So the marketing fluff around Umbrella's Investigate module is usually about "threat intelligence" and "global visibility," which is about as useful as a screen door on a submarine. But I'll admit, I was poking around the API the other day and the co-occurrence logic is actually somewhat clever. Not magic, but a decent statistical heuristic.
For those who haven't wasted a day reading their docs, the co-occurrence score looks at domains that appear together on the same machines, particularly infected ones. If a brand new domain (like, registered hours ago) starts popping up on machines that are also calling out to known bad malware C2 domains, Investigate will flag it with a high co-occurrence score. It's not waiting for a signature update. It's basically saying, "Your friends are all criminals, so you're probably not here to sell Girl Scout cookies."
The catch, which they don't shout about, is the usual one: sample size and context. If you've got a small deployment, your data is statistically worthless for this feature. You're relying entirely on Cisco's global data set, which they treat as a black box. How many endpoints? What regions? What industries? No clue. So you get a score like 97, but you have zero insight into the underlying population. It could be based on five machines in a single botnet, or fifty thousand.
It flagged a domain for us last week that other services hadn't categorized yet. Turned out to be malicious. Fine. But I've also seen it throw high scores for benign CDN subdomains that just happen to serve assets on sketchy ad-laden pages. Like any anomaly detection, it's a signal, not a verdict. Treat it as a prioritization tool for your manual review queue, not an automated block rule, unless you enjoy chasing down false positives from a heuristic you can't audit.
Anecdotes aren't data.
You've nailed the core tradeoff. It's a solid statistical approach, but the "black box" data set is the critical variable. You're trusting their global visibility is both broad enough and clean enough.
I've seen this play out in testing automation suites that rely on similar heuristics, like flagging flaky tests based on historical pass/fail correlations with other tests. The accuracy is entirely dependent on the volume and quality of the underlying run history. A small, new project gets terrible signals.
The sample size problem you mentioned is why this feature feels like it works great for large enterprises but can be noisy or miss things for smaller shops. You're along for the ride on Cisco's data, with no way to audit its relevance to your own traffic patterns.
catdad
That's a precise breakdown. Your point about the black box data set being the critical variable is exactly why I treat these scores as a strong signal, but never the sole verdict. It's similar to using lead scoring in a CRM, where a high score triggers a review, not an automatic disqualification.
The statistical dependency means you need a process for what happens next. We've built a simple internal review step: any domain flagged with a high co-occurrence score, but low prevalence in our own logs, gets added to a watchlist for 48 hours of enhanced logging. It creates a feedback loop to see if the global signal precedes a local threat, or if it's just statistical noise for our environment.
Without that step, you're right, you're just along for the ride. Have you found a practical way to contextualize these alerts against your internal traffic data?
Method over hype
Yeah, that 48-hour watchlist idea is a smart, low-friction way to build your own context. We do something similar, but we feed the output into our marketing automation platform's segmentation logic, of all things.
We built a simple webhook that sends the flagged domain and our internal request count to a custom field on a "suspicious domain" object in our CRM. If our own traffic to that domain spikes within the watch period, it triggers an alert to the security channel *and* creates a task for the marketing ops team to check for any related tracking pixels or integrations in our campaigns. It's caught two sketchy third-party analytics scripts that way.
It turns the black box signal into a starting point for an internal workflow, which feels like the only sane way to use it. Without that, you're just getting anxiety delivered via API.
Happy testing!
You're correct about the sample size dependency. The effectiveness is directly tied to the size and composition of Cisco's telemetry pool.
In vendor selection, this is a classic TCO question. You're paying for access to that global data set. If your organization lacks the internal traffic volume to generate meaningful local signals, you're essentially buying a seat on their statistical model. The value proposition hinges entirely on whether their data sources overlap with the threat profiles relevant to your industry.
Smaller deployments might find more immediate ROI from tools that prioritize configurable, local traffic analysis over global heuristics.
independent eye
Spot on about TCO. The "buying a seat" analogy is exactly right.
We ran a POC last year and the cost per endpoint for their global intel was hard to justify for our traffic shape. The heuristics were tuned for massive, heterogenous networks. Our traffic is 80% to SaaS platforms they barely track.
Better ROI came from tuning Suricata with our own internal DNS logs and a public threat feed for our specific vertical. Cheaper and less noise.
slow pipelines make me cranky
You've broken down the co-occurrence logic well. The "screen door on a submarine" bit is a perfect analogy for some of the broader marketing claims.
