FedRAMP 20x MCP Server

An MCP (Model Context Protocol) server that provides access to FedRAMP 20x security requirements and controls with Azure-first guidance.

Overview

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

Data Sources:

Requirements Data: JSON files from github.com/FedRAMP/docs (root directory)
Documentation: Markdown files from github.com/FedRAMP/docs/tree/main/docs

Azure Focus: All implementation examples, architecture patterns, and vendor recommendations prioritize Microsoft Azure services (Azure Government, Microsoft Entra ID, Azure Key Vault, AKS, Azure Functions, Bicep, etc.) while remaining cloud-agnostic where appropriate.

Data Coverage

The server provides access to 321 requirements (199 FRRs + 72 KSIs + 50 FRDs) across FedRAMP 20x documents:

FedRAMP Requirements (FRR) - 199 requirements across 10 families:

ADS - Authorization Data Sharing (20 requirements)
CCM - Collaborative Continuous Monitoring (25 requirements)
FSI - FedRAMP Security Inbox (16 requirements)
ICP - Incident Communications Procedures (9 requirements)
MAS - Minimum Assessment Scope (12 requirements)
PVA - Persistent Validation and Assessment (22 requirements)
RSC - Recommended Secure Configuration (10 requirements)
SCN - Significant Change Notifications (22 requirements)
UCM - Using Cryptographic Modules (4 requirements)
VDR - Vulnerability Detection and Response (57 requirements)
KSI - Key Security Indicators (2 requirements)

Key Security Indicators (KSI) - 72 indicators across 11 families:

AFR - Architecture, Features, and Resources (11 indicators)
CED - Continuous Evidence Delivery (4 indicators)
CMT - Continuous Monitoring and Testing (5 indicators)
CNA - Cloud Native Architecture (8 indicators)
IAM - Identity and Access Management (7 indicators)
INR - Incident and Near-Miss Reporting (3 indicators)
MLA - Monitoring, Logging, and Alerting (8 indicators)
PIY - Privacy and Transparency (8 indicators)
RPL - Resilience and Recovery Planning (4 indicators)
SVC - Secure Coding and Vulnerability Management (10 indicators)
TPR - Third-Party Risk Management (4 indicators)

FedRAMP Definitions (FRD) - 50 official term definitions

Features

🎯 Automated Evidence Collection (NEW): Automation guidance for 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, supporting compliance analysis for 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)
KSI & FRR Mapping: 72 KSIs and 199 FRRs supported through pattern definitions

How It Works:

Pattern Loading: YAML patterns loaded from data/patterns/ directory
Analysis Execution: Code analyzed using tree-sitter AST parsing with pattern matching
Finding Generation: Patterns generate findings with severity, description, and remediation
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.

Security

Vulnerability Disclosure: If you discover a security vulnerability, please see our Security Policy for responsible disclosure procedures (KSI-PIY-03).

Audit Logging: All MCP server operations are logged to stderr for audit purposes (KSI-MLA-05).

Security Features:

✅ No authentication required (local development tool)
✅ No Federal Customer Data handling
✅ HTTPS-only connections to GitHub
✅ 1-hour cache TTL reduces external requests
✅ All dependencies use minimum secure versions

For complete security documentation, see SECURITY.md.

Usage

With VS Code and GitHub Copilot

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

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.

Optional: Configure VS Code settings by copying .vscode/settings.json.example to .vscode/settings.json
Reload VS Code to activate the MCP server
Grant permissions when prompted by VS Code (first use only)
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

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 (8): Query requirements (get_control, list_family_controls, search_requirements), definitions (get_definition, list_definitions, search_definitions), and KSIs (get_ksi, list_ksi) KSI Tools (9): KSI implementation status, evidence automation, evidence queries, evidence artifacts, implementation matrix, implementation summary, coverage summary, coverage status, family status FRR Analysis Tools (7): Analyze code against specific FRRs, all FRRs, or FRR families; list FRRs; get FRR metadata, evidence automation, and implementation status Documentation Tools (3): Search and retrieve FedRAMP documentation Enhancement Tools (6): Implementation examples, dependencies, effort estimation, cloud-native guidance, architecture validation, Rev 4 comparison Export Tools (2): Excel/CSV export Planning Tools (2): Generate implementation questions and step-by-step checklists Evidence Collection Tools (4): Infrastructure code templates, collection code, architecture guidance, KSI specifications Code Analysis Tools (4): AST-powered analysis of infrastructure code, application code, CI/CD pipelines, and FedRAMP config validation Security Tools (2): CVE vulnerability checking for packages and dependency files Code Enrichment Tools (1): Add FedRAMP requirement comments to code

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 evidence automation recommendations for a specific KSI. 65 active KSIs include automated evidence collection guidance.

