Security Models for AI Systems

Formal Security Models for AI

Understand and implement mathematical models that enforce security constraints in AI development pipelines

Model Concepts

View Security Models →

Implementation

Apply Models →

Core Security Models

Bell-LaPadula

Enforces strict secrecy policies with multi-level access control

Confidentiality Model

Prevents unauthorized information flow between security levels

Biba

Ensures integrity through compartmentalized data protection

Integrity Model

Prevents unauthorized modifications across integrity domains

Clark-Wilson

Enforces controlled interfaces for data transaction validation

Transaction Integrity

Validates data transformations through certified agents

Model Application

Step 1: Define Threat Model

Identify attack vectors, security domains, and data flow boundaries

python threat_modeler.py --attack-surface network --threats 0475

Step 2: Map to Model

Select appropriate security framework based on confidentiality, integrity, and availability requirements

Selected: Biba Model [Integrity] + Non-Interference Constraints

Security Model Integration

System Architecture Diagram

┌────────────┐   ┌────────────┐   ┌────────────┐
│  Identity  │ → │  Access    │ → │  Data      │
│    Layer   │   │ Controller │   │ Processing │
└────────────┘   └────────────┘   └────────────┘

Zero Trust

Verify every access request within the AI pipeline regardless of provenance

OAuth2 JWT

Privacy by Design

Embed data anonymization and encryption requirements in system architecture

K-Anonymity GDPR

Model Validation Tools

SecML

Machine learning security library for adversarial robustness validation


                        pip install secml

Foolbox

Adversarial attack and defense testing framework for ML models


                        python -m foolbox

Trusted AI

Microsoft's AI security toolkit for bias and fairness validation


                        pip install trustedai