Hi everyone! I've been lurking here for a while as I was researching our cloud security move. About six months ago, my team switched from Check Point CloudGuard to Lacework for our AWS environment. I wanted to share my experience since it was a big learning curve for me.
With CloudGuard, I felt like I was mostly dealing with firewalls and network layers, which was great, but I struggled to get a clear picture of our overall cloud security posture. It felt a bit reactive. Moving to Lacework was eye-opening—the amount of data it collects and the way it visualizes everything in the Polygraph really changed how we operate.
The biggest win for me, as someone still getting up to speed, has been the alerting. Lacework’s alerts feel more contextual. Instead of just saying "anomaly detected," it usually ties the event to a specific resource, user, and potential risk level. This has made it way easier for me to triage and understand what's actually urgent versus just informational. I also love how it handles compliance reports; generating a report for a specific framework is just a few clicks compared to our old manual process.
That said, the initial setup and learning the interface was intense! The sheer number of findings can be overwhelming at first. I had to learn to customize policies and alerts pretty quickly to avoid alert fatigue. Also, coming from a more network-focused tool, some of the behavioral analytics concepts took time to click for me.
I'm curious if others have made a similar switch? For those who've used Lacework longer, any tips on managing the dashboard or creating custom reports effectively? I'm still figuring out the best way to tailor it for our sales ops data, making sure our customer data handling is always secure.
Thanks!
Hey, thanks for sharing this. I run security for a SaaS company of about 200 people, and we've been on Lacework in AWS for a couple of years now. I also evaluated CloudGuard in a previous role, so I've seen both sides.
Here's a breakdown of my take on the key differences:
1. **Target Audience & Fit**: CloudGuard felt tailored for teams already deep in the Check Point ecosystem, extending that firewall-first mindset to the cloud. Lacework is built for cloud-native teams; its strength is stitching together IAM, workloads, and compliance data. If your team is comfortable in AWS console, Lacework clicks faster.
2. **Real Operational Win**: As you noted, it's the context. At my last shop with a different tool, we'd get "security group changed," and spend 10 minutes hunting. Lacework typically links it to a specific user's console session and the API call, cutting triage time in half. That's the daily grind improvement.
3. **Where Lacework Can Struggle**: The initial data onboarding is heavy. Expect to spend a solid 2-3 weeks tuning the signal-to-noise ratio for your specific environment after deployment. Also, their pricing model based on data volume can get tricky with very chatty container environments; we had to implement some log filtering to keep costs predictable.
4. **Support & Vendor Experience**: Their support engineers are technically strong on their own platform, but escalation to engineering on a bug can be slower than I'd like. For urgent, break-fix issues, you need to lean on your account team to push. In my experience, Check Point's support structure feels more traditional and ticket-driven.
My pick is Lacework for teams that are cloud-first and want a unified view of workload security and compliance without managing a fleet of virtual appliances. If your organization is heavily invested in Check Point for network security and wants a tightly integrated extension, CloudGuard is the logical path.
Which one I'd recommend depends on two things: your team's tolerance for managing data ingestion costs, and whether you need the solution to cover non-AWS clouds equally well. If you can share those, the call gets clearer.
Yeah, the point about tuning the signal-to-noise ratio really hits home. We went through that a few months after our switch and it was brutal for a bit. I remember getting slammed with alerts every morning, mostly from our devs' sandbox accounts doing normal, non-malicious things. It took us a while to learn how to properly use suppression rules and tune the policies.
You mentioned pricing based on data volume - did you find that the cost started to creep up unexpectedly after that initial tuning period, or did getting the noise under control actually help manage it? I'm a bit nervous about that for our next budget cycle.
Also, as a newer guy, figuring out which alerts to tune first was overwhelming. Any rule of thumb you used to prioritize?
Learning by breaking
Oh man, the tuning phase is real. On the cost question, getting the noise under control actually *lowered* our data ingestion a bit, because we weren't logging every single minor event from our sandbox accounts. The real cost creep for us came later, when we started adding more integrations and expanding to more AWS accounts. So watch out for scope expansion more than the baseline usage.
For prioritizing alerts, we used a simple "blast radius" rule: anything that could affect production workloads or customer data got tuned first. A dev creating a weird security group in a playground account? We suppressed it for that account. An IAM policy change in our production perimeter? That stayed at the top of the list. It's not perfect, but it got us out of the daily alert storm.
cost first, then scale
Your point about cost creep from scope expansion is spot on. We saw the same pattern after the initial tuning settled.
One thing we did that helped was to tag every new integration or account expansion with a projected monthly cost delta before enabling it in Lacework. This forced a conversation about whether the coverage was worth it for that specific environment, like a rarely-used analytics account. It stopped the "just turn it on everywhere" reflex.
The "blast radius" rule is practical, but I'd add a nuance: we also prioritized tuning any alert that required a manual investigation taking over 10 minutes. If an alert consistently took a long time to resolve and was low-severity, it got suppressed or modified quickly. That saved immediate operational time, even if the theoretical risk was non-zero.
benchmark or bust