Research Mitigations

Service Approval — Researcher: Mitigations & Assets

Parse customer-provided input files to extract existing mitigations, build an initial asset inventory, and collect assessment gaps from the CAF assessment phase.

Output: .service-approval/<slug>/02-research/research-mitigations.json Schema: schemas/research-mitigations.schema.json — encodes phase: "research-mitigations" const, mitigation id pattern ^M-.+, assessment-gap id pattern ^GAP-, assets_partial[].cfn_type pattern ^AWS::, and per-mitigation required fields (id, mitigation_goal, statement). resource_type is allowed on mitigations but assets MUST use cfn_type (see Step 3 and "Quality rules" below).

This is one of 3 focused research sub-skills. The orchestrator merges outputs from all 3 into the final research.json.

Prerequisites

Per-service phase directory (.service-approval/<slug>/02-research/) is created dynamically using tools/paths.py::phase_dir(slug, "research").

Inputs

Parse arguments:

• ./input-folder (optional) — read all .md, .json, .xlsx, .pdf, .docx files
• --service=<name> — AWS service identifier (e.g., bedrock-agentcore, lambda, s3)

If no input folder is provided, skip mitigation extraction (MCP-only mode — mitigations will be empty). If no --service is provided, ask the user.

Step 1: Parse Input Files

For each file in the input folder:

JSON files (Mitigation Report)

Look for entities key containing mitigation arrays. For each entity:

• Extract resource type
• Extract mitigations with: id, statement, mitigation_goal, request_parameters
• Preserve the FULL statement and goal text — do NOT summarize or truncate

import json
data = json.load(open("<file>"))
for entity in data.get("entities", []):
    resource_type = entity.get("resource_type", "")
    for mit in entity.get("mitigations", []):
        # Extract: id, statement, mitigation_goal, request_parameters[], resource_type

Markdown files (Mitigation Report)

Mitigation reports (e.g. input/<Service Name> — Mitigation Report.md) follow a consistent shape. Parse with the reference spec below — do NOT reinvent on each run.

Block delimiter: each mitigation begins with a level-3 header of shape ### M-<Entity>.<N> — <title>. Regex:

BLOCK_RE = re.compile(r"^### M-(?P<entity>[A-Za-z]+)\.(?P<n>\d+) — (?P<title>.+)$", re.MULTILINE)

Split the document at each header match; each block runs until the next header or EOF.

Per-block table: the first Markdown table inside each block has column 1 as a bolded label and column 2 as the value. Parse rows keyed by the bolded label:

Emit each parsed block as an existing_mitigations[] entry with the fields above plus source: "<report-basename>" (e.g. "source": "<Service Name> — Mitigation Report.md") so downstream runs can trace provenance.

Row parsing pseudocode:

ROW_RE = re.compile(r"^\|\s*\*\*(?P<label>[^*]+)\*\*\s*\|\s*(?P<value>.+?)\s*\|$", re.MULTILINE)
label_map = {
    "ID": "id",
    "Statement": "statement",
    "Mitigation Goal": "mitigation_goal",
    "Resource Type": "resource_type",
    "Request Parameters": "request_parameters",
}
row_fields = {label_map[m.group("label").strip()]: m.group("value").strip()
              for m in ROW_RE.finditer(block)
              if m.group("label").strip() in label_map}
if "request_parameters" in row_fields:
    row_fields["request_parameters"] = [
        p.strip().lstrip("-* ").strip()
        for p in re.split(r",|\n", row_fields["request_parameters"])
        if p.strip()
    ]

Rationale: the reports are hand-curated in this exact shape. Parsing via this spec takes ~20 lines of Python; re-inventing the parser has cost ~15 min of LLM time on every cold-start run (confirmed across iter-13 and iter-14 runs). If a new report deviates from this shape, prefer fixing the report over making the parser tolerant of drift — uniform input shape keeps every downstream step deterministic.

If the input filename ends in .md but the shape diverges significantly, fall back to the previous bullet-point heuristic (mitigation IDs by pattern, statement = text between IDs) and log a warning to mcp-calls.log noting the deviation.

XLSX files

Note as framework source file — record path for the Map skill. Do NOT parse framework content here (that's the Map skill's job).

PDF / DOCX files

Extract security-relevant content. Look for:

• Mitigation tables
• Security requirements
• Threat descriptions

Other files

Read for context but do not extract structured data.

Step 2: Build Existing Mitigations

Compile all extracted mitigations into a structured array:

{
  "existing_mitigations": [
    {
      "id": "M-Resource.0",
      "statement": "<full statement text — NOT truncated>",
      "mitigation_goal": "<full goal text — NOT truncated>",
      "request_parameters": ["$.encryptionKeyArn", "$.executionRoleArn"],
      "resource_type": "AWS::<Service>::<Resource>"
    }
  ]
}

Quality rules:

• Every mitigation MUST have a non-empty statement field
• Every mitigation MUST have a non-empty mitigation_goal field
• Truncation guard: After extraction, verify mitigation_goal starts with an uppercase letter and forms a complete English phrase. If it starts lowercase or mid-word (e.g., "ulti-AZ" instead of "Multi-AZ"), the extraction truncated — re-extract from source.
• request_parameters must be JSON paths (starting with $.)
• resource_type should use CloudFormation format (AWS::<Service>::<Resource>)
• Preserve ALL mitigations — do not deduplicate or merge similar ones

