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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
62 lines
2.0 KiB
Markdown
62 lines
2.0 KiB
Markdown
# API Reference: AWS Macie Data Classification Agent
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## Dependencies
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| Library | Version | Purpose |
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|---------|---------|---------|
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| boto3 | >=1.28 | AWS SDK for Macie2 sensitive data discovery |
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## CLI Usage
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```bash
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python scripts/agent.py \
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--profile security-audit \
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--region us-east-1 \
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--output-dir /reports/ \
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--output macie_report.json
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```
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## Functions
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### `get_macie_client(profile, region)`
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Creates boto3 Macie2 client with optional named profile.
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### `enable_macie(client) -> dict`
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Calls `client.get_macie_session()` to check status, then `client.enable_macie()` if needed.
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### `list_s3_buckets_summary(client) -> list`
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Calls `client.describe_buckets()` to get bucket inventory with encryption, public access, and classifiable object counts.
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### `create_classification_job(client, bucket_names, job_name) -> dict`
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Calls `client.create_classification_job(jobType="ONE_TIME", s3JobDefinition={...})` for targeted sensitive data discovery.
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### `get_finding_statistics(client) -> dict`
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Calls `client.get_finding_statistics(groupBy=...)` for severity and type breakdowns.
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### `list_findings(client, severity, max_results) -> list`
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Calls `client.list_findings()` with severity criterion, then `client.get_findings(findingIds=[...])` for details.
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### `generate_report(client) -> dict`
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Orchestrates all functions and compiles summary with public bucket identification.
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## boto3 Macie2 Methods Used
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| Method | Purpose |
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|--------|---------|
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| `enable_macie(status)` | Enable Macie service |
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| `describe_buckets(criteria)` | S3 bucket inventory |
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| `create_classification_job(...)` | Start discovery job |
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| `get_finding_statistics(groupBy)` | Finding aggregations |
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| `list_findings(findingCriteria)` | Filter findings |
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| `get_findings(findingIds)` | Detailed finding data |
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## Output Schema
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```json
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{
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"summary": {"total_buckets": 45, "public_buckets": 2, "high_findings": 12},
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"bucket_inventory": [{"name": "my-bucket", "public_access": "NOT_PUBLIC"}],
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"high_findings": [{"type": "SensitiveData:S3Object/Personal", "bucket": "data-lake"}]
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}
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```
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