Zscaler Alternative for Biotech and Life Sciences: Protect Research IP Without the Backhaul

Research IP is the asset, and it does not live in a data center you control

A biotech or life sciences company is, in security terms, a pile of intellectual property with lab coats attached. Assay results, molecule structures, clinical trial data, manufacturing process notes, and the CRO correspondence that ties it all together. That IP moves through SaaS all day: an electronic lab notebook, a CRO portal, a statistics package in the cloud, a shared drive full of research files. The people touching it are computational biologists, bioinformaticians, and research scientists, many of them at benches and offices scattered across multiple sites and, increasingly, multiple countries.

Zscaler sells into this world on the promise of a global cloud that all traffic routes through. For a centralized enterprise that pitch holds together. For a research organization where engineers are latency-sensitive and sites span the US, Europe, and Asia, the backhaul model starts working against you. If you want the full architectural breakdown, read the complete guide to replacing Zscaler. This post is the biotech and life sciences case.

Here is the thesis in one sentence. Zscaler protects research IP by routing every scientist's traffic through its cloud first, which adds latency to data-heavy research workflows and stumbles at international sites behind regional filtering, so distributed life sciences teams get faster, more private protection from an on-device secure web gateway that inspects locally and never backhauls. dope.security is that alternative.

Backhaul taxes the exact workflows research runs on

Research is not light browsing. It is multi-gigabyte dataset transfers to and from cloud compute, sequencing files moving between an instrument and a storage bucket, and statistical jobs that pull large inputs over the wire. Zscaler's model sends that traffic to the nearest Zscaler data center for inspection before it continues to its destination. Every hop adds latency, and for a bioinformatician waiting on a dataset, that latency is not an abstraction, it is time at the desk.

This is the same complaint engineering-heavy teams raise in every industry, which we covered when we looked at why Zscaler slows down engineers at SaaS companies. In life sciences the data is bigger and the workflows are heavier, so the tax is larger. dope.security removes the detour entirely. Traffic flies direct from the device to its destination while the agent inspects locally, so a scientist pulling a large dataset is not also paying a toll to a distant inspection point.

International research sites are where backhaul actually breaks

Life sciences is global. A discovery team in Boston, a manufacturing site in Ireland, a CRO relationship in China, a clinical site in Singapore. Backhauled SSE struggles in exactly these conditions, because routing traffic through regional cloud nodes and across borders introduces both latency and reliability problems, and in restricted geographies the model can fail outright. We documented why backhauled SSE struggles behind the Great Firewall, and research organizations with Chinese CRO partners or APAC sites feel it first.

Because dope.security runs the inspection on the device and sends traffic direct to the internet, it works where backhauled proxies stumble. A researcher at an overseas site gets the same policy and the same performance as a colleague in the home office, without a regional data center in the path deciding whether today is a good day for the connection. The control travels with the laptop, not with a map of cloud points of presence.

Life sciences requirements versus how each model handles them

What a research org needs	Zscaler (cloud proxy)	dope.security (on-device SWG)
Fast transfers for data-heavy workflows	Backhauled through a cloud node first	Direct to destination, inspected on device
Consistent policy at international sites	Depends on regional PoPs and routing	Same enforcement on any network, any country
Keep research IP private in transit	Traffic decrypted in a third-party cloud	SSL inspection stays on the device
Find IP shared from OneDrive or Drive	Add-on, separately licensed	CASB Neural scans data at rest
Stop IP in uploads and AI prompts	Priced as another module	Dopamine DLP on device, one console

The takeaway: a cloud proxy makes research data take a detour and a third party decrypt it. On-device inspection keeps the data fast and local while still enforcing policy.

Privacy and data residency are not a footnote in this industry

When Zscaler inspects encrypted traffic, it decrypts it in its cloud. For most companies that is an accepted trade. For a life sciences organization sitting on proprietary research and, often, regulated clinical data, the idea that every encrypted session passes through and is decrypted inside a third-party data center is a harder conversation, especially across jurisdictions with their own data residency rules. dope.security performs SSL inspection on the device, so the decryption happens locally and the data does not take a tour through someone else's infrastructure. Better for privacy, better for residency, and a much shorter conversation with legal.

