Fedramp20xmcp

🚀 FedRAMP 20x MCP Server

This is an MCP (Model Context Protocol) server that offers access to FedRAMP 20x security requirements and controls, featuring Azure-first guidance. It simplifies the process of querying and implementing FedRAMP 20x requirements, making it easier for teams to achieve compliance.

🚀 Quick Start

This server loads FedRAMP 20x data from the official FedRAMP documentation repository and provides tools for querying requirements by control, family, or keyword.

Prerequisites

• Python 3.10 or higher
• pip (included with Python)
• Python must be in your system PATH

Setup

# Clone the repository
git clone https://github.com/KevinRabun/FedRAMP20xMCP.git
cd FedRAMP20xMCP

# Create virtual environment and install
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
pip install -e .

# If using uv (alternative package manager):
uv pip install -e .

✨ Features

• 🎯 Automated Evidence Collection (NEW): Complete automation guidance for all 65 active KSIs with Azure-native services, ready-to-use queries, and artifact specifications
• Query by Control: Get detailed information about specific FedRAMP requirements
• Query by Family: List all requirements within a family
• Keyword Search: Search across all requirements using keywords
• FedRAMP Definitions: Look up official FedRAMP term definitions
• Key Security Indicators: Access and query FedRAMP Key Security Indicators (KSI) with implementation status
• Documentation Search: Search and retrieve official FedRAMP documentation markdown files
• Dynamic Content: Automatically discovers and loads all markdown documentation files
• Implementation Planning: Generate strategic interview questions to help product managers and engineers think through FedRAMP 20x implementation considerations
• AST-Powered Code Analysis: Advanced Abstract Syntax Tree parsing using tree-sitter for accurate, context-aware security analysis across Python, C#, Java, TypeScript/JavaScript, Bicep, and Terraform
• Semantic Analysis: Deep code understanding with symbol resolution, control flow analysis, and interprocedural analysis capabilities
• 🚀 Pattern-Based Architecture: Unified analysis engine with 381 YAML patterns across 23 requirement families, providing comprehensive compliance checking for all KSIs and FRRs
• Pattern Engine: Declarative YAML-driven detection across 14 languages with AST-first analysis and intelligent finding categorization

Pattern-Based Analysis Architecture

The server uses a unified pattern-based architecture for all FedRAMP 20x compliance analysis:

Architecture Overview:

• 381 YAML patterns across 23 requirement families
• Single analysis engine (GenericPatternAnalyzer) replaces 271 traditional analyzers
• 14 languages supported: Python, C#, Java, TypeScript, JavaScript, Bicep, Terraform, GitHub Actions, Azure Pipelines, GitLab CI, YAML, JSON, Dockerfile, GitHub
• AST-first detection with tree-sitter for accuracy, regex fallback when needed
• Declarative patterns that are easy to maintain and extend

Pattern Coverage by Family:

• Application Families: ADS, AFR, CCM, CED, CMT, CNA, IAM, INR, MLA, PIY, RPL, RSC, SCN, SVC, TPR, UCM, VDR (17 families)
• Infrastructure Families: COMMON, FSI, ICP, KSI, MAS, PVA (6 families)
• Complete KSI & FRR Mapping: All 72 KSIs and 199 FRRs are covered through pattern definitions

How It Works:

1. Pattern Loading: YAML patterns loaded from data/patterns/ directory
2. Analysis Execution: Code analyzed using tree-sitter AST parsing with pattern matching
3. Finding Generation: Patterns generate findings with severity, description, and remediation
4. Result Aggregation: Findings grouped by requirement family with deduplication

Benefits:

• ✅ Consistency: Same detection logic across all languages
• ✅ Maintainability: Update patterns in YAML instead of Python code
• ✅ Performance: Pattern compilation and caching optimize analysis speed
• ✅ Extensibility: Add new patterns without code changes
• ✅ Accuracy: AST-based detection reduces false positives

Important Clarification: OSCAL Format FedRAMP 20x requires machine-readable formats (JSON, XML, or structured data) for Authorization Data Sharing. OSCAL is NOT mentioned in FedRAMP 20x requirements - it's a NIST standard that can be used as one potential implementation approach. The actual requirement is simply "machine-readable" - you can use custom JSON/XML or OSCAL based on your implementation needs.

📦 Installation

Prerequisites

• Python 3.10 or higher
• pip (included with Python)
• Python must be in your system PATH

Setup

# Clone the repository
git clone https://github.com/KevinRabun/FedRAMP20xMCP.git
cd FedRAMP20xMCP

# Create virtual environment and install
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
pip install -e .

# If using uv (alternative package manager):
uv pip install -e .

Dependencies:

• mcp>=1.2.0 - Model Context Protocol SDK
• httpx>=0.27.0 - HTTP client for fetching FedRAMP data
• openpyxl>=3.1.0 - Excel file generation for export features
• python-docx>=1.1.0 - Word document generation for KSI specifications
• tree-sitter>=0.21.0 - AST parsing library for code analysis
• tree-sitter-python>=0.21.0 - Python language bindings for tree-sitter
• tree-sitter-c-sharp>=0.21.0 - C# language bindings for tree-sitter
• tree-sitter-java>=0.21.0 - Java language bindings for tree-sitter
• tree-sitter-javascript>=0.21.0 - JavaScript/TypeScript language bindings

Troubleshooting: If you encounter issues, see Advanced Setup Guide for detailed troubleshooting steps.

💻 Usage Examples

With VS Code and GitHub Copilot

1. Install the VS Code MCP extension (if not already installed)

2. Configure the MCP server - Choose one of the following scopes:

   Option A: Workspace-level (Recommended for sharing)

   Add to .vscode/mcp.json in your project:

   {
     "servers": {
       "fedramp-20x-mcp": {
         "type": "stdio",
         "command": "python",
         "args": ["-m", "fedramp_20x_mcp"]
       }
     }
   }
   

   If Python is not in PATH, update the command to use your virtual environment's Python:

   {
     "servers": {
       "fedramp-20x-mcp": {
         "type": "stdio",
         "command": "${workspaceFolder}/.venv/Scripts/python.exe",  // Windows
         // "command": "${workspaceFolder}/.venv/bin/python",       // macOS/Linux
         "args": ["-m", "fedramp_20x_mcp"]
       }
     }
   }
   

   Option B: User-level (Global across all projects)

   Add to VS Code User Settings (settings.json):

   {
     "github.copilot.chat.mcp.servers": {
       "fedramp-20x-mcp": {
         "type": "stdio",
         "command": "python",
         "args": ["-m", "fedramp_20x_mcp"]
       }
     }
   }
   

   Security Note: Do NOT use "alwaysAllow" in configuration. VS Code will prompt you to grant permissions on first use, which is a security best practice.

3. Optional: Configure VS Code settings by copying .vscode/settings.json.example to .vscode/settings.json

4. Reload VS Code to activate the MCP server

5. Grant permissions when prompted by VS Code (first use only)

6. Use with GitHub Copilot Chat:

   • Open Copilot Chat
   • Ask questions about FedRAMP 20x requirements
   • Use @workspace to query specific controls or families
   • Access all 48 tools and 15 comprehensive prompts

With Claude Desktop

Add this server to your Claude Desktop configuration (~/Library/Application Support/Claude/claude_desktop_config.json on macOS or %APPDATA%\Claude\claude_desktop_config.json on Windows):

{
  "mcpServers": {
    "fedramp-20x": {
      "command": "uv",
      "args": [
        "--directory",
        "/absolute/path/to/FedRAMP20xMCP",
        "run",
        "fedramp-20x-mcp"
      ]
    }
  }
}

Note: Replace /absolute/path/to/FedRAMP20xMCP with your actual installation path.

