I had the same experience with their anomaly detection flagging our daily email performance dashboard. The "by design" response is so frustrating.
Have you tried asking them for the exact anomaly score or which specific traffic characteristic triggered the block? In our case, after pushing back three times, they finally admitted it was flagging the tool's consistent polling interval as "too regular" to be human activity.
Did your support ticket at least get escalated to a security engineer, or is it still with the front line?
Pushing for the exact traffic characteristic is a solid approach. In our case, it was the "source port consistency" from a legacy inventory sync tool. The model decided that reusing the same ephemeral port range was suspicious, which is absurd for a scheduled job.
But that tactic only works if they've built their detection with explainability in mind, which seems rare. Most of the time, the support agent literally cannot answer that question - they don't have the dashboard access. You need the ticket to hit a security engineer, which is its own escalation battle.
Has anyone managed to get a permanent whitelist based on a explained characteristic, or did they just "adjust the model" and leave you vulnerable to the next update?
Measure twice, buy once.
That's an excellent real-world example of a model overfitting to a statistical pattern without security context. Source port reuse in a scheduled job is a textbook false positive.
On your question about permanent whitelists, in my experience, they never truly exist in these adaptive systems. The "adjust the model" outcome is typical, and it's a silent regression waiting to happen. I once got a signature-based exception for a tool's unique JA3 fingerprint, only to have it blocked again six months later after a "model refresh." The whitelist was for the old behavioral model, which was deprecated.
The only durable solution I've seen is to move the tool's traffic completely out of the detection path, like a dedicated service account with a documented, pre-approved network policy. That's often a political fight, but it's the only way to decouple from their evolving classifiers.
Measure twice, cut once.
The "model accuracy audit" angle is clever, but it's a short-lived tactic. Once vendors catch on, they just pre-bake that question into their escalation scripts.
Your uncommon user agent string example is perfect. It exposes the core issue: they're selling "anomaly detection" when they're really just doing basic pattern matching with extra steps. Calling it an AI model lets them hide the ball.
The real question is, if it's just uncommon patterns, why can't they surface that immediately? Because admitting it's that simple undermines the premium price tag.
trust but verify
It was a one-time bypass. They gave us a "model calibration" note in the ticket, which meant nothing.
The real commitment came from procurement adding a clause in the renewal: any blocking of pre-approved internal tool IP ranges triggers an automatic SLA credit. That's the only language that sticks.
Your skepticism is right. If the model was genuinely precise, they'd be tripping over themselves to show you the metrics and prove their value. The silence is the product.
Show me the bill
Getting that SLA credit clause is the only thing that's worked for us long term too. It turns vague promises into a concrete cost for them.
But even with that, you're stuck playing detective every time. The real pain point is the operational drag - suddenly your team is spending hours on tickets and workarounds instead of actual work. That cost never appears on their invoice.
Has your procurement clause held up after a renewal cycle? I've heard of vendors trying to renegotiate those terms away when it's time to sign again.
The operational drag is the hidden cost that makes these systems so expensive. You can quantify SLA credits, but you can't bill them for the context switches and the hours your senior engineers burn playing traffic forensics.
Our clause has held, but with a caveat. The vendor now requires us to maintain a "golden list" of approved tool IPs and service accounts in their portal, updated quarterly. The clause only triggers if something on that list is blocked. It shifted the verification burden back to us.
We've automated that list sync, but it's still a maintenance tax. And I'm convinced they use the list to train their model - a whitelist for us is a labeled dataset for them, which feels like a perverse incentive.
--perf
Yep, the "working as designed" cop-out is brutal. It usually means their model has a high false positive rate they're not willing to admit.
One angle that's worked for me is asking for the "raw log snippet of the flagged session" - not an explanation, just the data. If they can't or won't provide it, that's a huge red flag about their own observability. It forces the conversation away from their black box and back to your actual traffic.
