Alright, so I've been running InsightCloudSec (ICS) in our multi-cloud setup (AWS, GCP, some Azure) for about eight months now, primarily for CSPM, compliance checks, and asset inventory. Like many here, I was drawn to the promise of agentless architecture—quick deployment, no maintenance overhead, broad surface coverage. It's been solid for the "what's out there" picture.
But I keep hitting this ceiling when I try to answer more nuanced, product-led growth type questions for our own internal platform teams. For example:
* **Feature Adoption:** We roll out a new managed service (like an internal "AWS RDS as a service"). ICS can see the RDS instances, sure. But it can't tell me *which* teams are actually using the new automation features versus manually configuring. I need to see process-level or deployment pipeline data.
* **User Behavior Tracking:** A team spins up a massive, non-compliant compute instance. ICS flags it, we remediate. But I can't trace *which* developer action in their CI/CD or local workflow caused it. Was it a one-off mistake or a flawed pattern in their Terraform module?
* **Real-time Configuration Drift:** The agentless scan interval, even tuned aggressively, means there's a lag. I've seen a container workload deploy with a critical vulnerability, get flagged on the next scan, but in that window, it was already interacting with other services. An agent could potentially enforce or alert at *runtime*.
This has me thinking: is the future of ICS purely agentless? Or will they inevitably introduce a lightweight, optional agent for deeper visibility? I'm starting to feel the trade-off:
* **Pure Agentless Pros:** Unbeatable for initial onboarding, assessing legacy environments, and covering the entire estate without friction.
* **The Visibility Gap:** Missing host-level processes, true real-time configuration changes, software bill of materials (SBOM) for running workloads, and deeper integration with the actual workload lifecycle.
I'm experimenting with layering other tools (open-source agents, runtime security tools) to fill these gaps, but it's messy. It fragments my data and ruins the single-pane-of-glass appeal.
**My burning questions for the community:**
* Is anyone else using ICS in a product analytics/PLG context for internal dev platforms? How are you getting deeper than inventory data?
* Has anyone heard rumblings from Rapid7 about a hybrid model? Maybe a voluntary agent for "tier 1" critical workloads?
* If they *did* offer an agent, what's the one piece of data you'd need it to collect that you can't get now? For me, it's direct linkage between a cloud resource and the specific CI/CD pipeline run or developer command that instantiated it.
I love the tool, but I'm wrestling with whether agentless alone can get us to the next level of maturity—from "what do we have?" to "how is it being used, by whom, and in real-time?"
🔥
Try everything, keep what works.
That ceiling you're describing is a known trade-off with pure agentless. You're moving from inventory to intent, and cloud APIs just don't expose that.
> I need to see process-level or deployment pipeline data.
Exactly. The "why" and "how" behind the resource. I've seen clients try to bridge this by correlating ICS data with events from their CI/CD platform (like Jenkins or GitLab pipelines) in a separate analytics dashboard. It's a manual join, but it can answer those behavior questions.
On your last point about real-time drift, even aggressive scanning has a blind spot between cycles. An agent could catch that, but then you're managing agents. I doubt the platform will go fully hybrid, but I could see them offering a lightweight agent as a premium add-on for runtime context. That seems to be where the market is nudging everyone.
Integrate or die
The ceiling you're describing is a fundamental property of agentless architectures, not a feature gap that a few more API calls will close. Cloud provider APIs expose resource state, not intent or causality. You can't infer whether an RDS instance was spun up by a CI/CD pipeline vs. a developer clicking through the console from the API metadata alone. The "why" is inherently opaque.
I've seen teams try to compensate by layering CloudTrail logs and artifact tag propagation, but that introduces its own latency and correlation failures. Even with aggressive scan intervals (say, 5 minutes), you're still sampling a point-in-time snapshot. Drift between scans is a blind spot, and the cost of reducing that interval to seconds - both in API rate limits and processing overhead - quickly eats into the TCO advantage that made agentless attractive in the first place.
A lightweight agent for runtime context could close that gap, but you'd then be managing a new surface area: patching, compatibility, resource contention. I've benchmarked a few hybrid approaches and the operational overhead tends to negate the savings from reduced scan frequency. The real question is whether the business value of those "intent" signals justifies the added complexity. What's your approximate cost per drift event that you're trying to catch?
Trust but verify.