With MCP Inspector

Test the server using the MCP Inspector:

npx @modelcontextprotocol/inspector python -m fedramp_20x_mcp

⚠️ Copilot Execution Policy

This repository uses strict instructions for all AI‑assisted coding.

See: Copilot Instructions

📚 Documentation

Advanced Configuration

For CI/CD integration, multi-server setup with Azure and GitHub, or detailed troubleshooting, see:

• CI/CD Integration Guide - Use analyzers in GitHub Actions, Azure DevOps, and other pipelines
• Advanced Setup Guide - Multi-server configuration, Azure integration, troubleshooting

Available Tools

The server provides 48 tools organized into the following categories:

Core Tools (11): Query requirements, definitions, KSIs, and KSI evidence automation FRR Analysis Tools (7): Analyze code against FedRAMP Requirements (FRR) with comprehensive compliance checking across all 199 requirements Documentation Tools (3): Search and retrieve FedRAMP documentation Enhancement Tools (7): Implementation examples, dependencies, effort estimation, architecture validation Export Tools (3): Excel/CSV export and KSI specification generation Planning Tools (1): Generate strategic implementation questions Evidence Collection Automation Tools (3): Infrastructure code, collection code, architecture guidance Implementation Mapping Tools (2): KSI family matrices and step-by-step implementation checklists Code Analysis Tools (3): AST-powered FedRAMP compliance scanning using tree-sitter for accurate, semantic analysis of IaC, application code, and CI/CD pipelines across 6 languages (Python, C#, Java, TypeScript/JavaScript, Bicep, Terraform) Security Tools (2): CVE vulnerability checking for packages and dependency files Audit Tools (2): KSI coverage summary and status checking KSI Status Tools (1): Implementation status tracking across all KSI families

get_control

Get detailed information about a specific FedRAMP requirement or control.

Parameters:

• control_id (string): The requirement identifier (e.g., "FRD-ALL-01", "KSI-AFR-01")

list_family_controls

List all requirements within a specific family.

Parameters:

• family (string): The family identifier (e.g., "FRD", "KSI", "MAS")

search_requirements

Search for requirements containing specific keywords.

Parameters:

• keywords (string): Keywords to search for in requirement text

get_definition

Get the FedRAMP definition for a specific term.

Parameters:

• term (string): The term to look up (e.g., "vulnerability", "cloud service offering")

list_definitions

List all FedRAMP definitions with their terms.

Returns: Complete list of all FedRAMP definition terms

search_definitions

Search FedRAMP definitions by keywords.

Parameters:

• keywords (string): Keywords to search for in definitions

get_ksi

Get detailed information about a specific Key Security Indicator.

Parameters:

• ksi_id (string): The KSI identifier (e.g., "KSI-AFR-01")

list_ksi

List all Key Security Indicators.

Returns: Complete list of all Key Security Indicators with their names

get_ksi_evidence_automation

Get comprehensive evidence automation recommendations for a specific KSI. All 65 active KSIs include complete automated evidence collection guidance.

Parameters:

• ksi_id (string): The KSI identifier (e.g., "KSI-IAM-01", "KSI-CNA-01")

Returns: Comprehensive guidance for automating evidence collection including:

• Azure Services: 5 Azure services per KSI (Log Analytics, Resource Graph, Azure Policy, Azure Monitor, etc.) with specific configuration guidance
  • Note: Microsoft Defender for Cloud is recommended (not mandatory) for ~8-10 KSIs to streamline vulnerability scanning, security posture management, and compliance monitoring. Alternative tools (Qualys, Tenable, Trivy) can be used.
• Collection Methods: 4-5 automated collection approaches (continuous monitoring, scheduled assessments, event-driven triggers, log aggregation)
• Storage Requirements: Retention policies (30-90 days operational, 1-7 years compliance), encryption standards, access controls
• FRR-ADS Integration: Machine-readable API endpoints for Authorization Data Sharing compliance
• Implementation Details: Effort estimates, prerequisites, cost considerations, responsible parties (Security, DevOps, Compliance, Engineering)
• Code Examples: Infrastructure-as-code templates (Bicep, Terraform) and automation scripts

Coverage: 100% of active KSIs across all 11 families:

• IAM (7): MFA, Privilege Management, Session Controls, Credential Lifecycle, User Termination, JIT Access, Shared Accounts
• CNA (8): Network Segmentation, TLS Configuration, Connection Logging, Azure Monitor, Key Vault, Secrets Detection, Container Registry, GitHub Security
• MLA (5): Log Aggregation, Retention, Tamper Detection, Search Capabilities, Alerting
• INR (3): Incident Response Plans, Incident Logging, After Action Reports
• AFR (11): Asset Discovery, SBOM, Penetration Testing, Dev/Prod Separation, GitHub Actions Security, Container Scanning, and more
• SVC (9): FIPS 140-3 Cryptography, API Authentication, Rate Limiting, Input Validation, Error Handling, Secrets Management, DoS Protection, Secrets Rotation, Dependency Management
• CMT (4): Continuous Monitoring, Health Checks, Configuration Baselines, Drift Detection
• CED (4): Authorization Documents, System Boundary, SSP Updates, Continuous Delivery
• TPR (2): Supply Chain Risk Assessment, Supply Chain Risk Monitoring
• RPL (4): Recovery Objectives, Recovery Plans, System Backups, Recovery Testing
• PIY (8): Automated Inventory, Data Minimization, Vulnerability Disclosure, Secure By Design, Security Evaluations, Investment Effectiveness, Supply Chain Risk, Executive Support

Example: get_ksi_evidence_automation("KSI-IAM-01") returns automated evidence collection for phishing-resistant MFA including Entra ID Conditional Access policies, sign-in logs via Log Analytics, MFA method registration queries, and compliance reporting dashboards.

get_ksi_evidence_queries

Get ready-to-use evidence collection queries for a specific KSI.

Parameters:

• ksi_id (string): The KSI identifier (e.g., "KSI-IAM-01", "KSI-CNA-01")

Returns: Production-ready queries for collecting evidence from Azure (5 queries per KSI):

• KQL Queries: Log Analytics/Azure Monitor Kusto queries for log analysis and metrics
• Azure Resource Graph: Infrastructure and configuration state queries
• REST API: Azure Resource Manager API calls for programmatic data retrieval
• Azure CLI: Command-line scripts for evidence extraction
• PowerShell: Azure PowerShell cmdlets for automated collection

Example: get_ksi_evidence_queries("KSI-CNA-01") returns Resource Graph queries for NSG rules, Azure Firewall policies, virtual network configurations, subnet segmentation analysis, and network topology validation.

get_ksi_evidence_artifacts

Get specifications for evidence artifacts to collect for a specific KSI.

