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 安全 授權 for 雲端 services used by federal agencies. As government agencies deploy AI systems in 雲端 environments — and as 雲端 service providers embed AI capabilities into their FedRAMP-authorized offerings — the intersection of FedRAMP and AI 安全 has become a critical compliance and 安全 challenge.
FedRAMP was designed for traditional 雲端 computing. Its control baselines address network 安全, access management, data protection, and incident response for deterministic systems. AI introduces non-deterministic behavior, model integrity as an asset class, and attack surfaces (提示詞注入, 資料投毒, 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 雲端 components regardless of whether the workload is AI or traditional software.
| Control Family | Application to AI | Notes |
|---|---|---|
| AC (Access Control) | Model API access, 訓練資料 access, 推論 endpoint 認證 | Standard application |
| AU (Audit) | Prompt logging, 推論 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, 推論 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 偵測, patch management, and 輸入 validation. For AI systems, SI must be extended to cover:
# Extended SI controls for AI systems
SI-AI-1: Model Integrity 監控
description: >
Continuously monitor AI model behavior for drift,
degradation, or manipulation. Establish behavioral
baselines and alert on statistically significant
deviations.
assessment_methods:
- Periodic 評估 against benchmark datasets
- Statistical process control on 輸出 distributions
- 對抗性 probe 測試 on a scheduled basis
evidence:
- Model 評估 reports (monthly)
- Drift 偵測 dashboards
- 對抗性 測試 results
SI-AI-2: Training Data Integrity
description: >
Verify the integrity and provenance of all data
used to train, 微調, or calibrate AI models.
Detect and prevent 資料投毒.
assessment_methods:
- Hash verification of 訓練 datasets
- Provenance tracking for all data sources
- Statistical analysis for 投毒 indicators
evidence:
- Data provenance documentation
- Integrity verification logs
- Poisoning 偵測 scan results
SI-AI-3: 提示詞注入 Protection
description: >
實作 controls to detect and prevent prompt
injection attacks against language model components.
assessment_methods:
- 輸入 filtering effectiveness 測試
- Red team 提示詞注入 評估
- 輸出 監控 for injection indicators
evidence:
- 輸入 filter configuration and 測試 results
- Red team 評估 reports
- 輸出 監控 alert logsRA (Risk 評估):
Traditional risk 評估 identifies assets, threats, and 漏洞. AI introduces new asset types (models, 訓練資料, 嵌入向量) and new threat categories (對抗性 inputs, 資料投毒, model extraction) that must be incorporated:
# AI-specific risk 評估 additions for FedRAMP
ai_risk_assessment_extensions = {
"new_asset_types": [
{
"asset": "AI Model",
"classification": "Based on 訓練資料 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": "嵌入向量 資料庫",
"classification": "Derived from source data classification",
"integrity_requirements": "High — manipulated 嵌入向量 "
"alter retrieval results",
},
],
"new_threat_categories": [
"Prompt injection and 越獄",
"Training 資料投毒",
"Model extraction and theft",
"對抗性 輸入 attacks",
"Model behavior manipulation",
"嵌入向量 inversion and data extraction",
],
}Controls That Do Not Exist Yet
Several AI-specific 安全 concerns have no corresponding FedRAMP control:
-
Model 供應鏈 安全. FedRAMP has 供應鏈 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, 訓練資料 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 授權 boundary — a precise description of what components are included in 系統's 安全 授權. 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 授權 boundary? The answer affects who is responsible for 模型's 安全 properties.
訓練資料 provenance. 訓練資料 may come from sources outside the 授權 boundary. If 模型 was trained on internet data, the entire internet is not inside the boundary — but 模型's behavior is influenced by that data.
Third-party model APIs. Many government AI systems call external model APIs. These API calls cross the 授權 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 訓練資料 (outside boundary)"
- "Pre-trained weights (sourced from model provider)"
- component: "Model 推論 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: "訓練資料 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 監控 for AI
Beyond Traditional ConMon
FedRAMP's Continuous 監控 (ConMon) program requires monthly 漏洞 scanning, annual penetration 測試, and ongoing 評估 of control effectiveness. AI systems require additional continuous 監控 activities:
Model behavior 監控:
# Continuous 監控 checks for AI in FedRAMP environments
class AIConMonChecks:
def monthly_checks(self):
return [
{
"check": "Model 輸出 distribution analysis",
"method": "Compare current month's 輸出 distribution "
"to established baseline",
"threshold": "KL divergence > 0.1 triggers investigation",
"reporting": "Include in monthly ConMon report",
},
{
"check": "對抗性 probe 測試",
"method": "Run standardized 對抗性 probe suite "
"against production model endpoints",
"threshold": "Any new successful probe is a finding",
"reporting": "Report as POA&M item if new 漏洞",
},
{
"check": "訓練資料 integrity verification",
"method": "Verify hashes of 訓練資料 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 紅隊 評估",
"method": "Comprehensive 對抗性 測試 including "
"提示詞注入, data extraction, model "
"manipulation, and bias 測試",
"scope": "All AI components within 授權 boundary",
"reporting": "Include in annual 評估 report",
},
{
"check": "Model 供應鏈 review",
"method": "Review all model components, pre-trained "
"weights, and third-party model dependencies "
"for known 漏洞",
"reporting": "Document in SSP update",
},
]Incident Response for AI in FedRAMP
When an AI-specific 安全 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.
紅隊 評估 Methodology
FedRAMP-Aligned AI 測試
A FedRAMP-aligned AI 紅隊 評估 should produce findings that map to FedRAMP control families and can be incorporated into 系統's Plan of Action and Milestones (POA&M).
Control mapping
Before 測試, map each planned 測試 to the FedRAMP control it evaluates. This ensures findings are immediately actionable within the FedRAMP compliance process.
Baseline 評估
評估 all standard FedRAMP controls as they apply to AI infrastructure. Do not skip traditional controls in favor of AI-specific 測試.
AI-specific 測試
Conduct AI-specific 測試 including 提示詞注入, data extraction, model manipulation, bias 評估, and 對抗性 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 系統's compliance documentation.
Further Reading
- Government AI 安全 概覽 — Broader government AI context
- Public Services AI 攻擊 — Citizen-facing AI 漏洞
- AI Incident Classification — How to classify AI 安全 incidents