Our cloud security queue is getting noisy across AWS and Azure, and the team keeps losing time on low impact findings while the important stuff sits there. We’re looking at Orca and a few other tools, but I’m more interested in the process than another dashboard.

How are people ranking alerts in practice? Asset criticality, internet exposure, exploitability, business impact, attack paths etc. Would appreciate any real world examples, thanks!

I put it on an EC2 instance and read CloudTrail straight out of S3 — no Glue crawler, no Athena workgroup. A throwaway account with 118 events in it was enough to prove the detection query I had already written was wrong — and to convince me this is worth building out properly

We’re testing AI agents for cloud ops tasks, but letting them make changes in prod is still a concern. Right now, we keep them read-only and require human approval for anything that changes state.

evaluating it to see how well it handles runtime protection for AI agents. Did it actually help control risky agent actions? can we go for our own solutions instead of it?

Hi everyone,

I’m currently working in cybersecurity(4+) and considering transitioning into cloud security. I’m trying to understand how this career switch works in the real world.

My main concern is that, although I already have professional experience in cybersecurity, I’m still a beginner when it comes to cloud technologies, particularly AWS.

If I switch to cloud security, will companies consider my existing cybersecurity experience, or will I be treated as a fresher/junior because I lack hands-on cloud experience?

For those who have made a similar transition:

• Did you have to take a significant pay cut or accept a junior-level position?
• Can existing cybersecurity experience help you get mid-level cloud security roles?
• What skills or practical projects would help demonstrate that you’re ready for something beyond an entry-level role?
• Is it better to transition directly into cloud security or first gain experience in a general cloud engineering role?

I’m willing to learn and build practical skills, but I don’t want to unnecessarily restart my career if my existing experience can be leveraged.

I’d appreciate honest advice from people who have made this transition or hired professionals in cloud security.

The security question changes once an AI workflow can access internal documents, customer data, code, or tools that do more than generate text.

At that point, the concern is not just hallucination. It is whether a connector has broader access than the workflow needs, whether untrusted content can influence tool use, and whether anyone can reconstruct what the system saw and did after an incident.

For teams deploying AI beyond simple chat, what control became non negotiable first?

The cloud IR problem I see most often is not the absence of logs. It is that the evidence cannot be joined.

You may have a suspicious sign in, a control plane event, and a workload behaving differently, but no reliable way to connect them to the same identity, resource owner, deployment, or business service. By the time someone works out the ownership path, the useful log window may be gone.

What missing source or missing context has most often blocked a cloud investigation for you?

Our platform only shows the public hostname and the vuln, then it turns into ticket ping pong because the DNS team, CDN team, BU cloud team, and vendor all say it is someone else. The hostname is not the root cause, so I am trying to standardize the evidence chain for DNS, CNAME, CDN config, reachable origin, affected component, org entity, and the control that must change. Still not sure why this is so messy

Anyone who's learning cloud security can reach out to me personally, had took me 3+ months but finally took life turning decision.

Which approach do you use for consolidating AppSec tooling? A: Standardizing on one vendor's full platform. B: Keeping best-of-breed point tools but building your own correlation layer. C: Cutting overlapping tools and accepting some coverage gaps. D: Something else entirely. What made you choose it, and has budget pressure actually forced the decision or was it a proactive call?

We're a small-ish security team (4 people) covering about 60 EKS clusters across two AWS accounts plus a bit of GKE from an acquisition we're still absorbing. Current setup is agentless posture scanning and we are drowning. Something like 1,200 "critical" findings open, and nobody believes the severity ratings anymore because half of them are on images that never get pulled or workloads with no network path to anything.

Renewal on our current CSPM is coming up in about six weeks so leadership wants us to actually look at alternatives instead of auto-renewing. The thing we keep coming back to is runtime context. We want the tool to tell us which of those 1,200 are actually reachable and running, not just that the CVE exists in a layer somewhere. A finding that got marked low last quarter turned out to be on a pod with a public ingress and we only caught it by luck.

Upwind keeps coming up when I search for runtime-based CNAPP, alongside Wiz and Sysdig which we're also putting on the shortlist. On paper Upwind's whole pitch is the runtime/posture fusion thing we want, but I can't tell how much of that holds up once you're actually living in it every day.

So for anyone running Upwind in anger: how noisy is it really once it's deployed? Does the runtime data actually collapse the posture backlog or does it just add another dashboard? And how painful was the agent/sensor rollout across a bunch of clusters? Trying to go into this eval with realistic expectations rather than the demo version.

What’s up! Just wanted some advice. I’m currently working as a cloud engineer with an AWS partner. I eventually want to move over to being a Cloud Security Engineer. What should areas should I emphasize during my transition?

Rolled wiz out across our AWS and GCP setup about two months back. The visibility into misconfigs and exposed buckets is honestly night and day next to the native cloud tooling we had before. Downside is our SecOps slack channel is now getting buried in alerts every single morning. Stuff we’re actively trying to fix:

1. Critical severity tags landing on internal dev resources that aren’t public.
   2.Duplicate alerts for the same container image vuln over and over.
   
2. Auto assigning tickets to the actual right engineering team.We had cloudfresh in during the original purchase and setup to define our risk environment rules, but our dev velocity makes keeping those rules clean a constant fight.

Any advice on remediation workflows that developers wont just ignore

Been running WAF governance for AWS, Azure, Cloudflare and one old enterprise WAF, and we have this weird gap rn. Infra and app teams own their stacks, our central view is basically a spreadsheet that says which app has a WAF resource, not which one is actually in blocking mode with sane rules and no direct to origin bypass. we have a big WAF coverage review coming up and I'm kind of stressed. Cloud consoles tell me what's deployed, but that misses monitor only mode, config drift, origin still exposed on old hostnames, forgotten dns etc. I want to switch to an outside in test for our internet facing apps, and classify stuff as protected or underprotected or flat out unprotected, then rerun whenever someone migrates or changes configs. How are you verifying blocking behavior, catching direct to origin bypasses, and turning that into a defensible WAF coverage percent that leadership wont laugh at? any hints?