Parameters:

• ksi_id (string): The KSI identifier (e.g., "KSI-IAM-01", "KSI-CNA-01")

Returns: Detailed artifact specifications (5 artifacts per KSI):

• Artifact Names: Specific evidence files/reports required (e.g., "MFA_Sign_In_Logs.csv", "NSG_Rule_Export.json")
• Collection Methods: How to gather each artifact (automated export, API retrieval, dashboard screenshot, compliance report)
• File Formats: CSV, JSON, PDF, PNG (dashboard screenshots), XLSX (compliance matrices)
• Update Frequencies: Daily, weekly, monthly, or quarterly collection schedules based on requirement criticality
• Retention Requirements: 30-90 days for operational data, 1-7 years for compliance evidence
• Storage Recommendations: Azure Blob Storage with encryption, access logging, immutability policies

Example: get_ksi_evidence_artifacts("KSI-IAM-01") returns sign-in logs (CSV, daily, 90 days), Conditional Access policy exports (JSON, weekly, 1 year), MFA method registration reports (XLSX, monthly, 3 years), authentication dashboard screenshots (PNG, quarterly, 1 year), and MFA compliance matrices (PDF, monthly, 7 years).

analyze_frr_code

Analyze code against a specific FedRAMP Requirement (FRR) for compliance issues.

Parameters:

• frr_id (string): FRR identifier (e.g., "FRR-VDR-01", "FRR-RSC-01", "FRR-ADS-01")
• code (string): Code to analyze
• language (string): Language/platform - "python", "csharp", "java", "typescript", "bicep", "terraform", "github-actions", "azure-pipelines", "gitlab-ci"
• file_path (string, optional): File path for context

Returns: Analysis results with findings, severity levels, and remediation recommendations

Supported FRR Families:

• VDR - Vulnerability Detection and Response (59 requirements): Vulnerability scanning, patch management, remediation timeframes, deviation tracking, KEV vulnerability handling
• RSC - Recommended Secure Configuration (10 requirements): Security baselines, configuration management, hardening standards
• UCM - Using Cryptographic Modules (4 requirements): FIPS 140-3 compliance, key management, encryption standards
• SCN - Significant Change Notifications (26 requirements): Change management, notification procedures, impact assessment
• ADS - Authorization Data Sharing (22 requirements): Machine-readable evidence APIs, data formats, authentication
• CCM - Collaborative Continuous Monitoring (25 requirements): Monitoring procedures, quarterly reviews, assessment coordination
• MAS - Minimum Assessment Scope (12 requirements): Authorization boundaries, system inventory, assessment requirements
• ICP - Incident Communications Procedures (9 requirements): Incident notification, communication protocols, escalation procedures
• FSI - FedRAMP Security Inbox (16 requirements): Security inbox management, vulnerability disclosure, response procedures
• PVA - Persistent Validation and Assessment (22 requirements): Continuous validation, assessment procedures, testing requirements

What It Checks: Analyzes code for FRR-specific compliance issues using AST-powered semantic analysis:

• Application Code: Security controls, API implementations, cryptographic usage, logging, error handling
• Infrastructure as Code: Resource configurations, security settings, compliance controls, network policies
• CI/CD Pipelines: Security gates, testing requirements, deployment procedures, evidence collection

Example Usage:

# Check Python code for FRR-VDR-01 compliance (vulnerability scanning)
result = analyze_frr_code(
    frr_id="FRR-VDR-01",
    code="""import subprocess
    subprocess.run(['trivy', 'image', 'myapp:latest'])
    """,
    language="python"
)
# ✅ Detects Trivy vulnerability scanning implementation

# Check Bicep for FRR-ADS-01 compliance (machine-readable evidence)
result = analyze_frr_code(
    frr_id="FRR-ADS-01",
    code="""resource apiManagement 'Microsoft.ApiManagement/service@2023-05-01-preview' = {
      name: 'evidence-api'
      properties: {
        publisherEmail: 'admin@contoso.com'
        publisherName: 'Contoso'
      }
    }""",
    language="bicep"
)
# ✅ Validates API Management for authorization data sharing

analyze_all_frrs

Analyze code against all 199 FedRAMP requirements for comprehensive compliance checking.

Parameters:

• code (string): Code to analyze
• language (string): Language/platform (python, csharp, java, typescript, bicep, terraform, github-actions, azure-pipelines, gitlab-ci)
• file_path (string, optional): File path for context

Returns: Complete analysis results grouped by FRR family with summary statistics

Use Cases:

• Pre-deployment validation: Check all code before production deployment
• Comprehensive audits: Full compliance scan for certification preparation
• Security reviews: Identify all FedRAMP compliance gaps
• CI/CD integration: Automated compliance checking in pipelines

Output Structure:

• Findings organized by family (VDR, RSC, UCM, SCN, ADS, CCM, etc.)
• Summary statistics: total findings, critical/high/medium/low counts
• Compliant requirements listed
• Actionable remediation guidance

Example Usage:

# Comprehensive FRR analysis of Bicep infrastructure code
result = analyze_all_frrs(
    code=bicep_template,
    language="bicep",
    file_path="main.bicep"
)
# Returns findings across all 10 FRR families

Performance: Analyzes all 199 FRRs in 2-5 seconds using parallel processing and AST caching.

analyze_frr_family

Analyze code against all requirements in a specific FRR family.

Parameters:

• family (string): Family code - "VDR", "RSC", "UCM", "SCN", "ADS", "CCM", "MAS", "ICP", "FSI", "PVA"
• code (string): Code to analyze
• language (string): Language/platform
• file_path (string, optional): File path for context

Returns: Analysis results for all requirements in the specified family

Common Use Cases:

VDR Family (59 requirements):

# Check CI/CD pipeline for vulnerability management compliance
result = analyze_frr_family(
    family="VDR",
    code=github_actions_yaml,
    language="github-actions"
)
# Checks: Vulnerability scanning, patch procedures, remediation timeframes,
# deviation management, KEV tracking, monthly reporting

ADS Family (22 requirements):

# Validate authorization data sharing API implementation
result = analyze_frr_family(
    family="ADS",
    code=python_api_code,
    language="python"
)
# Checks: Machine-readable formats, API authentication, data accuracy,
# real-time updates, audit logging, access controls

RSC Family (10 requirements):

# Check infrastructure for secure configuration compliance
result = analyze_frr_family(
    family="RSC",
    code=terraform_code,
    language="terraform"
)
# Checks: Security baselines, configuration standards, hardening,
# drift detection, compliance validation

list_frrs_by_family

List all FRR requirements in a specific family with implementation status.

Parameters:

• family (string): Family code (VDR, RSC, UCM, SCN, ADS, CCM, MAS, ICP, FSI, PVA)

Returns: List of all FRRs in the family with:

• FRR ID and name
• Implementation status (Implemented/Not Implemented)
• Code detectability (Code-Detectable/Process-Based)
• NIST 800-53 control mappings
• Impact levels (Low/Moderate/High)

Example Usage:

# List all vulnerability detection requirements
result = list_frrs_by_family("VDR")
# Returns 59 VDR requirements with status indicators

# List all authorization data sharing requirements
result = list_frrs_by_family("ADS")
# Returns 22 ADS requirements

Use Cases:

• Discover available FRR requirements in a family
• Check implementation coverage
• Plan FRR implementation priorities
• Understand code vs. process requirements

get_frr_metadata

Get detailed metadata for a specific FRR including NIST controls, related KSIs, and detection strategy.

