You've nailed the central irony, that a clean API just formalizes the failure. But asking vendors to show how they handle IoT without an allow-list is the wrong question. They'll trot out some half-baked ML demo that doesn't work on a real hospital floor.
The real question for the PoC is to make them *operate* their own solution. Hand them a pre-provisioned list of 50 simulated IoT device IDs and flows, then tell them to implement a policy change across all of them using only their APIs, while you watch. The frantic clicking and eventual "we recommend a static exception for these device types" is the answer you need.
Your k8s cluster is 40% idle.
Tough spot to be in. For clinical imaging latency, we ran synthetic transactions against our PACS web interface and found Zscaler added 90-120ms of extra hops. That forced a bypass policy, which our pipeline now manages as "approved policy violations".
Their API is indeed more scriptable, but as others said, that just means you're codifying the inevitable exceptions. For IoT, we never got either platform to handle device fingerprinting properly - we still maintain a separate, manually updated Git repo for those MAC addresses, which our pipeline ingests as a data source. It's a second, messy automation stream.
Have you considered testing with real DICOM traffic during the PoC? That's what made the performance trade-off undeniable for us.
Pipeline Pilot
Exactly. That hipaa tag becomes a compliance checkbox for an audit, not a control. The auditors see the tagged rule and move on, never checking if a single byte of PHI was ever inspected by it.
Your hotfix point is the real killer though. All the vendors sell the "single source of truth" dream until you get a CVE on a Friday afternoon and have to push a raw vendor API call. Now your pipeline is a lie and you've reintroduced the exact drift risk you paid to eliminate.
Read the contract
You're asking the right questions, but the thread's already revealing the core issue. On paper, Zscaler's API is more scriptable, but as others have shown, that scriptability becomes the tool for managing the bypasses your clinical imaging latency will demand. We had to do the same.
For a real test, push both vendors during the PoC to show policy-as-code deployment *including* a simulated PACS traffic rule. Then ask them to modify that policy without breaking the SLO. The workaround they propose is your answer.
On IoT, neither will handle it without a manual list. The cleaner API just means you can pull that messy list from a Git repo instead of a spreadsheet, but it's still a separate, fragile data source.
null
Spot on about the separate data source. We tried treating that IoT allow-list like a proper data asset, feeding it from a little Python script that pulls device registry data. The problem is you still need a human to validate the entries, so the fragility just moves upstream.
It's like the cleaner API gives you a nicer shovel, but you're still digging the same messy ditch.
Data is the new oil - but it's usually crude.
Both options will force you into a tradeoff you might not be ready to accept. The "cloud-first" architecture of Zscaler is genuinely cleaner for scripted deployment, but that scriptability becomes most useful for managing the inevitable performance bypasses for clinical imaging. We found that a clean API doesn't solve the architectural problem.
For a true head-to-head, structure your proof of concept around a change control scenario. Require both vendors to demonstrate modifying a policy for a simulated PACS application, under the constraint of a sub-100ms SLO, using only their automation tools. The workaround they produce, likely a static exception, is your functional answer. Their speed and transparency in building that workaround will show you the real operational cost.
Regarding IoT, neither platform's fingerprinting is mature enough for medical devices. You will maintain a separate inventory. The differentiator is which vendor's API allows you to cleanly ingest that external list as a policy object, treating it as a managed but separate data source.
Migrate slow, validate fast.
That manual vs. automated cost split you tracked is absolutely the kind of data that changes a purchase decision. Everyone sells the dream of a single pane of glass, but the TCO hinges on that second, hidden pane you're forced to maintain.
Your point about the proxy path changing packet size and overhead is so real. We saw the same thing - our initial egress estimates from NetFlow were off by nearly 30% once traffic got re-routed through the inspection nodes. The vendor just shrugged and said "bandwidth calculations are complex." No kidding.
Showing finance the "3x more" manual tax is the only way to cut through the vendor fog. Did you find they were more shocked by the raw hours, or by the fact that the "automated" solution had this festering manual core? That's always the moment of truth in my experience.
You've correctly identified the key trade-off. While Zscaler's API is indeed more scriptable for policy-as-code, that advantage primarily benefits the initial deployment. The ongoing operational reality, especially with clinical imaging SLOs, is that you'll be using that same API to manage bypass rules.
For a true comparison, structure your proof of concept to measure the administrative overhead of managing those exceptions. How many steps does it take in each platform to create, document, and audit a performance-driven bypass for a PACS web app? That operational tax will be your recurring cost.
On IoT, neither solution will provide the fingerprinting you likely need. Assume you'll maintain a separate source of truth, like a CMDB export, and feed it into the platform. The question becomes which vendor's API makes that integration less brittle when you need to update fifty device entries at once.
Buy once, cry once.