Single service comparison

Macie is S3-centric with automated, organization-wide bucket discovery and job-based scanning, while Huawei DSC requires explicit asset authorization and connection onboarding for databases, OBS, and big-data assets (RDS/GaussDB/CSS/Hive/HBase). DSC spans a broader asset surface than Macie's storage focus, but lacks Macie's zero-config multi-account S3 inventory and delegated administrator model. Architects must plan manual asset onboarding and connectivity (VPC, IAM/agency authorization) instead of relying on AWS Organizations auto-enrollment.

Detection models differ in substance. Macie combines managed data identifiers, custom regex/keywords, and ML with confidence scoring, exposing findings through a standard finding schema to Security Hub, EventBridge, and GuardDuty for orchestrated response. DSC uses a rule engine, expert knowledge base, and AI classifiers with built-in templates for ~200 unstructured file types and dozens of PII categories, plus classification grading and asset map. Findings stay within DSC/DSPM rather than a unified security hub, so response workflow integration is via SecMaster or custom pipelines, not a Macie-equivalent finding bus.

DSC is broader in capability scope within one product: it bundles static/dynamic masking, visible/invisible watermarking, and API data security protection alongside discovery, whereas Macie is discovery-and-classification only with no native masking or watermarking. Pricing also diverges sharply: Macie bills per GB inspected plus managed identifier volume, while DSC is a flat monthly subscription by edition (standard/professional/API data protection) with API call quotas for masking/watermark APIs, so TCO modeling must shift from usage-based to subscription-based.

Start with assessment: inventory Macie jobs, custom identifiers, allows-list/deny-lists, classification taxonomy, and S3 buckets in scope; map each to DSC asset types (OBS/RDS/big data) and choose the DSC edition that covers discovery plus any masking, watermark, or API protection you actually use. Confirm regional availability and DBSS/SecMaster requirements for response and audit gaps, since DSC alone does not replicate Macie's Security Hub finding-bus integration.

Re-onboard assets in DSC by authorizing database/OBS/big-data connections with the required agency IAM permissions, then rebuild detection rules: translate Macie managed-data-identifier categories and custom regex/keywords into DSC built-in and custom templates, preserving category-to-sensitivity-grade mappings. Recreate scheduled scan equivalents (Macie jobs have no direct DSC job object; DSC runs scans against on-boarded assets) and document coverage gaps where Macie scanned S3 metadata-only object inspection that DSC's asset model does not match one-to-one.

Validate results and response paths before cutover: compare find counts by category, run parallel discovery, export DSC results via API for parity reporting, and wire DSC events into SecMaster or custom EventGrid/SMN pipelines to replace Macie->Security Hub/EventBridge automation. Re-point downstream SIEM/DLP consumers to DSC/DSPM APIs and confirm masking/watermark use cases (if migrated) meet parity, since DSC's masking adds capability Macie never had and should not be treated as a like-for-like replacement.

Mind the gaps and cost-model change: Macie's organization-level S3 auto-discovery and continuous monitoring have no exact DSC equivalent, so ongoing asset onboarding becomes operational work. Reshape TCO from per-GB-inspected to flat subscription (standard ~3000, professional ~5000, API data protection ~6300 RMB/month) plus API quotas for masking/watermark; verify API call allowances against peak scan and mask volumes, and budget SecMaster/DBSS separately for audit and SOAR parity.