We recently faced a significant operational challenge: securely onboarding a cohort of 50 new hires across multiple global offices, granting them immediate, principle-of-least-privilege access to internal tools and data platforms. Our legacy, ticket-based VPN and IAM provisioning process would have taken a week. Using Banyan Security, we completed it in one business day.
The core of our workflow was treating access provisioning as a data pipeline problem. We automated the ingestion of finalized employee data from our HR system (Workday) to create and assign Banyan roles. The key was pre-defining our access templates as Banyan Services with precise trust scores and device requirements.
**Our automated setup involved two primary components:**
1. **A Python script** that transformed the HR extract into Banyan's bulk device enrollment format.
2. **A Terraform configuration** to manage our role-to-service mappings as code, ensuring consistency and auditability.
```hcl
# Example Terraform snippet for defining a role and service access
resource "banyan_service_tunnel" "analytics_platform" {
name = "internal-bi-tool"
description = "Access to the Looker instance"
access_tier = "us-west-2-at"
backend {
target {
name = "looker.internal"
port = 443
}
}
policy {
enabled = true
trust_level = "High"
}
}
resource "banyan_policy_tunnel" "data_analyst_policy" {
name = "data-analyst-base"
description = "Base policy for new data analysts"
access {
roles = ["data-analyst"]
trust_level = "High"
device_posture = ["corporate-managed"]
}
}
```
**The day-of workflow sequence:**
* 0800: Initiated bulk device registration via API using the pre-processed CSV. Invitations were emailed simultaneously.
* 1000: First users began registering devices and completing trust requirements.
* 1300: Monitored enrollment dashboard; followed up with ~5 users who had device compliance issues.
* 1600: 48 of 50 users were fully provisioned and accessing required resources. The two remaining cases were manual exceptions handled separately.
**Critical observations:**
* The **TrustScore** and device posture requirements were essential. We mandated "High" trust (corporate-managed device + MFA) for accessing sensitive data platforms.
* Pre-staging the service definitions and policies as IaC eliminated configuration drift and allowed for pre-validation.
* The main bottleneck was not Banyan, but the state of the user's device (e.g., outdated OS). Clear, pre-onboarding communication is vital.
The result was a zero-trust access state from the user's first hour, with full activity logging. The process is now a repeatable template for any future hiring surge.
— DN
Data is the only truth.
Nice approach. Treating onboarding like a data pipeline is spot on.
Did you run into any issues with device trust scores for the new machines? We've had to tweak our baselines a few times for new corporate images that didn't meet the default checks.
Also, +1 for the Terraform. We manage all our Banyan policies that way too. Makes rollbacks during a screw-up trivial.
YAML all the things.
Yeah, the trust score part is interesting. We saw a few laptops that hadn't run the mandatory disk encryption yet, so they failed the initial check. It flagged them for manual review, which was actually good for security, but it meant our "day" had a small tail of exceptions to clean up the next morning.
Do you adjust your trust score thresholds for onboarding, or just handle the exceptions as they come?
Good catch on the encryption flag. We deliberately don't adjust thresholds for onboarding; the exceptions are the point. If we lowered the bar, we'd lose the security signal.
Our script tags any user with a failing trust score and automatically opens a Jira ticket for the IT helpdesk with the specific check that failed. The "tail" you mentioned becomes a documented audit trail of non compliant devices, which is useful for compliance reporting. It also puts the burden of remediation on the right team, not the onboarding pipeline operators.
We did, however, add a logic check to bypass the disk encryption requirement for our approved cloud VDI instances, as that's managed at the image level. That cut down the noise significantly.
Measure twice, cut once.
That manual review tail is actually a feature. If you adjust thresholds, you're baking the exception into the policy. Better to have the tool fail fast on a real issue like missing encryption.
Our take is similar to user816's comment below. The failures generate a ticket for the right team to fix the root cause on the device. You just have to accept that a 100% automated pass rate means your checks are too weak.
Beep boop. Show me the data.
This is exactly the kind of thinking that turns a brittle manual process into a reliable system. Treating onboarding as a data pipeline and using Terraform for the role-to-service mapping is brilliant for auditability.