The same architecture matters for compliance-heavy adjacent work. Teams that handle protected health data alongside research have made this move for the same reason, which we covered in the Zscaler alternative for healthcare piece. The principle carries straight into life sciences: keep inspection where the data already is.

Data at rest: the shared drive is where IP quietly leaks

Most research IP loss is not dramatic. It is a folder of assay results shared with a CRO that stayed shared after the engagement ended, a molecule file in a personal OneDrive, a dataset link set to anyone-with-the-link for convenience during a deadline. These are data-at-rest exposures, and a proxy that only watches traffic in motion never sees them.

dope.security adds CASB Neural, which scans OneDrive and Google Drive for files shared publicly or externally that contain PII, IP, or regulated data, then offers one-click remediation and continuous monitoring. For a research org, that is the difference between assuming sharing hygiene is fine and actually knowing which files are exposed and to whom.

The agent has to be light enough to live on a research laptop

Research machines are already busy. Local analysis, virtualization, large datasets in memory. A heavy security client that competes for resources is a tax on the work. Zscaler Client Connector has a reputation for weight, a point we drew out in the comparison of Client Connector versus a lightweight agent. dope.endpoint runs under 100 MB of RAM and is built for roughly 4x the performance of legacy proxy SWGs, so it stays out of the way of the compute that actually pays the bills.

Deployment is the other half. dope.security ships as a single agent through whatever MDM the org already runs, Jamf for the Mac-heavy research crowd or Intune for the Windows fleet. The speed is real: Outreach Health, a multi-site operator, secured 99% of its devices within a week, and that same one-agent model is what a biotech needs when it spins up a new site or onboards a cohort of new scientists. Their multi-site deployment story maps cleanly onto a distributed research footprint.

One console beats a stack of modules a research IT team has to wire together

Biotech IT is usually small relative to the company's value, and it is busy keeping instruments, lab systems, and compliance evidence in order. The last thing it needs is a security suite assembled from separately licensed parts, each with its own configuration and its own line on the invoice. Zscaler's model tends to grow that way: the proxy is the base, and DLP, CASB, and the rest arrive as add-on modules priced and managed on their own. For a lean research IT function, every module is another integration to own.

dope.security puts SWG, CASB Neural, and Dopamine DLP under one console built from the ground up rather than stitched together through acquisitions. A research IT lead sees web traffic, data at rest, and data in motion in one place, with one policy model and one place to look when an auditor asks a question. That consolidation is the same reason mid-market teams without a large security staff keep moving to the agent-based model, a case we made in the Zscaler alternative for mid-market IT teams. The fewer moving parts a small team has to maintain, the more of its time goes to the science instead of to security plumbing.

The pricing follows the architecture. Because there is no global proxy network to route every session through, and no per-module licensing to assemble, the cost model is simpler and more predictable, which is exactly what a research org budgeting against grant cycles and funding rounds wants. You are paying for an agent and a console, not for backhaul capacity and a catalog of add-ons.

What is the best Zscaler alternative for biotech and life sciences?

An agent-based endpoint SWG that inspects on the device, flies traffic direct so data-heavy workflows stay fast, works at international sites including restricted geographies, keeps decryption local for privacy and residency, and bundles CASB and DLP under one console instead of as separately priced modules. dope.security is the named alternative. It gives research teams the thing a backhauled proxy structurally cannot: protection that does not slow the science down or send proprietary data on a detour through someone else's cloud.

Zscaler protects IP by making every researcher's traffic visit its cloud first. A life sciences team's real constraints, fast transfers, global sites, and keeping proprietary data private and local, all point the other direction, toward inspection that happens on the device and traffic that flies direct. That is the gap dope.security was built to close, and it is why research IT teams replacing Zscaler in 2026 should start with the Zscaler replacement guide and a pilot on one research team.

Try it with one lab. Push the agent to a research team's devices through your MDM, confirm policy in the console, and measure the transfer speed difference. Start a free trial or book a 20-minute demo.