Parameters:

• frr_id (string): FRR identifier (e.g., "FRR-VDR-01")

Returns: Comprehensive FRR metadata including:

• FRR Details: ID, name, family, requirement statement
• NIST 800-53 Controls: Related security controls with full titles
• Related KSIs: Key Security Indicators that align with this FRR
• Impact Levels: Applicable authorization levels (Low/Moderate/High)
• Detection Strategy: How the requirement can be validated (code analysis, configuration checks, process review)
• Implementation Guidance: Azure-specific recommendations and best practices

Example Usage:

# Get metadata for FRR-VDR-01 (vulnerability scanning)
result = get_frr_metadata("FRR-VDR-01")
# Returns: NIST controls (RA-5, SI-2), related KSIs (KSI-AFR-04),
# detection strategy (CI/CD pipeline analysis, tool configuration checks)

# Get metadata for FRR-ADS-01 (machine-readable evidence)
result = get_frr_metadata("FRR-ADS-01")
# Returns: NIST controls (CA-2, CA-5, CA-7), related KSIs (KSI-CED-01),
# detection strategy (API endpoint analysis, data format validation)

Use Cases:

• Understand FRR requirements before implementation
• Map FRRs to NIST controls for compliance documentation
• Identify related KSIs for integrated compliance approach
• Get Azure-specific implementation guidance

get_frr_evidence_automation

Get evidence automation recommendations for a specific FRR.

Parameters:

• frr_id (string): FRR identifier (e.g., "FRR-VDR-01", "FRR-ADS-01")

Returns: Comprehensive evidence automation guidance including:

• Evidence Type: Configuration-based, log-based, or API-based evidence
• Automation Feasibility: High/Medium/Low automation potential
• Azure Services: 3-5 recommended services for evidence collection
• Collection Methods: Automated approaches (Azure Monitor, Resource Graph, Policy, APIs)
• Storage Requirements: Retention policies, encryption, access controls
• Evidence Artifacts: Specific files/reports to collect
• Implementation Steps: Step-by-step automation setup
• Code Examples: Infrastructure templates and scripts
• Update Frequency: Daily/weekly/monthly/quarterly collection schedules
• Responsible Party: Team ownership (Security, DevOps, Compliance)

Example Usage:

# Get evidence automation for FRR-VDR-01 (vulnerability scanning)
result = get_frr_evidence_automation("FRR-VDR-01")
# Returns: Azure Defender for Cloud configuration, KQL queries for
# vulnerability data, scan result export automation, compliance dashboards

# Get evidence automation for FRR-ADS-01 (data sharing API)
result = get_frr_evidence_automation("FRR-ADS-01")
# Returns: API Management setup, authentication configuration,
# audit logging, API call metrics, response format validation

Supported FRR Families:

• VDR: Vulnerability scan results, patch status, remediation tracking, deviation approvals
• ADS: API call logs, data format compliance, authentication records, access audits
• CCM: Monitoring metrics, quarterly review artifacts, assessment coordination logs
• RSC: Configuration baselines, drift detection reports, compliance scan results
• All others: Family-specific evidence recommendations

get_frr_implementation_status

Get implementation status summary across all FRR analyzers.

Parameters: None

Returns: Implementation status summary including:

• Total FRRs: 199 requirements across 10 families
• Implementation by Family: VDR (59), RSC (10), UCM (4), SCN (26), ADS (22), CCM (25), MAS (12), ICP (9), FSI (16), PVA (22)
• Status Breakdown: Implemented vs. Not Implemented counts
• Code-Detectable: Requirements that can be validated through code analysis
• Process-Based: Requirements requiring manual review or documentation
• Coverage Statistics: Implementation completion percentage by family

Example Usage:

# Get overall FRR implementation status
result = get_frr_implementation_status()
# Returns: Family-by-family breakdown with implementation rates

Use Cases:

• Track FRR analyzer development progress
• Identify gaps in FRR coverage
• Plan FRR implementation priorities
• Report on compliance automation capabilities

Output Example:

FRR Implementation Status:
- VDR Family: 59/59 implemented (100%)
- RSC Family: 10/10 implemented (100%)
- ADS Family: 22/22 implemented (100%)
- Total: 199/199 implemented (100%)

Code-Detectable: 145 FRRs (73%)
Process-Based: 54 FRRs (27%)

compare_with_rev4

Compare FedRAMP 20x with Rev 4/Rev 5 requirements for specific areas.

Parameters:

• requirement_area (string): Area to compare (e.g., "continuous monitoring", "vulnerability management", "authorization boundary", "evidence collection", "change management", "incident response")

get_implementation_examples

Get practical implementation examples for specific requirements.

Parameters:

• requirement_id (string): The requirement identifier (e.g., "KSI-IAM-01", "FRR-VDR-01")

check_requirement_dependencies

Check dependencies between FedRAMP 20x requirements.

Parameters:

• requirement_id (string): The requirement identifier to check dependencies for

estimate_implementation_effort

Estimate implementation effort for specific requirements.

Parameters:

• requirement_id (string): The requirement identifier to estimate effort for

get_cloud_native_guidance

Get cloud-native implementation guidance for specific Azure and multi-cloud technologies.

Parameters:

• technology (string): Technology to get guidance for (e.g., "kubernetes", "containers", "serverless", "terraform")

Note: All cloud examples and best practices prioritize Azure services (AKS, Azure Functions, Key Vault, Bicep, etc.)

validate_architecture

Validate a system architecture against FedRAMP 20x requirements.

Parameters:

• architecture_description (string): Description of the architecture to validate

search_documentation

Search FedRAMP official documentation markdown files for specific keywords.

Parameters:

• keywords (string): Keywords to search for in documentation

Returns: Matching documentation sections with context from all available markdown files

Note: Automatically loads all markdown files from the docs directory, so new documentation is always searchable.

get_documentation_file

Get the full content of a specific FedRAMP documentation file.

Parameters:

• filename (string): The markdown filename (e.g., "overview.md", "key-security-indicators.md")

Returns: Full markdown content of the documentation file

list_documentation_files

List all available FedRAMP documentation files.

Returns: Complete list of all markdown documentation files dynamically discovered from the repository

export_to_excel

Export FedRAMP 20x data to Excel files for offline analysis and reporting.

Parameters:

• export_type (string): Type of data to export:
  • "ksi" - All 72 Key Security Indicators
  • "all_requirements" - All 329 requirements across all families
  • "definitions" - All FedRAMP term definitions
• output_path (string, optional): Custom output path. If not provided, saves to Downloads folder

Returns: Path to the generated Excel file with professional formatting (styled headers, borders, frozen panes)

KSI Export Columns:

1. KSI ID - Unique identifier (e.g., KSI-AFR-01)
2. Name - KSI name
3. Category - Control family category
4. Status - Active or Retired
5. Statement - Full requirement statement
6. Note - Additional information (e.g., supersession notes for retired KSIs)
7. NIST 800-53 Controls - Related security controls with titles
8. Reference - Reference document name (if applicable)
9. Reference URL - Link to FedRAMP documentation (if applicable)
10. Impact Levels - Applicable levels (Low, Moderate, High)

All Requirements Export Columns:

1. Requirement ID - Unique identifier
2. Family - Control family
3. Term/Name - Requirement name
4. Description - Full description
5. Document - Source document

Definitions Export Columns:

1. Term - FedRAMP term
2. Definition - Term definition
3. Notes - Additional context
4. References - Related documentation

Example usage:

• Export all KSIs: export_to_excel("ksi")
• Export all requirements: export_to_excel("all_requirements")
• Export definitions: export_to_excel("definitions")

export_to_csv

Export FedRAMP 20x data to CSV files for data analysis and spreadsheet imports.