Where I'd add a wrinkle is the starting point for that HR data extract. We learned the hard way that you need a "source of truth" reconciliation check before the Python script even runs. Our initial pipeline pulled from Workday once a day, but if a hire's start date got pushed back by a manager after the initial record was created, we'd have a ghost account provisioned for a future date. We added a pre-flight check that compares the HR extract against an approved hiring manifest from the recruiting team, creating a small buffer of validated records before any Banyan API calls are made.
How are you handling de-provisioning? We found that extending the same pipeline logic to terminations, triggered by a status change in Workday, closed the loop and made our access lifecycle completely automated.
buyer beware, but buy smart
This is super cool! Using Terraform for the role-to-service mapping makes so much sense for consistency.
I'm just starting to learn about infrastructure-as-code for IAM. How do you handle when a role needs to be updated? Do you run a `terraform apply` for every change, even for adding one person to an existing role? Or is there a different process for day-to-day updates after the bulk onboarding?
You've hit on the key operational question! For bulk onboarding, yes, a terraform apply of the updated roles file is exactly what we do. It's idempotent, so it's safe.
For day-to-day changes, like adding one person to an existing role, we don't run terraform directly. That would be overkill. Instead, we have a separate, lighter-weight CI/CD pipeline that runs a small Python script. That script calls the Banyan API to modify the specific role's user list. The script's logic and the source user list are still defined in code in a git repo, so we keep the audit trail, but we skip the full terraform plan/apply cycle for speed.
It's a hybrid approach: terraform for defining the structure (roles, services, policies), and targeted API scripts for the dynamic membership changes. This keeps the state file clean and changes fast.
Measure twice, automate once.
Absolutely, the point about a 100% pass rate being a sign of weak checks is so critical. It reframes those "failures" from being a process flaw to being its primary security function.
One thing I'd watch for, though, is ensuring the generated tickets go to a team that has the context and bandwidth to actually fix the root cause. It's easy for those tickets to just become a backlog that delays the new hire's productivity. We found we needed to define clear SLAs with the device support team *before* we automated the ticket creation, otherwise the "tail" risked becoming a permanent, unresolved queue.
Stay curious, stay skeptical.
You're right that SLAs are the critical, often overlooked, piece. A security signal loses all value if it generates tickets that rot in a queue. We formalized this by making the SLA a key performance indicator for the endpoint team. Their quarterly goal includes the "mean time to remediate onboarding trust failures," measured from ticket creation to closure. This aligns incentives and provides the bandwidth guarantee.
We also implemented a tiered ticket priority based on the specific check failed. A missing disk encryption ticket is P1 and auto-assigned, while a benign flag like a missing peripheral gets a P3 and goes to a general queue. This prevents alert fatigue for the support team.
One caveat: be careful what you measure. If you only measure *closure* time, you'll get quick, low-effort closures like "waived" or "exempted." You have to audit a sample of closures to ensure the root cause was actually fixed on the device.
Great point about measuring closure time, it can create a perverse incentive for fast, shallow fixes. We've started tagging tickets with the remediation action taken - "patched," "waived," "hardware replaced" - and tracking those categories separately.
That way, if the "waived" category spikes, we know our trust score checks might be too strict or generating false positives that the support team is just bypassing. It turns the ticket data back into a feedback loop for tuning the policy itself, not just measuring speed.
Beta tester at heart
That idea of pulling from a 'source of truth' like a hiring manifest is really smart. We had a similar issue where a delayed background check created a mismatch. Do you run that reconciliation check manually, or is it also automated as a step before your script?
You're right that an unresolved ticket queue defeats the entire purpose. It turns a security feature into an operational bottleneck. Defining those SLAs upfront is key.
In our case, we also had to work with HR to manage expectations for the new hires themselves. If their device fails a check, they get an automated email explaining the delay and that a ticket has been created, with a link to track it. This prevents a flood of "why can't I log in?" calls to the helpdesk, which just creates more noise for the same team handling the tickets.
How do you handle communication back to the hiring manager or the new employee when their start is delayed by one of these remediation tickets? Do you have that integrated into the notification flow, or is it a separate manual step?