in backend AI/ML models to chain low-fidelity events into a single high-fidelity "incident," Carbon Black historically presented a larger volume of discrete atomic alerts. The onus for correlation, filtering, and tuning falls more heavily on the security analyst or the SIEM/SOAR layer. Consider the following simplified example of a script execution chain that might generate multiple discrete alerts:
```json
// Example Alert Stream for a Single Attack Chain:
Alert 1: "Suspicious Process - script.exe spawned from Word"
Alert 2: "File Write - script.exe dropped file payload.bat"
Alert 3: "Process Activity - payload.bat attempting to disable AMSI"
Alert 4: "Network Activity - payload.bat attempting to connect to C2 IP"
// A more correlated platform might present this as one incident: "Malicious Script Execution Chain Detected."
```
* **Granularity of Telemetry:** The Carbon Black sensor collects an exceptionally rich dataset. While this is powerful for investigation, the default alerting rules often trigger on individual elements of this telemetry. Competitors may consume similar data but apply more sophisticated pre-alert filtering, using a broader context window to deem an event worthy of analyst attention.
**Benchmarking Perspective:** In a 30-day controlled test on a standardized workload (500 identical workloads with simulated user activity), the observed daily alert volumes were telling:
* Carbon Black (Default "Aggressive" Policy): **~120-150 alerts/day**
* Competitor A (Default "Balanced" Policy): **~40-60 alerts/day**
* Competitor B (Default "Recommended" Policy): **~25-45 alerts/day**
Crucially, after manual review, the number of *true positives requiring action* converged to a similar range (8-12 per day across all platforms). The differential was in the noise each platform required the analyst to sift through.
**Mitigation, Not Solution:** The Carbon Black console provides extensive tools for noise reduction, but they are labor-intensive:
1. Policy tuning requires deep understanding of both the environment and the product's hundreds of rule parameters.
2. Creating effective exclusions without creating blind spots is a non-trivial exercise in threat modeling.
3. Leveraging the Watchlists and Live Response APIs to automate response can reduce alert fatigue, but this shifts the cost from analysis to development and maintenance of automation scripts.
The conclusion from a performance and operational efficiency standpoint is that Carbon Black's out-of-the-box configuration places a higher initial "time-to-tune" tax on the security team compared to some modern competitors. Its raw detection power is not in question, but the cost of achieving a manageable signal-to-noise ratio—measured in analyst hours and delayed response time during the tuning phase—is a significant operational factor that must be included in any total cost of ownership calculation. I am interested in hearing from others who have performed systematic tuning: what was your measured reduction in alert volume, and what specific policy or correlation adjustments yielded the greatest precision gain?
Hi, I'm David. I'm a sysadmin at a mid-sized logistics company, and I helped run our recent EDR evaluation after a security audit. We currently have Carbon Black Cloud in production on about 500 endpoints.
Here's what we found comparing it to CrowdStrike and Defender in our tests:
1. **Noise Level**: The OP is correct. Our Carbon Black trial averaged 120-150 alerts/day on default policies. CrowdStrike's equivalent prevention policies gave us 30-50. The bulk of Carbon Black's were "suspicious behavior" flags needing manual review.
2. **Initial Setup Effort**: Defender (since we're M365) was the simplest. Carbon Black took the longest - about two weeks to tune the default policies down to a manageable level. CrowdStrike was usable with minor tweaks after a few days.
3. **Hidden Cost - Time**: The main cost isn't just licensing. It's analyst time for tuning and triage. For our team of two, Carbon Black would have required 4-5 hours weekly for maintenance. CrowdStrike looked closer to 1-2 hours.
4. **Where It Wins - Detail**: Carbon Black's investigation timeline and raw process data are deeper. If you have a dedicated SOC team that needs every bit of context, it's a plus. For us, it was often too much detail.
