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sica-fondt/knowledge/cyber-skills/skills/implementing-aws-macie-for-data-classification/references/api-reference.md
Claude 24f816b6a3
Consolidate 22 sibling repos into layered organism structure 
Place useful parts of the surrounding repos into sica-fondt by layer, per the
body model (Ada = membrane; brain/endocrine/capabilities/knowledge non-Ada):

- brain/        LLM reasoning + providers (dapr, hermes, MoMoA)
- capabilities/ REPRAG sidecars: hermes tools/skills, dapr tools, parallel
                dispatch, A51 channels, and the OSINT cluster
- knowledge/    LORAG corpus: 754 cyber-skills, agency personas, secure-coding,
                MITRE ATT&CK data
- reference/    defensive threat-reference (C3, shhbruh doc) + AdaYaml parser

License handling: AGPL sources (worldosint, advanced_evolution, mercury,
Reticulum) and GPL DeTTECT are SPEC-only clean-room/port descriptions — no
copyleft code copied. MIT/Apache/data parts copied as working trees.

Safety: shhbruh escape/persistence material and C3 covert-C2 kept as reference
only, not wired into the running organism. See CONSOLIDATION.md.

https://claude.ai/code/session_01UehUqEXXJJCsHoA4voCU5c
2026-06-10 06:53:01 +00:00

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API Reference: AWS Macie Data Classification Agent

Dependencies

Library Version Purpose
boto3 >=1.28 AWS SDK for Macie2 sensitive data discovery

CLI Usage

python scripts/agent.py \
  --profile security-audit \
  --region us-east-1 \
  --output-dir /reports/ \
  --output macie_report.json

Functions

get_macie_client(profile, region)

Creates boto3 Macie2 client with optional named profile.

enable_macie(client) -> dict

Calls client.get_macie_session() to check status, then client.enable_macie() if needed.

list_s3_buckets_summary(client) -> list

Calls client.describe_buckets() to get bucket inventory with encryption, public access, and classifiable object counts.

create_classification_job(client, bucket_names, job_name) -> dict

Calls client.create_classification_job(jobType="ONE_TIME", s3JobDefinition={...}) for targeted sensitive data discovery.

get_finding_statistics(client) -> dict

Calls client.get_finding_statistics(groupBy=...) for severity and type breakdowns.

list_findings(client, severity, max_results) -> list

Calls client.list_findings() with severity criterion, then client.get_findings(findingIds=[...]) for details.

generate_report(client) -> dict

Orchestrates all functions and compiles summary with public bucket identification.

boto3 Macie2 Methods Used

Method Purpose
enable_macie(status) Enable Macie service
describe_buckets(criteria) S3 bucket inventory
create_classification_job(...) Start discovery job
get_finding_statistics(groupBy) Finding aggregations
list_findings(findingCriteria) Filter findings
get_findings(findingIds) Detailed finding data

Output Schema

{
  "summary": {"total_buckets": 45, "public_buckets": 2, "high_findings": 12},
  "bucket_inventory": [{"name": "my-bucket", "public_access": "NOT_PUBLIC"}],
  "high_findings": [{"type": "SensitiveData:S3Object/Personal", "bucket": "data-lake"}]
}