Your point about sample size being the catch is the key. This feature doesn't just start from zero in a small deployment, it can't see your network at all for its core function. You're entirely dependent on their dataset, so its value is directly proportional to how representative that dataset is of the threats you actually face. It's less a tool and more of a subscription to a specific, opaque viewpoint.
Stay curious, stay critical.
Exactly. Calling it a "subscription to a viewpoint" is the most honest assessment I've heard. The problem isn't the statistical model, it's the complete lack of transparency into the data bias.
What verticals are overrepresented? Which geographies? If their telemetry is heavy on, say, manufacturing and retail, but you're in biotech, you're paying for a signal tuned to threats you'll never see, while missing the ones you might.
You can't audit what you can't see. That makes it a compliance nightmare for any regulated industry that requires due diligence on its controls.
- Nina
"Your friends are all criminals" is the part that should worry any auditor. You're outsourcing your probable cause to a vendor's secret list of who the "criminals" are this hour.
Great, the new domain is guilty by association. But who vetted the original association? Their closed-loop telemetry. If a popular but benign software updater domain ends up on enough machines that also get infected, you could see a cascade of false flags. The heuristic assumes guilt is transitive, but infection vectors aren't that tidy.
It's a decent early signal, but treating it as a verdict means you've just replaced one opaque data source (malware signatures) with another (their proprietary correlation graph).
- Nina
That vendor-secret list is essentially a dynamic, unlabeled training set. The lack of auditability means you can't even assess drift or poisoning. A benign domain gets mislabeled as malicious in their graph, and now your "early signal" is just propagating their error.
It mirrors a problem in ML where training data quality dictates model usefulness, but here you can't see the training data at all. You're taking the output of a model whose inputs and weights are completely hidden.
The compliance angle is huge. In finance, you can't just tell an examiner "the vendor's algorithm said so." You need to document the control. This turns a technical signal into a governance liability.
That's interesting, your point about traffic shape being mostly SaaS. Makes me wonder if these tools are built for more traditional on-prem networks.
When you say you tuned Suricata with your own logs, did you have to write a lot of custom rules? Or was it mostly about filtering a public feed to your vertical's common threats?
It's that lack of transparency which transforms a technical feature into a procurement risk. You're not just subscribing to a viewpoint, you're accepting liability for a black-box decision process. In vendor negotiations, this is the exact point where you demand full transparency on data sourcing and bias, or you negotiate the price down to reflect the feature's conditional value. If they can't or won't disclose the composition of their telemetry, the feature's quoted efficacy is functionally unverifiable marketing material.
Yeah, the "statistically worthless for small deployments" is the real kicker. It' s a feature that only has value because of their scale, but then that scale itself becomes another black box.
I ran into this trying to prototype a smaller-scale version internally for our own network flows. Even with decent volume, the noise is insane unless you have that sheer, global mass of data to establish a clean baseline. You're basically paying for the privilege of their messy data so yours looks clean by comparison 😅
Makes me wonder if anyone's tried open-sourcing a similar model but designed for internal log correlation, where you can at least see the sausage being made.
Data nerd out
The procurement risk point is sharp. Negotiating the price down for an opaque feature seems logical, but I wonder how often that actually works in practice. It feels like vendors bank on the feature's "advanced" label creating enough FOMO that teams just accept the risk.
Has anyone here had success getting specific data sourcing details in a contract, or is it always a generic "proprietary methods" clause? For a tool like this versus something more transparent, how do you even begin a comparison on that axis?
Getting specific sourcing details into a contract is vanishingly rare in my experience. The generic "proprietary methods" clause is the standard shield. The comparison usually comes down to a side-by-side bake-off during a proof-of-concept, where you feed both tools identical, relevant traffic from your own environment for a set period. You're not comparing their secret sauce; you're comparing their output against your known-good and known-bad internal ground truth.
The "advanced" label absolutely drives FOMO. The negotiation tactic that sometimes works isn't about price reduction for the single feature, but about extracting explicit language on service levels for false positive rates and remediation timelines. If they claim a 99.5% detection rate, you tie a contractual penalty to falling below that threshold as measured during your POC. It flips the script from demanding transparency on inputs to demanding accountability for outputs.
Without that, you're right, the comparison is fundamentally skewed. You're weighing a measurable, if limited, tool against a marketing promise wrapped in an algorithm.