FedRAMP for AI Systems
Applying the Federal Risk and Authorization Management Program to AI systems: AI-specific security controls, continuous monitoring for model behavior, authorization boundary challenges, and compliance testing methodologies.
The Federal Risk and Authorization Management Program (FedRAMP) provides a standardized approach to security authorization for cloud services used by federal agencies. As government agencies deploy AI systems in cloud environments — and as cloud service providers embed AI capabilities into their FedRAMP-authorized offerings — the intersection of FedRAMP and AI security has become a critical compliance and security challenge.
FedRAMP was designed for traditional cloud computing. Its control baselines address network security, access management, data protection, and incident response for deterministic systems. AI introduces non-deterministic behavior, model integrity as an asset class, and attack surfaces (prompt injection, data poisoning, model extraction) that do not map cleanly to existing FedRAMP controls. This page covers how to bridge that gap.
FedRAMP Control Mapping for AI
Controls That Apply Directly
Many FedRAMP controls apply to AI systems without modification. The underlying infrastructure — compute, storage, networking, identity management — uses the same cloud components regardless of whether the workload is AI or traditional software.
| Control Family | Application to AI | Notes |
|---|---|---|
| AC (Access Control) | Model API access, training data access, inference endpoint authentication | Standard application |
| AU (Audit) | Prompt logging, inference logging, model version tracking | Extended for AI artifacts |
| CM (Configuration Management) | Model versioning, hyperparameter tracking, deployment configuration | New asset types |
| IA (Identification/Authentication) | API key management for model endpoints | Standard application |
| SC (System/Communications Protection) | Encryption for model artifacts, inference traffic | Standard application |
Controls That Need AI-Specific Extension
Several FedRAMP control families require extension to address AI-specific risks:
SI (System and Information Integrity):
Traditional SI controls focus on malware detection, patch management, and input validation. For AI systems, SI must be extended to cover:
# Extended SI controls for AI systems
SI-AI-1: Model Integrity Monitoring
description: >
Continuously monitor AI model behavior for drift,
degradation, or manipulation. Establish behavioral
baselines and alert on statistically significant
deviations.
assessment_methods:
- Periodic evaluation against benchmark datasets
- Statistical process control on output distributions
- Adversarial probe testing on a scheduled basis
evidence:
- Model evaluation reports (monthly)
- Drift detection dashboards
- Adversarial testing results
SI-AI-2: Training Data Integrity
description: >
Verify the integrity and provenance of all data
used to train, fine-tune, or calibrate AI models.
Detect and prevent data poisoning.
assessment_methods:
- Hash verification of training datasets
- Provenance tracking for all data sources
- Statistical analysis for poisoning indicators
evidence:
- Data provenance documentation
- Integrity verification logs
- Poisoning detection scan results
SI-AI-3: Prompt Injection Protection
description: >
Implement controls to detect and prevent prompt
injection attacks against language model components.
assessment_methods:
- Input filtering effectiveness testing
- Red team prompt injection assessment
- Output monitoring for injection indicators
evidence:
- Input filter configuration and test results
- Red team assessment reports
- Output monitoring alert logsRA (Risk Assessment):
Traditional risk assessment identifies assets, threats, and vulnerabilities. AI introduces new asset types (models, training data, embeddings) and new threat categories (adversarial inputs, data poisoning, model extraction) that must be incorporated:
# AI-specific risk assessment additions for FedRAMP
ai_risk_assessment_extensions = {
"new_asset_types": [
{
"asset": "AI Model",
"classification": "Based on training data classification",
"integrity_requirements": "High — model manipulation can "
"cause incorrect decisions",
"availability_requirements": "Depends on application criticality",
},
{
"asset": "Training Data",
"classification": "May contain PII, CUI, or classified data",
"integrity_requirements": "Critical — poisoned data produces "
"compromised models",
"availability_requirements": "Medium — needed for retraining",
},
{
"asset": "Embedding Database",
"classification": "Derived from source data classification",
"integrity_requirements": "High — manipulated embeddings "
"alter retrieval results",
},
],
"new_threat_categories": [
"Prompt injection and jailbreaking",
"Training data poisoning",
"Model extraction and theft",
"Adversarial input attacks",
"Model behavior manipulation",
"Embedding inversion and data extraction",
],
}Controls That Do Not Exist Yet
Several AI-specific security concerns have no corresponding FedRAMP control:
-
Model supply chain security. FedRAMP has supply chain controls (SR family) but these do not address the unique risks of foundation models, pre-trained weights, and model marketplaces.
-
AI-specific incident response. Incident response controls (IR family) do not address AI incidents such as jailbreaks, training data extraction, or model behavior manipulation.