Step 3: Build Initial Asset Inventory

From the mitigations, derive an initial asset list. Each unique resource_type becomes an asset entry:

{
  "assets_partial": [
    {
      "name": "<ResourceName>",
      "cfn_type": "AWS::<Service>::<Resource>",
      "data_classification": "<from mitigation context — what data does this resource handle?>",
      "trust_boundaries": [],
      "entry_points": [],
      "source": "mitigations"
    }
  ]
}

Derive data_classification from the mitigation statements — what do they describe the resource as storing/processing? If unclear, set to "<needs enrichment from capabilities>".

The trust_boundaries and entry_points will be enriched by the capabilities and api-surface sub-skills respectively.

Step 4: Collect Assessment Gaps

Check if the /assess skill has produced artifacts:

test -f .service-approval/<slug>/01-assess/assessment-summary.md && echo "OK" || echo "not found"

If assessment-summary.md exists:

• Parse each [DOMAIN_GAPS] Compliance Matrix table
• For every row with status REQUIREMENT GAP, CUSTOMER ACTION REQUIRED, or PARTIALLY SUPPORTED:
  • Record in assessment_gaps[] with: domain, requirement, status, details

{
  "assessment_gaps": [
    {
      "domain": "IAM|LOGGING|INFRA|DATA|INCIDENT",
      "requirement": "...",
      "status": "REQUIREMENT GAP|CUSTOMER ACTION REQUIRED|PARTIALLY SUPPORTED",
      "details": "..."
    }
  ]
}

If assessment-summary.md does not exist, set assessment_gaps to an empty array.

Step 5: Write Output

Write .service-approval/<slug>/02-research/research-mitigations.json:

CRITICAL — Field Name Enforcement:

• The top-level key MUST be "phase" (not "skill", not "stage")
• Each assets_partial[] entry MUST use "cfn_type" (not "resource_type") and "name" (not "display_name")
• These exact field names are required for the merge step to work correctly

{
  "phase": "research-mitigations",
  "service": "<service-name>",
  "existing_mitigations": [...],
  "assets_partial": [
    {
      "name": "<ResourceName>",
      "cfn_type": "AWS::<Service>::<Resource>",
      "data_classification": "...",
      "trust_boundaries": [],
      "entry_points": [],
      "source": "mitigations"
    }
  ],
  "assessment_gaps": [...],
  "input_files_parsed": ["<file1>", "<file2>"],
  "framework_file": "<path-to-xlsx-if-found>"
}

Output Verification

Output validation runs automatically via PostToolUse hook. The JSON Schema (see Schema above) hard-fails on phase-const mismatch, missing id / mitigation_goal / statement on any mitigation, assets_partial[].cfn_type not matching ^AWS::, or any assessment-gap id not matching ^GAP-. validate_state.py::check_research_mitigations delegates entirely to the schema — no additional hand-rolled checks remain.

MITRE ATT&CK Threat Overlay (optional)

If data/attack-cloud-matrix.json exists, enrich mitigations with MITRE ATT&CK technique mappings:

1. Load the cloud matrix (Tactics: Initial Access → Impact, ~70 techniques)
2. For each mitigation, match its category and description keywords against technique names and descriptions
3. Assign mitre_technique_ids (e.g., ["T1078", "T1190"]) to each mitigation
4. Add a threat_overlay section to the output:

"threat_overlay": {
  "techniques_mapped": 18,
  "tactics_covered": ["Initial Access", "Execution", "Persistence", "Privilege Escalation"],
  "technique_to_mitigation": {"T1078": ["MIT-001", "MIT-003"], ...}
}

This is optional — if the matrix file is absent, skip silently. The threat overlay feeds into controls-catalog.md and the APPROVAL-REPORT.md threat coverage section.

MITRE ATT&CK Threat Overlay (optional)

If data/attack-cloud-matrix.json exists, enrich mitigations with MITRE ATT&CK technique mappings:

1. Load the cloud matrix (Tactics: Initial Access → Impact, ~70 techniques)
2. For each mitigation, match its category and description keywords against technique names and descriptions
3. Assign mitre_technique_ids (e.g., ["T1078", "T1190"]) to each mitigation
4. Add a threat_overlay section to the output:

"threat_overlay": {
  "techniques_mapped": 18,
  "tactics_covered": ["Initial Access", "Execution", "Persistence", "Privilege Escalation"],
  "technique_to_mitigation": {"T1078": ["MIT-001", "MIT-003"], ...}
}

This is optional — if the matrix file is absent, skip silently. The threat overlay feeds into controls-catalog.md and the APPROVAL-REPORT.md threat coverage section.

Print Summary

Research (mitigations) complete:
  Mitigations extracted:   {N}
  Assets identified:       {N}
  Assessment gaps:         {N}
  MITRE techniques:        {N} (or "skipped — no matrix file")
  Input files parsed:      {N}
  Framework file:          {path or "none"}
  Output: .service-approval/<slug>/02-research/research-mitigations.json