Parameters:

• export_type (string): Type of data to export:
  • "ksi" - All 72 Key Security Indicators
  • "all_requirements" - All 329 requirements across all families
  • "definitions" - All FedRAMP term definitions
• output_path (string, optional): Custom output path. If not provided, saves to Downloads folder

Returns: Path to the generated CSV file

Columns: Same structure as Excel export (see above for detailed column descriptions)

Example usage:

• Export all KSIs: export_to_csv("ksi")
• Export all requirements: export_to_csv("all_requirements")
• Export definitions: export_to_csv("definitions")

generate_ksi_specification

Generate a comprehensive product specification Word document for a KSI to guide engineering implementation and planning.

Parameters:

• ksi_id (string): The KSI identifier (e.g., "KSI-AFR-01")
• evidence_collection_strategy (string): High-level evidence collection strategy description provided by the user
• output_path (string, optional): Custom output path. If not provided, saves to Downloads folder

Returns: Path to the generated Word (.docx) document

Document Contents:

• Metadata: KSI ID, category, impact levels, status, date
• Overview: Purpose and scope aligned with FedRAMP 20x
• Requirement Statement: Full KSI requirement text
• NIST 800-53 Controls: Related security controls with titles
• Azure-First Implementation: Recommended Azure services, IaC guidance, automation strategies
• Evidence Collection: User-defined strategy + recommended evidence types and flexible collection schedule
• 5-Phase Implementation Plan: Requirements analysis → Design → Implementation → Testing → Documentation (engineering teams determine timelines)
• Team Roles: Cloud architect, DevOps, security engineer, compliance specialist, etc.
• Success Criteria: Measurable outcomes for implementation validation
• Risks and Mitigation: Common risks with Azure-specific mitigation strategies
• Resources: Links to FedRAMP, NIST, Azure documentation

Azure Services Recommended (context-aware based on KSI category):

• Microsoft Entra ID, Azure Policy, Azure Monitor (all KSIs)
• Microsoft Defender for Cloud, Azure Key Vault, Azure Firewall (category-specific)
• Microsoft Sentinel, Azure Automation, Log Analytics (control-specific)

Example usage:

Generate specification for KSI-AFR-01:
> generate_ksi_specification with ksi_id="KSI-AFR-01" 
  and evidence_collection_strategy="Collect Azure Policy compliance reports quarterly using Azure Automation runbooks. Store evidence in Azure Blob Storage with immutable storage policy."

generate_implementation_questions

Generate strategic interview questions for product managers and engineers to facilitate thoughtful planning discussions.

Parameters:

• requirement_id (string): The requirement or KSI identifier (e.g., "FRR-CCM-01", "KSI-IAM-01")

Returns: Comprehensive set of strategic questions organized by stakeholder role

Question Categories:

1. Strategic Questions for Product Managers (10 questions):

   • Business Impact & ROI
   • Customer Value & Competitive Position
   • Resource Allocation & Prioritization
   • Dependencies & Phasing

2. Technical Questions for Engineers (15 questions):

   • Architecture & Design Decisions
   • Azure Service Selection
   • Automation Opportunities
   • Monitoring & Evidence Collection
   • Operations & Maintenance

3. Cross-Functional Questions (10 questions):

   • Security & Compliance Integration
   • User Experience Impact
   • Training & Support Needs
   • Incident Response Alignment

4. Azure-Specific Considerations (dynamic, up to 20 questions):

   • Microsoft Entra ID configuration
   • Azure RBAC and Conditional Access
   • Log Analytics and Sentinel integration
   • Azure Policy and governance
   • Defender for Cloud setup
   • Key Vault and encryption strategy

Additional Guidance:

• Decision Framework (5 must-answer questions before implementation)
• Success Criteria (5 measurable outcomes)
• Red Flags (5 warning signs to watch for)
• Next Steps (9-phase implementation approach)
• Recommended Resources (Microsoft docs, FedRAMP resources, community)

analyze_infrastructure_code

Analyze Infrastructure as Code (IaC) files for FedRAMP 20x compliance issues and provide actionable recommendations.

Parameters:

• code (string): The IaC code content to analyze
• file_type (string): Type of IaC file - "bicep" or "terraform"
• file_path (string): Path to the file being analyzed (for reporting)
• context (string, optional): Additional context about the code (e.g., PR description)

Returns:

• findings: Array of compliance findings with requirement IDs, severity, descriptions, and recommendations
• summary: Counts of high/medium/low priority issues and good practices
• pr_comment: Formatted markdown suitable for GitHub/ADO PR comments

Supported Languages:

• Bicep: Azure Resource Manager templates
• Terraform: Azure RM provider resources

What It Checks: Analyzes your infrastructure code against 40+ FedRAMP KSIs including:

• Identity & Access Management (MFA, RBAC, privileged access)
• Service Configuration (encryption, secrets management, backups)
• Network Architecture (security groups, DDoS protection, segmentation)
• Monitoring & Logging (diagnostic settings, audit logs, alerting)
• DevSecOps (change management, vulnerability scanning, testing)
• Privacy & Data Protection (data classification, retention policies)
• Incident Response (detection, logging, automation)
• Supply Chain Security (container scanning, trusted registries)

Example Usage:

// This Bicep code will be flagged for missing diagnostic settings
resource storageAccount 'Microsoft.Storage/storageAccounts@2023-01-01' = {
  name: 'mystorageaccount'
  location: location
  properties: {
    // Missing: diagnostic settings for KSI-MLA-05
  }
}

analyze_application_code

Analyze application code for FedRAMP 20x security compliance issues.

Parameters:

• code (string): The application code content to analyze
• language (string): Programming language - "python", "csharp", "java", "typescript", or "javascript"
• file_path (string): Path to the file being analyzed (for reporting)
• dependencies (array, optional): List of project dependencies (e.g., ["flask==2.3.0", "requests==2.31.0"])

Returns:

• findings: Array of security findings (same structure as infrastructure analysis)
• summary: Counts of high/medium/low priority issues and good practices
• pr_comment: Formatted markdown for PR reviews
• dependencies_checked: Number of dependencies analyzed

Supported Languages & Frameworks:

• Python: Flask, Django, FastAPI applications
• C#: ASP.NET Core, Entity Framework, Azure SDK for .NET
• Java: Spring Boot, Spring Security, Jakarta EE, Azure SDK for Java
• TypeScript/JavaScript: Express, NestJS, Next.js, React, Angular, Vue, Azure SDK for JS

FedRAMP Requirements Checked (Phase 1 + Phase 2):

Phase 1 - Foundation:

• KSI-IAM-01: API authentication and authorization
• KSI-SVC-06: Secret management (hardcoded passwords, API keys, certificates)
• KSI-SVC-05: Resource integrity (vulnerable libraries, unsafe functions)
• KSI-PIY-02: Security objectives and requirements documentation
• KSI-MLA-05: Diagnostic logging configuration

Phase 2 - Application Security:

• KSI-IAM-05: Service account management (Managed Identity vs hardcoded credentials)
• KSI-CNA-03: Microservices security (service-to-service auth, mTLS, rate limiting)

Phase 3 - Secure Coding Practices:

• KSI-SVC-01: Continuous improvement (security evaluation and improvements)
• KSI-SVC-02: Network encryption (TLS, secure transmission)
• KSI-SVC-07: Patching (security updates, vulnerability remediation)
• KSI-PIY-01: Automated inventory (real-time asset tracking)
• KSI-PIY-03: Documentation requirements
• KSI-CNA-07: Service mesh security (Istio/Linkerd)
• KSI-IAM-04: Least privilege access
• KSI-IAM-07: Session management and token security

analyze_cicd_pipeline

Analyze CI/CD pipeline configurations for FedRAMP 20x DevSecOps compliance.

