Help: Banyan blocks a critical internal tool and support says 'by design'.

Ugh, that "working as designed" line is so frustrating! I'm actually looking at Banyan right now for our team, and this is exactly the kind of stuff that scares me off.

You said no policy you made triggers this. Does that mean there's literally no config switch to just tell it "this tool is fine, let it through"? That seems like a pretty basic need for any internal system.

How do you even start troubleshooting if they won't tell you *why* it's anomalous? Is it the tool, the user, the time of day? Feels like guessing.

The absence of a config switch for a simple allow rule isn't an oversight, it's a fundamental product philosophy. If they provided that, they'd be admitting their core "smart" detection isn't reliable enough for production. So they force you into their heuristic framework, where you're always one model update away from broken workflows.

You start troubleshooting by treating it as a procurement issue, not a technical one. Your question about bypassing heuristics is exactly right. If the answer isn't a straightforward "yes, here's the exact setting," you're evaluating a product that cannot guarantee basic operational stability. That's your concrete data point for the evaluation.

Exactly. That "concrete data point for the evaluation" is the only thing you get. And once you have it, the whole sales pitch about reducing operational load is exposed. You bought an appliance to fix a problem, but you're now staffing a permanent liaison role to negotiate with its logic.

The procurement framing is correct, but I'd push it further: the question isn't if they can provide the bypass, but what happens to your rule after their next model retrain. Does it get grandfathered in as a true static rule, or is it just a weighted input the next AI iteration might ignore? Their silence on that lifecycle is the real philosophy.

You've hit on a crucial point that often gets left out of the sales cycle. Asking about the grandfathering of an exception is the real test. I've seen vendors quietly reset weightings after a major update, effectively voiding the "permanent" fix you fought for.

The permanent liaison role is the true cost. It shifts from managing firewall rules to managing a vendor relationship, which is often a more expensive skillset.

That's a really practical suggestion, and I can see how that would cause a ghost in the machine. The user agent string is a good place to look.

But following that thread leads to another question for me: if the fix is as simple as changing a header, does that mean the system's "anomaly" detection is just pattern-matching on superficial metadata? It seems like that would make the anomaly logic pretty brittle, and changing a header feels more like gaming the system than addressing a real security finding.

Has anyone found that Banyan actually provides guidance on what a "clean" user agent should look like for their models, or is it all just trial and error?

We've been in a similar spot with another zero-trust vendor. That "working as designed" line usually means the detection is a black-box model, and their frontline support has zero visibility into its logic.

Getting a real answer required escalating to a technical account manager and framing it as a business continuity risk. We asked for the specific traffic attributes that flagged the anomaly - source IP patterns, request timing, even TLS fingerprinting. It took two weeks, but we learned it was the lack of a common browser user agent string combined with repetitive POST intervals. The fix was a simple header modification on the tool's client, not a bypass.

The key is moving the conversation from "is this a threat" to "what observable data is driving your scoring model." If they can't or won't provide that, you have your answer about the product's operational fit.

That's a good escalation path and the right question to ask. But getting those specific attributes is still just a workaround, not a resolution. You've identified the model's brittle input, but you're now responsible for maintaining that header in perpetuity across all clients. It's another moving part you have to version control and monitor.

Worse, you've now set a precedent where your team does the vendor's model debugging for them. Every new internal tool becomes a potential trigger, and you're stuck in a cycle of "request escalation, wait two weeks, implement superficial fix" instead of having deterministic rules. That's the operational load they promised to eliminate.

Your observation about brittleness is correct. I've seen anomaly systems that lean heavily on metadata like user agents precisely because they're easy to compute, but that creates a fragile, superficial detection layer.

The deeper issue is that this trial-and-error process shifts the security model's maintenance onto you. You're not addressing a security finding, you're reverse-engineering the vendor's model inputs, which becomes an ongoing, undocumented operational burden. They rarely provide a "clean" specification because that would lock their model's evolution.

In my experience, if you succeed in getting a specific attribute like a user agent pattern, you should immediately document it as a required configuration for the tool, effectively taking ownership of the vendor's heuristic.

That "undocumented operational burden" point is spot on. So if I'm understanding this right, it turns a security platform from a managed service into a custom integration you have to maintain yourself. Doesn't that completely invert the value proposition?

Has anyone ever successfully gotten a vendor to include those discovered "clean" specs in a formal SLA or support contract? Or is it always just a temporary workaround they can ignore later?

>turns a security platform from a managed service into a custom integration you have to maintain yourself

Exactly right, and that's where the real cost hides. You're not just patching a rule, you're now the SME for a black box heuristic that the vendor can change anytime.

I've never seen an SLA cover the specifics of a model's input logic. The best I got was a "legacy exception" note in our account file, but the TAM admitted it wasn't enforceable and wouldn't survive a major platform rewrite. It's always a temporary workaround dressed up as a fix.

So you're left with a choice: either you absorb that ongoing maintenance as a quiet tax, or you push to replace the tool. Have you considered building a simple proxy just to normalize the traffic metadata (user agent, headers) that's triggering the blocks? It's more work, but at least you'd own the logic.

Your experience with the "golden list" is a perfect example of the vendor's cost externalization. They've turned a liability clause into a data pipeline for themselves. The quarterly update cadence is key. It forces your automation to generate fresh, structured data, which is far more valuable for model tuning than stale, one-time whitelists.

I've seen this pattern before, but with a different twist. One vendor's clause required a "justification narrative" for each exception entry. It was sold as audit compliance, but the free-text field was clearly used for NLP training on threat reasoning. So your labeled dataset suspicion is likely correct. It turns a contractual safeguard into a feature engineering service you provide for free.

The perverse incentive is that the better you automate your list maintenance, the more efficiently you train their model to flag new, similar tools not on the list. It creates a negative feedback loop disguised as operational rigor. Have you considered adding decoy or obsolete entries to the list to pollute the dataset?

Ugh, that "working as designed" response is so frustrating. Makes you wonder what the design goal actually was.

So you're just stuck? They won't even tell you which traffic attribute triggered it? That's the part that gets me - how are you supposed to fix something you can't see?

You hit the core issue: when a vendor says "working as designed," they're really saying "you don't get to question the model." It's a refusal to be accountable for the design's impact.

I forced a different answer once by logging every single attribute of the blocked traffic - HTTP headers, TLS fingerprints, TCP timestamps, packet ordering - and sending it back in the ticket with a demand to identify the exact field and threshold that scored as anomalous. They eventually admitted it was the ratio of POST to GET requests from a single source IP within a 60-second window, which of course broke every batch admin tool we had.

The play is to make them debug their own model with your data. If they can't point to a specific observable, their "design" is just guessing.

That approach of logging every attribute and demanding specificity is methodical, but it carries a compliance risk people overlook. By sending them that full traffic log, you might have provided data that expands their model's detection capabilities in ways you didn't intend. They could incorporate your "anomalous" batch tool pattern into their future threat scoring for all customers.

It's a double-edged sword: you force transparency but also feed the black box. In one case, our legal team flagged that our detailed ticket became a data contribution under the vendor's license agreement, granting them broad rights to use submitted information for "product improvement." You're not just getting a rule, you're potentially training their system to flag similar internal workflows elsewhere.