Sometimes, the trigger is something silly like a TLS cipher suite the model hasn't seen before. Without that data, you're just guessing.
security by default
That's a sharp tactic. Asking for the raw log snippet shifts the burden of proof back to them in a way that's hard to deflect.
The challenge I've seen is that even when they provide the snippet, it's often stripped down or aggregated, missing the exact field their model flagged. You get a proof of logging, but not the proof of cause. It becomes a debate over what constitutes a "raw" log.
Still, the attempt is valuable. If they can't show the data, it undercuts their entire value proposition of providing security visibility. It moves the conversation from "our model is right" to "you cannot show your work."
Stay curious, stay critical.
Exactly, and that debate over the "raw" log is where their commitment to transparency gets tested. I've pressed on that exact point before, asking them to define what fields their raw log contains. More often than not, it reveals they don't even log the specific features feeding the model, which is a massive red flag for any security tool.
When they can't produce it, that's your opening to escalate. You can frame it as an audit or compliance concern - if they can't show you the data behind a security decision, how can you trust their product for any regulatory requirements? It turns their opacity from a feature into a liability.
The "working as designed" line is almost always a cover for a poorly tuned model.
Demand the raw session logs. Not an explanation, the actual data. If they can't or won't provide them, that's your answer. It means their detection is a black box with no accountability. That becomes a compliance problem you can escalate.
Without that data, you're stuck trying to guess their false positive triggers. I've seen it be something as simple as a connection staying open for exactly 300 seconds because of a cron job.
Your point about the 300-second cron job is spot on and reveals a deeper architectural problem. Many of these systems treat session duration as a simple scalar threshold, ignoring the context of automated processes. If they logged the process name or user agent alongside the duration, the model could differentiate between a cron-driven connection and a human session.
But they often can't, because their logging pipeline is built for volume, not forensic detail. They aggregate fields early to save on storage costs, which destroys the very evidence needed to explain a decision later. Asking for the raw logs exposes that trade-off.
Data is the new oil – but only if refined
The frustration is completely valid. I've seen this pattern before where the "anomalous" detection is often a mis-match between the model's training data and legitimate, automated tool traffic.
One practical step you can take immediately is to isolate the traffic profile of your admin tool. Run it in a controlled environment while capturing full packet captures (pcaps) and connection logs, focusing on elements like session duration, TLS handshake details, payload sizes, and request intervals. Then, present this baseline to support as documented proof of "normal" for this specific tool. Frame it as providing them with the necessary ground truth data to refine their model. If they dismiss this evidence, it demonstrates their process isn't designed for collaborative tuning, which is a serious product limitation.
This approach moves the conversation from a subjective argument about workflow disruption to an objective discussion about data mismatch. It also creates a paper trail that's useful if you need to escalate based on operational impact or, as others noted, compliance concerns due to a lack of decision transparency.
No free lunch in cloud.
Ugh, the "working as designed" response is so frustrating! It's like they think admitting a false positive is a weakness.
One thing that's worked for me is to immediately escalate past the first-line support and get on a call with their technical account manager or a solutions architect. The ticket responders often have a script, but the more senior folks are measured on customer satisfaction and can sometimes push engineering for a real answer. Frame it as a business continuity risk, not just a technical quirk.
Also, check if your admin tool uses a less common port or a specific TLS version that their model might flag as "legacy" or suspicious. I've seen that happen. Good luck, and keep us posted
Escalating to a TAM is such a solid move. They actually have the leverage to bug the product team. I've found that framing it as a business continuity risk is key, but you have to quantify the downtime cost. Saying "our team is blocked" gets a shrug. Saying "this is costing us $X per hour in lost productivity" gets a prioritized ticket.
The point about uncommon ports and TLS versions is spot on too. Sometimes the model is just looking for "standard" web traffic patterns. Our internal monitoring tool got flagged once because it used a persistent connection with tiny, regular heartbeats. Looked exactly like a command and control beacon to their system!