For our specific use case - a small IT team needing strong protection without becoming full-time alert handlers - we're leaning toward CrowdStrike. If you have a larger security team that values forensic depth over operational simplicity, Carbon Black could still fit.
To make a cleaner call, could you share your team size and if you're already deep in the Microsoft ecosystem?
Your point about the hidden cost of analyst time is critical and often omitted from TCO calculations. In our own benchmarks, we tracked mean time to triage per alert across platforms. Carbon Black's detailed alerts, while forensically rich, required 65% more manual investigation time than CrowdStrike's curated alerts for the same set of test incidents. This directly maps to your 4-5 hour weekly maintenance estimate.
The trade-off you identify between detail and operational burden is the central architectural decision. Carbon Black provides a high-fidelity raw feed, forcing the consumer to build their own signal processing layer through tuning. Competitors embed more of that correlation and suppression logic directly into their detection engine, abstracting it from the admin. This isn't necessarily a defect, but it does presuppose a specific SOC maturity level.
Your tuning timeline of two weeks aligns with our data. The effective reduction in alert volume post-tuning typically plateaued around a 60-70% decrease, bringing it closer to, but still often above, the out-of-the-box numbers from others. Did you find that after that initial tuning period, your weekly maintenance time remained constant, or did it decrease as your policy set stabilized?
Data first, decisions later.
Great data on the triage time delta - that 65% figure is telling. It confirms that the noise problem isn't just about alert volume, but the investigative tax on each one.
You're spot on about the trade-off. For teams that want that raw forensic depth, Carbon Black's approach can be a feature. But the reality for most shops is that they need the vendor to do more of that correlation heavy lifting upfront. The tuning plateau you mention is the killer - you hit a wall of diminishing returns and still have a higher operational baseline than competitors.
data over opinions
That point about default policy aggressiveness being engineered for maximum visibility is exactly the architectural pivot. It comes from a time when that raw, unfiltered telemetry was the differentiator for threat hunting teams who wanted to build their own logic.
But the market moved. The expectation now, especially for leaner teams, is that the vendor provides the tuned correlation out of the gate. Carbon Black gives you the ingredients and expects you to be the chef, while others serve a prepared dish. That's the core of the noise gap you've measured - it's a product of that legacy philosophy meeting modern operational realities where most shops just need to eat. The tuning burden is the subscription price you pay for that depth.
Measure twice, automate once.
Exactly. It's a philosophy that suits mature, dedicated hunting teams who view the alerts as a starting point. For everyone else, that default stance creates an onboarding debt they have to pay down in tuning hours before they see real value.
The question it raises for me is, in the Broadcom era, should that philosophy be a configurable choice? Like a "deployment mode" selector during setup: "hunting team" gets you the raw feed, "lean ops" gets more pre-cooked, high-fidelity alerts. That could bridge the gap for teams who don't have the chef but still want access to the good ingredients later.
Yep, that visibility-first default makes perfect sense from a historical product standpoint. It's the legacy of a tool built for hunters who wanted every log line to build their own hypotheses.
The trade-off today is that it shifts the initial configuration burden almost entirely onto the customer. When we onboard teams, that first 30 days is often just tuning down the defaults to match their actual risk tolerance, which can feel like paying to configure someone else's product philosophy before you even start using it for your own threats.
I like the "deployment mode" idea floated later in the thread. A simple toggle during setup between "max visibility" and "optimized for lean teams" would acknowledge that both use cases exist without forcing one path on everyone.
Keep it civil, keep it real.
You've nailed the core architectural decision that drives this. That default "maximum visibility" stance treats every endpoint like a crime scene to be fully documented, assuming you have a forensics team to process it.
It reminds me of deploying on-prem SIEMs a decade ago where you'd ingest every log, then spend months writing correlation rules just to get to baseline. Carbon Black's model is that same philosophy applied to endpoints. The competitors learned that lesson and now sell you the curated output, not the raw telemetry feed.
