AI/ML Security Framework
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flowchart TB
subgraph "AI/ML Security Framework"
direction TB
subgraph "Data Security Layer"
D1["<i class="fas fa-database"></i><br>Data Collection<br>Security<br><small>NIST 800-53 SC-8</small>"] --> D2["<i class="fas fa-lock"></i><br>Data Storage<br>Encryption<br><small>FIPS 140-2</small>"]
D2 --> D3["<i class="fas fa-user-shield"></i><br>Data Access<br>Controls<br><small>NIST 800-53 AC-3</small>"]
D3 --> D4["<i class="fas fa-check-double"></i><br>Data Integrity<br>Verification<br><small>NIST 800-53 SI-7</small>"]
end
subgraph "Model Security Layer"
M1["<i class="fas fa-code"></i><br>Model Development<br>Security<br><small>NIST AI 100-1</small>"] --> M2["<i class="fas fa-shield-alt"></i><br>Model Training<br>Protection<br><small>NIST SP 1270</small>"]
M2 --> M3["<i class="fas fa-tasks"></i><br>Model Validation<br>Controls<br><small>NIST AI RMF</small>"]
M3 --> M4["<i class="fas fa-rocket"></i><br>Model Deployment<br>Security<br><small>FedRAMP</small>"]
end
subgraph "Infrastructure Security Layer"
I1["<i class="fas fa-server"></i><br>Compute Environment<br>Security<br><small>NIST 800-53 SC-7</small>"] --> I2["<i class="fas fa-network-wired"></i><br>Network Security<br>Controls<br><small>NIST 800-53 SC-7</small>"]
I2 --> I3["<i class="fas fa-plug"></i><br>API Security<br><small>NIST 800-95</small>"]
I3 --> I4["<i class="fas fa-box"></i><br>Container Security<br><small>NIST 800-190</small>"]
end
subgraph "Governance Layer"
G1["<i class="fas fa-file-contract"></i><br>Security Policies<br><small>NIST 800-53 PL-1</small>"] --> G2["<i class="fas fa-clipboard-check"></i><br>Compliance<br>Monitoring<br><small>FISMA</small>"]
G2 --> G3["<i class="fas fa-list-check"></i><br>Audit Logging<br><small>NIST 800-53 AU-2</small>"]
G3 --> G4["<i class="fas fa-exclamation-triangle"></i><br>Incident Response<br><small>NIST 800-61</small>"]
end
D4 --> M1
M4 --> I1
I4 --> G1
subgraph "Threat Protection"
T1["<i class="fas fa-user-secret"></i><br>Adversarial Attack<br>Detection<br><small>Example: Evasion Attacks</small>"]
T2["<i class="fas fa-virus"></i><br>Model Poisoning<br>Prevention<br><small>Example: Backdoor Attacks</small>"]
T3["<i class="fas fa-eye-slash"></i><br>Inference Attack<br>Protection<br><small>Example: Membership Inference</small>"]
T4["<i class="fas fa-mask"></i><br>Privacy<br>Preservation<br><small>Example: Differential Privacy</small>"]
end
subgraph "Continuous Monitoring"
C1["<i class="fas fa-chart-line"></i><br>Performance<br>Monitoring<br><small>KPI: Model Accuracy</small>"] --> C2["<i class="fas fa-shield-alt"></i><br>Security<br>Monitoring<br><small>KPI: Threat Detection Rate</small>"]
C2 --> C3["<i class="fas fa-chart-bar"></i><br>Drift<br>Detection<br><small>KPI: Distribution Shift %</small>"]
C3 --> C4["<i class="fas fa-search"></i><br>Anomaly<br>Detection<br><small>KPI: False Positive Rate</small>"]
end
%% Enhanced connections between layers
D1 -.-.> T1
D2 -.-.> T4
M2 -.-.> T2
M3 -.-.> T3
%% Clearer connections between Threat Protection and Continuous Monitoring
T1 --> C2
T2 --> C2
T3 --> C2
T4 --> C2
G4 --> C1
C4 --> D1
%% Additional connections showing the cyclical nature
C1 -.-.> M4
C3 -.-.> M3
end
classDef data fill:#4e4376,stroke:#2b5876,color:#fff,stroke-width:2px;
classDef model fill:#2b5876,stroke:#4e4376,color:#fff,stroke-width:2px;
classDef infra fill:#3a6073,stroke:#16222a,color:#fff,stroke-width:2px;
classDef gov fill:#4CAF50,stroke:#8BC34A,color:#fff,stroke-width:2px;
classDef threat fill:#ff5f57,stroke:#ff9f43,color:#fff,stroke-width:2px;
classDef monitor fill:#16222a,stroke:#3a6073,color:#fff,stroke-width:2px;
class D1,D2,D3,D4 data;
class M1,M2,M3,M4 model;
class I1,I2,I3,I4 infra;
class G1,G2,G3,G4 gov;
class T1,T2,T3,T4 threat;
class C1,C2,C3,C4 monitor;
Legend
Infrastructure Security Layer
Our comprehensive AI/ML security framework ensures that artificial intelligence and machine learning implementations
meet federal, state/local, and commercial security standards while delivering optimal performance. The framework addresses security at every layer
of the AI/ML lifecycle, from data collection to model deployment, with continuous monitoring and threat protection across all sectors.
Multi-Sector AI/ML Security Standards
Federal Standards
NIST AI Risk Management Framework
NIST SP 800-53 Rev. 5
FIPS 140-2/3
FedRAMP
State & Local Standards
NASCIO Cybersecurity Framework
State-Specific AI Guidelines
Municipal Data Protection Standards
Smart City Security Requirements
Commercial Standards
ISO/IEC 27001:2022
SOC 2 Type II
PCI DSS for AI/ML Systems
Industry-Specific AI Frameworks
Cross-Sector Standards
GDPR AI Requirements
IEEE AI Ethics Guidelines
CIS Controls for AI/ML
Zero Trust for AI Systems
Security Effectiveness Metrics
Model Robustness Score
Measures resistance to adversarial examples (higher is better)
Target: >85%
Data Privacy Index
Quantifies the level of privacy preservation in training and inference
Target: >90%
Security Control Coverage
Percentage of applicable NIST controls implemented
Target: 100% of High Impact controls
Mean Time to Detect (MTTD)
Average time to detect security incidents
Target: <24 hours