AI/ML Security Framework
Zoom In
Zoom Out
Reset
Toggle Details
%%{init: {'theme': 'neutral', 'themeVariables': { 'primaryColor':
'#2b5876', 'primaryTextColor': '#fff', 'primaryBorderColor': '#4e4376',
'lineColor': '#2b5876', 'secondaryColor': '#4CAF50', 'tertiaryColor':
'#f8f9fa'}}}%% 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