After running Wiz in our AWS environment for about eight months, our security team made the decision to switch to Lacework last quarter. The primary driver was an overwhelming volume of false positive alerts, which was creating alert fatigue and causing us to miss more subtle, actual issues.
Specifically, we were getting flagged constantly for "Publicly accessible storage bucket" on internal buckets that had complex, but correct, bucket policies and ACLs in place. Wiz's assessment seemed to ignore the deny statements in our policies. We also saw regular "Unencrypted database" alerts for Aurora instances where encryption was definitely enabled at rest. Each alert required manual investigation to dismiss, which became a significant time sink.
I'm curious if others have experienced similar challenges with false positives, particularly in complex cloud environments. Did you find tuning strategies that worked, or was switching the only viable path? I want to be clear this isn't a blanket condemnation—Wiz's asset inventory and visualization were excellent—but the noise ultimately undermined its core value for us.
We're still evaluating the long-term fit with Lacework, but the reduction in noise has been substantial so far.
Keep it civil, keep it real
I'm a procurement lead at a 450-person fintech, managing vendor risk for our AWS and GCP estates. We trialed both Wiz and Lacework over 18 months before standardizing on a third option.
* **Target Fit** - Wiz is built for greenfield cloud, where you can adopt its security model wholesale. Lacework is better for complex, existing environments with legacy IAM and hybrid systems. If your cloud setup is mature and idiosyncratic, Wiz will fight you.
* **Real Pricing** - Lacework's list price is roughly 25-30% higher for comparable workload coverage in my last shop. The hidden cost with Wiz is operational: you pay in engineering hours for tuning. Their per-workload pricing sounds simple but can spike if you auto-provision broadly.
* **Deployment & Tuning Effort** - Wiz deploys fast but requires significant policy customization to be usable. Their default rules are noisy, as you found. Lacework took us three weeks to fully onboard due to agent deployment, but its default policies were calibrated better for production.
* **Where It Breaks** - Wiz's policy engine uses a simplified abstraction of IAM and resource policies. In complex environments with nested denies and service accounts, it often misses context and flags false positives. Lacework's data collection is more thorough but can impact performance on constrained instances.
I'd recommend Wiz only for teams building a new cloud footprint from scratch with a dedicated cloud security engineer to manage the rule set. If you're managing an existing, complex environment, Lacework is the less noisy choice. To make a clean call, tell us the size of your dedicated cloud security team and whether you're mandated to run an agent-based solution.
Trust but verify.
We saw the exact opposite. Lacework's model was noisier for us, especially on IAM analysis. It flagged every potential cross-account trust as critical, even with explicit scoped conditions.
Wiz's bucket check is known to be aggressive. It simulates external access, which often misses context from internal VPC endpoints or service controls. You can suppress those rules by resource tag, but it's a band-aid.
If Lacework's reduction holds, your environment might match its detection logic better. Monitor its Kubernetes runtime alerts; that's where we saw its false positives spike.
Trust, but verify
I hear you on the bucket policy frustration. We went through a similar tuning phase early on. In our case, the "unencrypted database" alerts for Aurora were often triggered during the brief maintenance windows when AWS performs certificate rotations. Wiz was catching a transient state that, while technically true for a moment, wasn't a meaningful risk.
The switch makes sense if the alert fatigue was crippling. That operational tax is real. One thing to watch with Lacework is how it handles drift over time. Their model can be less noisy initially, but we found their baseline for "normal" needed recalibration after major infrastructure changes to avoid missing new, real threats.
How's your team handling alert ownership and tuning in Lacework?
Trust the data, not the demo.