Parameters:

• code (string): The pipeline configuration content (YAML/JSON)
• pipeline_type (string): Type of pipeline - "github-actions", "azure-pipelines", "gitlab-ci", or "generic"
• file_path (string): Path to the pipeline file (for reporting)

Returns:

• findings: Array of DevSecOps findings (same structure as code analysis)
• summary: Counts of high/medium/low priority issues and good practices
• pr_comment: Formatted markdown for PR reviews
• pipeline_type: The detected pipeline platform

Supported Platforms:

• GitHub Actions (.github/workflows/*.yml)
• Azure DevOps Pipelines (azure-pipelines.yml)
• GitLab CI/CD (.gitlab-ci.yml)

FedRAMP Requirements Checked (Phase 4):

• KSI-CMT-01: Change management automation (PR triggers, required reviews, branch protection)
• KSI-CMT-02: Deployment procedures (approval gates, environment protection, rollback capabilities)
• KSI-CMT-03: Automated testing in CI/CD (unit tests, security scans in pipeline)
• KSI-AFR-01: Automated vulnerability scanning (container, IaC, SAST/DAST tools)
• KSI-AFR-02: Security finding remediation (blocking on vulnerabilities, automated issue creation)
• KSI-CED-01: Continuous evidence collection (artifact uploads, test results, retention policies)

Example usage:

# GitHub Actions workflow that will be flagged for missing security scans
name: Build
on: [push]

jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - run: docker build -t myapp .
      - run: docker push myapp:latest
      # ❌ No vulnerability scanning
      # ❌ No test execution
      # ❌ No evidence collection

💡 Result: Analyzer recommends adding Trivy container scanning, unit test execution, security gates, and artifact uploads for compliance evidence.

Example usage:

# This Python code will be flagged for multiple issues
from flask import Flask

app = Flask(__name__)
API_KEY = "sk-1234567890abcdef"  # KSI-SVC-06: Hardcoded secret

@app.route('/api/users')  # KSI-IAM-01: Missing authentication
def get_users():
    users = [
        {'name': 'Alice', 'ssn': '123-45-6789'},  # PII in logs - see NIST SI-12
    ]
    return {'users': users}

Automated PR Review Workflow:

1. Code submitted via PR (GitHub/Azure DevOps)
2. Analyzer tools examine IaC and application code
3. Findings generated with FedRAMP requirement citations
4. PR comment posted with compliance review
5. Developers address issues before merge

Purpose: Help teams think deeply about implementation considerations, trade-offs, and success criteria before committing resources. Questions are designed to facilitate planning sessions, design reviews, and stakeholder alignment.

get_infrastructure_code_for_ksi

Generate Infrastructure as Code templates (Bicep or Terraform) for automated evidence collection infrastructure.

Parameters:

• ksi_id (string): The Key Security Indicator identifier (e.g., "KSI-IAM-01", "KSI-MLA-01")
• infrastructure_type (string): Either "bicep" or "terraform"

Returns: Complete IaC templates for deploying evidence collection infrastructure

Supported KSI Families:

• IAM (Identity and Access Management): Microsoft Entra ID, Log Analytics workspaces, diagnostic settings, automation accounts
• MLA (Monitoring, Logging, and Auditing): Log Analytics workspaces, Azure Sentinel, diagnostic settings, alert rules
• AFR (Audit and Financial Reporting): Storage accounts with immutability, event subscriptions, audit logs
• CNA (Change Notification and Approval): Event Grid topics, Logic Apps, DevOps pipelines, change tracking
• RPL (Release Pipeline): Azure DevOps pipelines, deployment slots, rollback capabilities, approval gates
• SVC (Service and Vulnerability Management): Defender for Cloud, security assessments, compliance dashboards

Example Usage:

> get_infrastructure_code_for_ksi with ksi_id="KSI-IAM-01" and infrastructure_type="bicep"

Output Includes:

• Azure resource definitions (Log Analytics, Storage, Event Grid, etc.)
• Diagnostic settings for evidence collection
• Retention policies and immutability
• Integration with Azure Monitor and Sentinel
• Automation for evidence gathering
• RBAC roles and permissions

get_evidence_collection_code

Generate business logic code (Python, C#, PowerShell, Java, or TypeScript) for collecting and storing KSI evidence programmatically.

Parameters:

• ksi_id (string): The Key Security Indicator identifier (e.g., "KSI-IAM-01")
• language (string): Either "python", "csharp", "powershell", "java", or "typescript" (also accepts "javascript")

Returns: Complete code examples with authentication, evidence collection, and storage

Code Features:

• Authentication: Azure DefaultAzureCredential pattern for managed identity or local development
• Evidence Collection: SDKs for Microsoft Graph API, Azure Resource Manager, Azure Monitor
• Evidence Storage: Save to Azure Blob Storage with immutability and metadata tagging
• Error Handling: Comprehensive try-catch patterns and logging
• Documentation: Inline comments explaining each step

Supported Languages:

• Python: Uses azure-identity, azure-storage-blob, azure-monitor-query, msgraph-sdk
• C#: Uses Azure.Identity, Azure.Storage.Blobs, Azure.Monitor.Query, Microsoft.Graph
• PowerShell: Uses Az.Accounts, Az.Storage, Az.Monitor, Microsoft.Graph modules
• Java: Uses Azure Identity, Azure Storage Blobs, Azure Resource Manager, Microsoft Graph SDK
• TypeScript/JavaScript: Uses @azure/identity, @azure/storage-blob, @azure/arm-resources, @microsoft/microsoft-graph-client

Example Usage:

> get_evidence_collection_code with ksi_id="KSI-MLA-01" and language="python"

Output Includes:

• SDK imports and authentication setup
• Evidence collection logic specific to the KSI
• JSON formatting and metadata tagging
• Blob storage upload with immutability
• Error handling and retry logic

get_evidence_automation_architecture

Get comprehensive architecture guidance for automated evidence collection systems.

Parameters:

• scope (string): Architecture scope - "minimal", "single-ksi", "category", or "all"

Returns: Complete architecture patterns with components, data flows, and implementation guidance

Architecture Scopes:

1. minimal: Quick-start architecture for pilot projects

   • Single Log Analytics workspace
   • Azure Function for scheduled evidence collection
   • Blob storage with basic retention
   • Event Grid for notifications

2. single-ksi: Production architecture for one KSI

   • Dedicated evidence collection infrastructure
   • Azure Functions with monitoring
   • Managed identities for security
   • Sentinel integration

3. category: Enterprise architecture for one KSI category (IAM, MLA, etc.)

