What we still need to verify : 1 point in this profile is not yet confirmed against vendor documentation.
- Current breadth of IaC and pipeline scanning features: confirm against vendor documentation
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What it does
Orca's defining mechanism is SideScanning: instead of running an agent inside a workload, it uses the cloud provider's own APIs to take and read snapshots of block storage volumes out of band. From that filesystem image it enumerates installed packages, application dependencies, embedded secrets, malware indicators and configuration files. Nothing runs on the workload and nothing consumes its CPU, which is why coverage tends to be complete rather than limited to hosts someone remembered to instrument.
That workload data is combined with cloud control plane configuration, identity and permission graphs, and network reachability into a single model. The platform then reasons over that model to produce attack paths: an internet-facing load balancer, reaching a container with an exploitable vulnerability, running with a role that can read a sensitive data store. Prioritizing on path rather than on CVSS alone is the main answer to alert volume. The platform also covers Kubernetes posture, sensitive data discovery, and scanning of infrastructure as code and repositories.
Where it fits
Orca is a production posture and vulnerability platform, connected to cloud accounts through a read role and operated by a cloud security team. Onboarding is fast precisely because there is no agent rollout to negotiate with application owners. Findings flow to ticketing and chat for remediation by the teams that own the workloads. The platform's strength is comprehensive visibility of what is actually deployed.
Strengths
- Agentless collection removes the biggest blocker to coverage, and gaps are where cloud incidents tend to start.
- Attack path analysis gives a defensible prioritization story that reduces the pile of findings to something a team can actually work through.
- Onboarding a new account is a permissions change, not a deployment project, so time to first useful result is short.
- Combines workload, identity, configuration and data findings in one model rather than in separate product silos.
Limitations
- Snapshot-based scanning is point in time. It sees what is on disk, not what is happening now, so in-memory activity, live process behavior and short-lived compromise are outside its view. Runtime detection needs a separate capability.
- Very short-lived workloads such as ephemeral serverless containers can come and go between scan cycles.
- Cloud-native by design, so on-premises and non-cloud estate is not covered, and consolidating on the platform creates the usual data and workflow lock-in.
Who it suits
Strong fit for security teams responsible for large cloud estates who have struggled to get agents deployed and need trustworthy coverage first. Less appropriate as a standalone answer where real-time runtime detection and response is the requirement, or for organizations with substantial on-premises infrastructure that would still need a second platform.
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