Parameters:

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

Returns: 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: All 65 active KSIs across 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 compliance analysis.

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: Analysis results grouped by FRR family with summary statistics

Use Cases:

Pre-deployment validation: Check all code before production deployment
Compliance audits: Analysis scan to support 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: FRR metadata including:

FRR Details: ID, name, family, requirement statement
NIST 800-53 Controls: Related security controls
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: 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 patterns available
- RSC Family: 10/10 patterns available
- ADS Family: 22/22 patterns available
- Total: 199/199 patterns available

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 markdown files from the docs directory for searchability.

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:

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

All Requirements Export Columns:

Requirement ID - Unique identifier
Family - Control family
Term/Name - Requirement name
Description - Full description
Document - Source document

Definitions Export Columns:

Term - FedRAMP term
Definition - Term definition
Notes - Additional context
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 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: Strategic questions organized by stakeholder role

Question Categories:

Strategic Questions for Product Managers (10 questions):
- Business Impact & ROI
- Customer Value & Competitive Position
- Resource Allocation & Prioritization
- Dependencies & Phasing
Technical Questions for Engineers (15 questions):
- Architecture & Design Decisions
- Azure Service Selection
- Automation Opportunities
- Monitoring & Evidence Collection
- Operations & Maintenance
Cross-Functional Questions (10 questions):
- Security & Compliance Integration
- User Experience Impact
- Training & Support Needs
- Incident Response Alignment
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:

Code submitted via PR (GitHub/Azure DevOps)
Analyzer tools examine IaC and application code
Findings generated with FedRAMP requirement citations
PR comment posted with compliance review
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: 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: 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: 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 architecture guidance for automated evidence collection systems.

Parameters:

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

Returns: Architecture patterns with components, data flows, and implementation guidance

Architecture Scopes:

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
single-ksi: Production architecture for one KSI
- Dedicated evidence collection infrastructure
- Azure Functions with monitoring
- Managed identities for security
- Sentinel integration
category: Enterprise architecture for one KSI category (IAM, MLA, etc.)
- Category-specific evidence collectors
- Centralized evidence storage
- Automated reporting dashboards
- Integration with Azure Policy
all: Enterprise architecture for 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:

Planning & Assessment Prompts

initial_assessment_roadmap - 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 - 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 - Guide for implementing 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 - 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 - Checklist for FedRAMP 20x quarterly reviews (FRR-CCM-QR) covering 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 - Guide for FedRAMP 20x assessment preparation with evidence gathering, common findings, and interview prep (teams determine preparation timeline)

ato_package_checklist - Checklist for Authority to Operate (ATO) package preparation including 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 - Family-specific assessment guide for 199 FRR requirements with detailed checklists, assessment questions, and evidence planning for 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 compliance analysis. 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 supports 22 authorization data sharing requirements
Combined analysis identifies which KSIs map to which FRR endpoints
Supports machine-readable compliance data requirements

Example 4: Pre-Deployment Scan

Scenario: Compliance analysis before production deployment combining 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_results['compliant_count'],
    'non_compliant': all_frr_results['non_compliant_count'],
    'not_applicable': all_frr_results['not_applicable_count']
}
print(f"  Total FRRs analyzed: {all_frr_results['total_frrs']}")
print(f"  Compliant: {frr_summary['compliant']}")
print(f"  Non-compliant: {frr_summary['non_compliant']}")
print(f"  Not applicable: {frr_summary['not_applicable']}")

# Step 3: Family-specific deep dive on critical families
critical_families = ['VDR', 'ADS', 'CCM', 'RSC', 'UCM']
print("\nCritical Family Analysis:")
for family in critical_families:
    family_result = analyze_frr_family(family, terraform_infra, "terraform", "main.tf")
    print(f"  FRR-{family}: {family_result['compliant_count']}/{family_result['total_frrs']} compliant")

# Step 4: Generate deployment checklist
print("\nPre-Deployment Checklist:")
print("  [" + ("✓" if ksi_summary['high_severity'] == 0 else "✗") + "] No high-severity KSI findings")
print("  [" + ("✓" if frr_summary['non_compliant'] == 0 else "✗") + "] All applicable FRRs compliant")
print("  [" + ("✓" if frr_summary['non_compliant'] < 10 else "✗") + "] Less than 10 FRR findings")
print("  [" + ("✓" if ksi_summary['compliant'] >= 65 else "✗") + "] At least 65/72 KSIs compliant")