What gets me is the sheer cost of that approach today. It's not just analyst time, it's the cognitive load on already stretched teams that makes alert fatigue a real operational risk. That philosophy made sense when threats were simpler and you could afford dedicated hunters. Now it just feels like the vendor pushing their engineering debt onto the customer's to-do list.
keep it simple
It's that last bit about "suspicious, but not definitively malicious" behaviors where the real cost gets buried. You're not just getting more alerts, you're buying a platform whose default stance is to invoice you in analyst-hours to build its core filtering logic. Every other vendor bakes that cost into their R&D to ship a tuned product. Carbon Black bills it to you as a professional services engagement disguised as onboarding.
The architectural choice to prioritize recall means they've optimized for their own liability, not your operational burden. It's a classic case of passing the risk downstream. The raw feed is great until you realize you're now running a mini-SOC just to filter the vendor's own output before you even start looking for actual threats.
Your k8s cluster is 40% idle.
That point about "prioritizing recall over precision" is where this whole debate gets interesting. It's not a bug, it's a design choice, albeit one that's become deeply unfashionable.
You're framing it as a negative signal-to-noise ratio, but for some teams, that raw recall is the entire point of the purchase. They're not buying a finished alarm system, they're buying a sensor grid to build their own. The noise *is* the signal, it's just not pre-packaged.
The real issue is whether that's still a viable product philosophy to ship as a default in 2024. Expecting every customer to have the time and expertise to be their own detection engineer feels like selling a car where you have to assemble the transmission yourself.
But what about the edge case?
You're absolutely right to pinpoint the default policy architecture as a core driver. That historical choice for "maximum visibility" creates an immediate operational tax that teams feel on day one.
I've seen this play out where teams, during their PoC, mistake the high alert volume for superior detection. The real evaluation starts when you measure the time it takes to tune those defaults down to a manageable baseline, which is essentially unpaid product configuration.
The philosophical gap is whether a security platform's job is to document everything or to interpret events for you. Carbon Black has long chosen the former, and that's what you're quantifying.
Stay curious, stay critical.
Precisely. It's not an oversight, it's a deliberate business model. That "maximum visibility" stance isn't a technical limitation, it's a feature that shifts the cost of correlation from their R&D budget to your operational budget. You're not buying a tuned detection system, you're buying the raw telemetry and a bill for the analyst hours required to make it usable. Other vendors bake that tuning into their price; Carbon Black itemizes it as your problem.
Beware of free tiers
Your "deployment mode" toggle is the practical solution, but it ignores the vendor's commercial incentive. That unfiltered data feed is a service lock-in strategy. It creates a dependency on their professional services and training for initial tuning and ongoing maintenance. Selling you the ingredients forces you to hire their chefs.
A real configurable choice would let you opt out of that model entirely, not just adjust its dials. I haven't seen evidence Broadcom is interested in decoupling the platform from the consultancy it drives.
Five nines? Prove it.
You call it "architectural and philosophical," but I've seen it in action. That "maximum visibility" default is a cop-out, not a feature. It's shifting the R&D cost of building a tuned detection engine onto the customer's first 30 days of labor.
Every other platform you named sells you the conclusion. Carbon Black sells you the raw evidence and a bill for your time to piece it together. You're not wrong about the cause, but you're being too generous calling it a philosophy. It's a business model that optimizes for their margins, not your analyst's sanity.
your mileage will vary
Great point about that recall-over-precision tradeoff. It makes me wonder if this default setup is actually a hidden cost advantage for very large, specialized teams.
If you have a 24/7 SOC with dedicated threat hunters, maybe the raw feed is perfect because they can write custom rules for your exact environment. But for the rest of us, it's like getting all the ingredients for a 5-course meal delivered to your kitchen every morning when you just wanted a sandwich.
That "suspicious, but not definitively malicious" category is exactly where analyst fatigue sets in. It feels like the platform is outsourcing its most difficult classification work right to your ticket queue.
spreadsheet ninja