-
Algorithmic fairness. FedRAMP has no controls related to bias, discrimination, or algorithmic fairness — yet these are critical requirements for government AI under EO 14110.
-
Model explainability. No FedRAMP controls require that AI decisions be explainable, even when the AI affects citizen rights or access to government services.
Authorization Boundary Challenges
Where Does the AI System End?
FedRAMP requires a clearly defined authorization boundary — a precise description of what components are included in the system's security authorization. AI systems challenge this boundary in several ways:
Foundation model inclusion. If a FedRAMP-authorized system uses a foundation model (e.g., GPT-4, Claude, Llama) as a component, is the foundation model inside the authorization boundary? The answer affects who is responsible for the model's security properties.
Training data provenance. Training data may come from sources outside the authorization boundary. If the model was trained on internet data, the entire internet is not inside the boundary — but the model's behavior is influenced by that data.
Third-party model APIs. Many government AI systems call external model APIs. These API calls cross the authorization boundary and must be treated as interconnections with other systems.
# Authorization boundary documentation for AI components
authorization_boundary:
ai_components:
- component: "Fine-tuned language model"
boundary_status: "Inside"
justification: "Model fine-tuned and hosted within authorized environment"
inherited_risks:
- "Foundation model training data (outside boundary)"
- "Pre-trained weights (sourced from model provider)"
- component: "Model inference API"
boundary_status: "Inside"
justification: "API endpoints hosted within authorized infrastructure"
- component: "Foundation model provider API"
boundary_status: "Outside — interconnection"
justification: "External API, documented as interconnection"
interconnection_agreement: "ISA-2024-AI-001"
- component: "Training data pipeline"
boundary_status: "Inside"
justification: "Data processing occurs within authorized environment"
data_sources:
- source: "Agency databases"
classification: "CUI"
boundary_status: "Inside"
- source: "Public datasets"
classification: "Unclassified"
boundary_status: "Sourced externally, stored internally"Continuous Monitoring for AI
Beyond Traditional ConMon
FedRAMP's Continuous Monitoring (ConMon) program requires monthly vulnerability scanning, annual penetration testing, and ongoing assessment of control effectiveness. AI systems require additional continuous monitoring activities:
Model behavior monitoring:
# Continuous monitoring checks for AI in FedRAMP environments
class AIConMonChecks:
def monthly_checks(self):
return [
{
"check": "Model output distribution analysis",
"method": "Compare current month's output distribution "
"to established baseline",
"threshold": "KL divergence > 0.1 triggers investigation",
"reporting": "Include in monthly ConMon report",
},
{
"check": "Adversarial probe testing",
"method": "Run standardized adversarial probe suite "
"against production model endpoints",
"threshold": "Any new successful probe is a finding",
"reporting": "Report as POA&M item if new vulnerability",
},
{
"check": "Training data integrity verification",
"method": "Verify hashes of training data have not changed",
"threshold": "Any unauthorized change is a critical finding",
"reporting": "Immediate notification to AO if changed",
},
]
def annual_checks(self):
return [
{
"check": "Full AI red team assessment",
"method": "Comprehensive adversarial testing including "
"prompt injection, data extraction, model "
"manipulation, and bias testing",
"scope": "All AI components within authorization boundary",
"reporting": "Include in annual assessment report",
},
{
"check": "Model supply chain review",
"method": "Review all model components, pre-trained "
"weights, and third-party model dependencies "
"for known vulnerabilities",
"reporting": "Document in SSP update",
},
]Incident Response for AI in FedRAMP
When an AI-specific security incident occurs in a FedRAMP-authorized system, the incident must be reported through both FedRAMP's incident reporting process and any agency-specific AI incident reporting requirements.
Red Team Assessment Methodology
FedRAMP-Aligned AI Testing
A FedRAMP-aligned AI red team assessment should produce findings that map to FedRAMP control families and can be incorporated into the system's Plan of Action and Milestones (POA&M).
Control mapping
Before testing, map each planned test to the FedRAMP control it evaluates. This ensures findings are immediately actionable within the FedRAMP compliance process.
Baseline assessment
Evaluate all standard FedRAMP controls as they apply to AI infrastructure. Do not skip traditional controls in favor of AI-specific testing.
AI-specific testing
Conduct AI-specific testing including prompt injection, data extraction, model manipulation, bias assessment, and adversarial robustness. Map findings to extended SI, RA, and IR controls.
POA&M integration
Format findings as POA&M items with risk ratings, remediation plans, milestones, and responsible parties. This format allows direct integration into the system's compliance documentation.
Further Reading
- Government AI Security Overview — Broader government AI context
- Public Services AI Attacks — Citizen-facing AI vulnerabilities
- AI Incident Classification — How to classify AI security incidents