# Step 5: Get implementation status for tracking
ksi_status = get_ksi_implementation_status()
frr_status = get_frr_implementation_status()

print("\nCompliance Tracking:")
print(f"  KSI Implementation: {ksi_status['implementation_percentage']}%")
print(f"  FRR Implementation: {frr_status['overall_compliance_percentage']}%")
print(f"  Combined Score: {(ksi_status['implementation_percentage'] + frr_status['overall_compliance_percentage']) / 2}%")

# Output:
# KSI Analysis (72 indicators):
#   Total KSIs analyzed: 72
#   Compliant: 58
#   Non-compliant: 14
#   High-severity findings: 5
#
# FRR Analysis (199 requirements):
#   Total FRRs analyzed: 199
#   Compliant: 167
#   Non-compliant: 18
#   Not applicable: 14
#
# Critical Family Analysis:
#   FRR-VDR: 45/59 compliant
#   FRR-ADS: 22/22 compliant
#   FRR-CCM: 18/25 compliant
#   FRR-RSC: 8/10 compliant
#   FRR-UCM: 4/4 compliant
#
# Pre-Deployment Checklist:
#   [✗] No high-severity KSI findings
#   [✗] All applicable FRRs compliant
#   [✓] Less than 10 FRR findings
#   [✓] At least 65/72 KSIs compliant
#
# Compliance Tracking:
#   KSI Implementation: 80.6%
#   FRR Implementation: 90.3%
#   Combined Score: 85.4%

Integration Benefits:

Analysis across 72 KSIs and 199 FRRs
Single command provides compliance analysis results
Family-specific analysis highlights priority areas
Automated checklist for deployment approval
Tracking metrics for continuous improvement

Recommended Workflow

Development Phase: Use KSI analyzers for tactical code reviews
- factory.analyze(ksi_id, code, language, file_path) for specific security checks
- analyze_frr_code(frr_id, code, language, file_path) for requirement validation
Pre-Commit Phase: Analyze against critical FRR families
- analyze_frr_family("VDR", code, language, file_path) for vulnerability management
- analyze_frr_family("RSC", code, language, file_path) for secure configuration
CI/CD Pipeline: Compliance analysis before deployment
- factory.analyze_all_ksis(code, language, file_path) for all KSIs
- analyze_all_frrs(code, language, file_path) for all FRRs
Quarterly Review: Compliance tracking and reporting
- get_ksi_implementation_status() for KSI progress
- get_frr_implementation_status() for FRR compliance
- Generate combined compliance report for stakeholders

Data Source

Data is fetched from the official FedRAMP repository: https://github.com/FedRAMP/docs/tree/main/data

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for:

Development setup and testing
Security scanning requirements
Dependency management guidelines
Pull request process
Project structure and architecture
Complete test documentation

Security

For security vulnerability reporting and security best practices, see SECURITY.md.

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for:

Development setup and testing
Security scanning requirements
Dependency management guidelines
Pull request process

License

MIT License - See LICENSE file for details.

This project is open source and contributions are welcome! See CONTRIBUTING.md for guidelines.

The FedRAMP data is provided by the U.S. General Services Administration as public domain content.

FedRAMP 20x Requirements

Quick Install

FedRAMP 20x MCP Server

Overview

Data Coverage

Features

Pattern-Based Analysis Architecture

Installation

Prerequisites

Setup

Security

Usage

With VS Code and GitHub Copilot

With Claude Desktop

With MCP Inspector

⚠️ Copilot Execution Policy

Advanced Configuration

Available Tools

get_control

list_family_controls

search_requirements

get_definition

list_definitions

search_definitions

get_ksi

list_ksi

get_ksi_evidence_automation

get_ksi_evidence_queries

get_ksi_evidence_artifacts

analyze_frr_code

analyze_all_frrs

analyze_frr_family

list_frrs_by_family

get_frr_metadata

get_frr_evidence_automation

get_frr_implementation_status

compare_with_rev4

get_implementation_examples

check_requirement_dependencies

estimate_implementation_effort

get_cloud_native_guidance

validate_architecture

search_documentation

get_documentation_file

list_documentation_files

export_to_excel

export_to_csv

generate_ksi_specification

generate_implementation_questions

analyze_infrastructure_code

analyze_application_code

analyze_cicd_pipeline

get_infrastructure_code_for_ksi

get_evidence_collection_code

get_evidence_automation_architecture

Available Prompts

Planning & Assessment Prompts

Implementation & Automation Prompts

Monitoring & Compliance Prompts

Audit & Documentation Prompts

FRR Compliance & Code Review Prompts

Combined KSI + FRR Analysis Examples

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

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

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

Example 4: Pre-Deployment Scan

Recommended Workflow

Data Source

Contributing

Security

Contributing

License

References

Reviews