   • Category-specific evidence collectors
   • Centralized evidence storage
   • Automated reporting dashboards
   • Integration with Azure Policy

4. all: Complete enterprise architecture for all 72 KSIs

   • Multi-region evidence collection
   • High-availability design
   • Automated compliance reporting
   • Integration with GRC tools

Example Usage:

> get_evidence_automation_architecture with scope="all"

Output Includes:

• Component diagram and descriptions
• Data flow architecture
• Security and identity patterns
• Monitoring and alerting strategy
• Evidence storage and retention
• Disaster recovery considerations
• Integration patterns with Azure services
• Scaling recommendations
• Implementation steps

Available Prompts

The server provides 18 prompts for FedRAMP compliance workflows:

Comprehensive Planning & Assessment Prompts

• initial_assessment_roadmap - Complete 6-phase roadmap for FedRAMP 20x authorization with checklists, deliverables, and critical success factors (engineering teams determine timelines)
• gap_analysis - Detailed gap analysis framework comparing current state against FedRAMP 20x requirements with prioritization and remediation planning
• vendor_evaluation - Comprehensive vendor assessment framework with category-specific questions, scorecard template, and evaluation criteria
• migration_from_rev5 - Detailed migration plan from FedRAMP Rev 5 to 20x with 7-phase approach, gap analysis, and requirement mapping (teams determine timelines and budgets)
• significant_change_assessment - Framework for evaluating significant changes per FRR-CCM-SC including impact analysis, testing requirements, and authorization update triggers

Implementation & Automation Prompts

• ksi_implementation_priorities - Prioritized guide for implementing all 72 Key Security Indicators across 8 priority phases with dependency mapping (engineering teams determine rollout timelines)
• azure_ksi_automation - Complete guide for implementing all 72 KSIs using Microsoft, Azure, and M365 capabilities including PowerShell scripts, Azure CLI commands, Microsoft Graph API integration, KQL queries, Azure Functions/Logic Apps, evidence collection framework, and integration with Defender suite, Entra ID, Key Vault, and Sentinel
• api_design_guide - Complete guide for Authorization Data Sharing API (FRR-ADS) with endpoints, authentication, OSCAL formats, and examples
• authorization_boundary_review - Guidance for defining and documenting authorization boundaries, system interconnections, and data flows per FedRAMP 20x requirements

Monitoring & Compliance Prompts

• continuous_monitoring_setup - Guide for establishing continuous monitoring programs aligned with FedRAMP 20x requirements including automation, metrics, and reporting
• quarterly_review_checklist - Comprehensive checklist for FedRAMP 20x quarterly reviews (FRR-CCM-QR) covering all 72 KSIs, vulnerability review, and change review
• vulnerability_remediation_timeline - Timeline and prioritization framework for vulnerability remediation aligned with FedRAMP 20x VDR requirements

Audit & Documentation Prompts

• audit_preparation - Comprehensive guide for FedRAMP 20x assessment preparation with evidence gathering, common findings, and interview prep (teams determine preparation timeline)
• ato_package_checklist - Complete checklist for Authority to Operate (ATO) package preparation including all required artifacts, templates, and submission requirements
• documentation_generator - OSCAL SSP templates, procedure templates (VDR, ICP, SCN), and KSI implementation documentation templates

FRR Compliance & Code Review Prompts

• frr_code_review - Guide for reviewing code against FedRAMP Requirements (FRR) using AST-powered semantic analysis across all 10 FRR families (VDR, ADS, RSC, UCM, CCM, SCN, MAS, ICP, FSI, PVA) with PR workflow integration
• frr_family_assessment - Comprehensive family-specific assessment guide for all 199 FRR requirements with detailed checklists, assessment questions, and evidence planning for each of the 10 FRR families
• frr_implementation_roadmap - Strategic 16-week, 4-phase roadmap for implementing all 199 FRR requirements with prioritization framework, Azure service recommendations, and KSI integration strategies

Combined KSI + FRR Analysis Examples

The MCP server provides integrated analysis capabilities combining Key Security Indicators (KSI) with FedRAMP Revised Requirements (FRR) for comprehensive compliance validation. These examples demonstrate how to use both KSI code analyzers and FRR analysis tools together for holistic security assessments.

Example 1: Network Segmentation (KSI-CNA-01 + FRR-RSC Family)

Scenario: Validate network security controls for both KSI tracking and FRR compliance

# Step 1: Analyze infrastructure code with KSI analyzer
from analyzers.ksi.factory import get_factory

bicep_code = """
resource nsg 'Microsoft.Network/networkSecurityGroups@2023-11-01' = {
  name: 'prod-nsg'
  location: location
  properties: {
    securityRules: [
      {
        name: 'allow-https'
        properties: {
          priority: 100
          direction: 'Inbound'
          access: 'Allow'
          protocol: 'Tcp'
          sourceAddressPrefix: '*'
          destinationAddressPrefix: '*'
          destinationPortRange: '443'
        }
      }
    ]
  }
}
"""

# Analyze against KSI-CNA-01 (Network Segmentation)
factory = get_factory()
ksi_result = factory.analyze("KSI-CNA-01", bicep_code, "bicep", "nsg.bicep")

print(f"KSI-CNA-01 Analysis:")
print(f"  Compliant: {ksi_result.is_compliant}")
print(f"  Findings: {len(ksi_result.findings)}")
for finding in ksi_result.findings:
    print(f"  - {finding.severity.value}: {finding.message}")

# Step 2: Analyze against FRR-RSC (Secure Configuration) requirements
frr_analysis = analyze_frr_family(
    family="RSC",
    code=bicep_code,
    language="bicep",
    file_path="nsg.bicep"
)

print(f"\nFRR-RSC Family Analysis:")
print(f"  Total FRRs: {frr_analysis['total_frrs']}")
print(f"  Compliant: {frr_analysis['compliant_count']}")
print(f"  Non-compliant: {frr_analysis['non_compliant_count']}")

# Step 3: Get integrated remediation guidance
for frr_id, result in frr_analysis['results'].items():
    if not result['compliant']:
        print(f"\n{frr_id}: {result['frr_name']}")
        print(f"  Related KSI: {get_frr_metadata(frr_id)['related_ksis']}")
        print(f"  Remediation: {result['recommendation']}")

# Output:
# KSI-CNA-01 Analysis:
#   Compliant: False
#   Findings: 2
#   - HIGH: NSG allows traffic from any source (sourceAddressPrefix: *)
#   - MEDIUM: NSG rule priority too permissive (100 vs recommended >1000)
#
# FRR-RSC Family Analysis:
#   Total FRRs: 10
#   Compliant: 7
#   Non-compliant: 3
#
# FRR-RSC-01: Network Security Groups Configuration
#   Related KSI: ['KSI-CNA-01', 'KSI-CNA-03']
#   Remediation: Use specific source IP ranges, implement deny-by-default rules

Integration Benefits:

• KSI analysis provides tactical code-level findings
• FRR analysis provides strategic compliance coverage
• Combined view shows both security defects and policy compliance
• Remediation guidance links code fixes to compliance requirements

Example 2: Vulnerability Management (KSI-AFR-04 + FRR-VDR Family)

Scenario: Validate vulnerability scanning configuration and remediation processes

# Step 1: Analyze vulnerability scanning implementation with KSI
cicd_pipeline = """
name: Security Scanning
on:
  push:
    branches: [main]
  pull_request:
  schedule:
    - cron: '0 0 * * *'  # Daily scans

jobs:
  vulnerability-scan:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      
      - name: Run Trivy vulnerability scanner
        uses: aquasecurity/trivy-action@master
        with:
          scan-type: 'fs'
          scan-ref: '.'
          format: 'json'
          output: 'trivy-results.json'
          severity: 'CRITICAL,HIGH,MEDIUM'
      
      - name: Upload scan results
        uses: actions/upload-artifact@v3
        with:
          name: trivy-results
          path: trivy-results.json
          retention-days: 90
"""

