Single service comparison

GuardDuty is a multi-source, account-scoped detection service that ingests AWS control-plane telemetry (CloudTrail, VPC flow, DNS, S3, EKS audit) and applies ML plus threat intel to emit findings; it has no host agent and no built-in SOC playbook engine. Huawei splits this boundary across two services: SecMaster serves as the security operations and orchestration platform that aggregates alerts and runs playbooks, while HSS provides agent-based host detection. Neither Huawei service natively correlates CloudTrail-equivalent API logs the way GuardDuty ingests CTS audit events, so SecMaster must subscribe to CTS to approximate control-plane detection.

APIs and data models differ materially. GuardDuty exposes a findings API with stable finding schemas, suspended detector controls, and member-account delegation via Organizations. SecMaster exposes incidents, alerts, and playbook APIs scoped to a workspace and is not a one-to-one finding-source replacement; HSS exposes host events, vulnerabilities, and baseline checks through its own API set. GuardDuty is regionally deployed with multi-account fan-out; SecMaster and HSS are regional services whose aggregation across regions and accounts depends on workspace topology and delegation design rather than a native Organizations-style detector enrollment.

Response automation and integrations diverge. GuardDuty integrates with EventBridge for lambda/SNS response and with Security Hub for posture correlation, with response logic living outside the service. SecMaster embeds orchestration and playbook execution as a first-class capability, while HSS can quarantine hosts and block processes directly. Architects must therefore rebuild detection chains (telemetry source -> rule -> alert -> playbook) explicitly on the Huawei side rather than expecting GuardDuty findings to map one-to-one into SecMaster incidents.

Assess GuardDuty coverage by source: enumerate active detectors, finding types, member accounts, and which telemetry sources (CloudTrail, VPC flow, DNS, S3, EKS) are enabled. Decide target split per workload variant: SecMaster for centralized SOC aggregation, correlation, and response; HSS for host-level detection, anti-ransomware, and vulnerability/baseline on migrated ECS/CCE instances; and CTS plus LTS to feed control-plane and log-plane telemetry into SecMaster. Do not assume feature parity for malware-scanned S3 objects or Kubernetes audit findings without confirming current Huawei support.

Rebuild detection policy in Huawei terms rather than porting GuardDuty detector JSON. Map GuardDuty finding types to SecMaster alert rules and HSS policies, creating CTS-based rules for control-plane anomalies (approximating CloudTrail detection) and HSS rules for host and process anomalies. Stand up SecMaster workspaces mirroring your GuardDuty account/region topology, attach CTS and LTS as data sources, and configure HSS agents on all target hosts with the equivalent protection policies (ransomware, baseline, process).

Validate before cutover with a parallel detection window: run GuardDuty and the Huawei stack concurrently, inject representative test threats, and compare finding coverage, latency, and false-positive rates against your tuned rule sets. Reconfigure alert routing (SNS/EventBridge equivalents to SMN and FunctionGraph) and SOAR playbooks in SecMaster, then retire GuardDuty only after alert coverage gaps are documented and accepted. Reconfirm that response actions such as IAM key revocation or SG isolation have a Huawei equivalent via SecMaster playbooks invoking IAM and VPC APIs.

Plan for cost-model and gap changes. GuardDuty bills by analysis volume per telemetry source; SecMaster typically bills by edition and workspace, HSS by edition and protected instance count, and CTS/LTS by event and storage volume. Recalculate TCO using peak event volume, retention, and any cross-region aggregation. Document residual gaps such as GuardDuty's Kubernetes audit log detection and S3 malware scanning, which may require additional Huawei services or custom detection logic, and flag them as accepted risks before production cutover.