# Analyze against KSI-AFR-04 (Vulnerability Detection)
ksi_result = factory.analyze("KSI-AFR-04", cicd_pipeline, "yaml", ".github/workflows/security.yml")

print(f"KSI-AFR-04 Analysis:")
print(f"  Compliant: {ksi_result.is_compliant}")
print(f"  Evidence: {ksi_result.evidence}")

# Step 2: Analyze against all FRR-VDR requirements (59 total)
vdr_analysis = analyze_frr_family(
    family="VDR",
    code=cicd_pipeline,
    language="yaml",
    file_path=".github/workflows/security.yml"
)

print(f"\nFRR-VDR Family Analysis (59 requirements):")
print(f"  Scan configuration: {vdr_analysis['results']['FRR-VDR-01']['compliant']}")
print(f"  Remediation timeframes: {vdr_analysis['results']['FRR-VDR-TF-01']['compliant']}")
print(f"  KEV tracking: {vdr_analysis['results']['FRR-VDR-TF-02']['compliant']}")
print(f"  Authenticated scanning: {vdr_analysis['results']['FRR-VDR-AC-01']['compliant']}")

# Step 3: Get evidence automation recommendations
evidence_guidance = get_frr_evidence_automation("FRR-VDR-01")
ksi_evidence = get_ksi_evidence_automation("KSI-AFR-04")

print(f"\nIntegrated Evidence Collection:")
print(f"  FRR-VDR-01 Artifacts: {evidence_guidance['evidence_artifacts']}")
print(f"  KSI-AFR-04 Queries: {ksi_evidence['collection_methods']}")
print(f"  Storage: Azure Blob (retention: 730 days per FRR-VDR, KSI-MLA-02)")

# Output:
# KSI-AFR-04 Analysis:
#   Compliant: True
#   Evidence: Daily vulnerability scans, artifact retention 90 days
#
# FRR-VDR Family Analysis (59 requirements):
#   Scan configuration: True
#   Remediation timeframes: False (missing deadline tracking)
#   KEV tracking: False (no CISA KEV integration)
#   Authenticated scanning: False (filesystem scan only)
#
# Integrated Evidence Collection:
#   FRR-VDR-01 Artifacts: ['scan-results.json', 'remediation-status.csv', 'kev-report.pdf']
#   KSI-AFR-04 Queries: ['Log Analytics vulnerability metrics', 'Defender scan results']
#   Storage: Azure Blob (retention: 730 days per FRR-VDR, KSI-MLA-02)

Integration Benefits:

• KSI-AFR-04 validates scanning tool configuration
• FRR-VDR family checks compliance with all 59 vulnerability requirements
• Combined analysis identifies gaps (KEV tracking, authenticated scans)
• Evidence automation recommendations cover both KSI and FRR needs

Example 3: Authorization Data Sharing (KSI-CED-01 + FRR-ADS Family)

Scenario: Validate API implementation for machine-readable compliance data

# Step 1: Analyze API implementation with KSI
api_code = """
@app.route('/api/authorization/technical-controls', methods=['GET'])
@require_auth
def get_technical_controls():
    \"\"\"FRR-ADS-TC-02: Technical controls data endpoint\"\"\"
    
    # Query compliance data from storage
    controls_data = storage_client.get_blob(
        container='security-controls',
        blob='latest-technical-controls.json'
    )
    
    response = {
        'metadata': {
            'timestamp': datetime.utcnow().isoformat(),
            'version': '1.0',
            'frr_requirement': 'FRR-ADS-TC-02',
            'classification': 'CUI'
        },
        'data': json.loads(controls_data)
    }
    
    return jsonify(response), 200
"""

# Analyze against KSI-CED-01 (Continuous Evidence Delivery)
ksi_result = factory.analyze("KSI-CED-01", api_code, "python", "api/authorization.py")

print(f"KSI-CED-01 Analysis:")
print(f"  API endpoint defined: {ksi_result.is_compliant}")
print(f"  Authentication required: {'@require_auth' in api_code}")

# Step 2: Analyze against FRR-ADS family (22 requirements)
ads_analysis = analyze_frr_family(
    family="ADS",
    code=api_code,
    language="python",
    file_path="api/authorization.py"
)

print(f"\nFRR-ADS Family Analysis (22 requirements):")
for frr_id in ['FRR-ADS-01', 'FRR-ADS-02', 'FRR-ADS-AC-01', 'FRR-ADS-TC-02']:
    result = ads_analysis['results'][frr_id]
    print(f"  {frr_id}: {result['compliant']} - {result['frr_name']}")

# Step 3: List all required FRR-ADS-TC endpoints
tc_endpoints = list_frrs_by_family("ADS")
tc_frrs = [frr for frr in tc_endpoints if 'TC-' in frr['frr_id']]

print(f"\nRequired FRR-ADS-TC Endpoints ({len(tc_frrs)} total):")
for frr in tc_frrs:
    print(f"  {frr['frr_id']}: {frr['name']}")
    metadata = get_frr_metadata(frr['frr_id'])
    print(f"    Related KSI: {metadata['related_ksis']}")

# Output:
# KSI-CED-01 Analysis:
#   API endpoint defined: True
#   Authentication required: True
#
# FRR-ADS Family Analysis (22 requirements):
#   FRR-ADS-01: True - Machine-readable authorization data
#   FRR-ADS-02: True - Real-time compliance data API
#   FRR-ADS-AC-01: True - API authentication and access control
#   FRR-ADS-TC-02: True - Technical controls data endpoint
#
# Required FRR-ADS-TC Endpoints (7 total):
#   FRR-ADS-TC-01: Continuous monitoring data
#     Related KSI: ['KSI-CED-01', 'KSI-MLA-01']
#   FRR-ADS-TC-02: Technical controls data
#     Related KSI: ['KSI-CED-01']
#   FRR-ADS-TC-03: Vulnerability data
#     Related KSI: ['KSI-CED-01', 'KSI-AFR-04']
#   [... 4 more endpoints ...]

Integration Benefits:

• KSI-CED-01 validates evidence delivery mechanism
• FRR-ADS family ensures all 22 authorization data sharing requirements met
• Combined analysis identifies which KSIs map to which FRR endpoints
• Comprehensive coverage of machine-readable compliance data requirements

Example 4: Comprehensive Pre-Deployment Scan

Scenario: Full compliance check before production deployment combining all KSIs and FRRs

# Complete infrastructure/application scan
terraform_infra = open('main.tf', 'r').read()
app_code = open('app.py', 'r').read()
cicd_pipeline = open('.github/workflows/deploy.yml', 'r').read()

# Step 1: Analyze against all 72 KSIs
print("KSI Analysis (72 indicators):")
all_ksi_results = factory.analyze_all_ksis(terraform_infra, "terraform", "main.tf")
ksi_summary = {
    'compliant': sum(1 for r in all_ksi_results if r.is_compliant),
    'non_compliant': sum(1 for r in all_ksi_results if not r.is_compliant),
    'high_severity': sum(1 for r in all_ksi_results for f in r.findings if f.severity.value == 'HIGH')
}
print(f"  Total KSIs analyzed: {len(all_ksi_results)}")
print(f"  Compliant: {ksi_summary['compliant']}")
print(f"  Non-compliant: {ksi_summary['non_compliant']}")
print(f"  High-severity findings: {ksi_summary['high_severity']}")

# Step 2: Analyze against all 199 FRRs
print("\nFRR Analysis (199 requirements):")
all_frr_results = analyze_all_frrs(
    code=terraform_infra,
    language="terraform",
    file_path="main.tf"
)

frr_summary = {
    'compliant': all_frr