AI Security & Intelligent Guardrails

AI Threat Landscape

Adversarial Attacks

Evasion Attacks

• Adversarial Examples: Carefully crafted inputs that fool ML models
• Gradient-Based Attacks: FGSM, PGD, and C&W attack methods
• Query-Based Attacks: Black-box attacks using model queries
• Physical Adversarial: Real-world adversarial patches and objects

Poisoning Attacks

• Training Data Poisoning: Malicious samples in training datasets
• Model Poisoning: Direct manipulation of model parameters
• Backdoor Attacks: Hidden triggers in AI models
• Federated Learning Poisoning: Malicious client participation

Model Extraction & Inversion

• Model Stealing: Recreating proprietary models through queries
• Membership Inference: Determining if data was used in training
• Property Inference: Extracting sensitive dataset properties
• Model Inversion: Reconstructing training data from models

AI Security Controls

Defensive Techniques

Adversarial Training

# Example: Adversarial training implementation
import torch
import torch.nn.functional as F

def adversarial_training(model, data_loader, optimizer, epsilon=0.3):
    model.train()
    for batch_idx, (data, target) in enumerate(data_loader):
        # Generate adversarial examples
        data.requires_grad = True
        output = model(data)
        loss = F.cross_entropy(output, target)
        model.zero_grad()
        loss.backward()
        
        # Create adversarial perturbation
        data_grad = data.grad.data
        perturbed_data = data + epsilon * data_grad.sign()
        
        # Train on both clean and adversarial examples
        combined_data = torch.cat([data, perturbed_data])
        combined_target = torch.cat([target, target])
        
        optimizer.zero_grad()
        output = model(combined_data)
        loss = F.cross_entropy(output, combined_target)
        loss.backward()
        optimizer.step()

Input Sanitization

• Preprocessing Defenses: Input transformation and filtering
• Feature Squeezing: Reducing input complexity and precision
• Detection Systems: Adversarial example detection
• Certified Defenses: Provable robustness guarantees

Model Hardening

Differential Privacy

• DP-SGD: Differential private stochastic gradient descent
• Privacy Budget: Epsilon and delta parameter management
• Noise Addition: Calibrated noise for privacy preservation
• Composition Theorems: Privacy loss accounting

Federated Learning Security

• Secure Aggregation: Cryptographic aggregation protocols
• Byzantine Robustness: Defense against malicious clients
• Client Authentication: Identity verification and access control
• Audit Mechanisms: Contribution tracking and validation

AI Guardrails Architecture

Safety Framework

Input Validation Layer

input_guardrails:
  content_filters:
    - profanity_filter: enabled
    - hate_speech_detection: enabled
    - personal_info_scrubbing: enabled
    - malicious_prompt_detection: enabled
  
  rate_limiting:
    requests_per_minute: 100
    concurrent_sessions: 10
    burst_allowance: 20
  
  validation_rules:
    - max_input_length: 4096
    - allowed_file_types: ["txt", "json"]
    - prohibited_patterns: ["(?i)ignore.*instructions"]

Output Monitoring

• Content Classification: Automated output categorization
• Toxicity Detection: Harmful content identification
• Fact Checking: Automated factual accuracy validation
• Bias Measurement: Fairness metric calculation

Behavioral Boundaries

• Usage Pattern Analysis: Abnormal usage detection
• Capability Restrictions: Function and access limitations
• Context Awareness: Situational appropriateness checking
• Escalation Triggers: Human oversight activation

Monitoring & Alerting

Real-time Monitoring

• Model Drift Detection: Performance degradation monitoring
• Input Distribution Shifts: Data drift identification
• Adversarial Attack Detection: Real-time threat identification
• Privacy Violation Alerts: Sensitive information exposure

Audit & Compliance

• Decision Logging: Comprehensive decision trail recording
• Explanation Generation: AI decision rationale capture
• Compliance Checking: Regulatory requirement validation
• Performance Metrics: Fairness and accuracy measurement

Bias Detection & Mitigation

Fairness Metrics

Individual Fairness

• Counterfactual Fairness: Similar individuals receive similar outcomes
• Individual Treatment: Person-specific fairness assessment
• Consistency: Similar cases handled similarly

Group Fairness

• Demographic Parity: Equal positive prediction rates
• Equal Opportunity: Equal true positive rates across groups
• Equalized Odds: Equal TPR and FPR across groups
• Calibration: Equal prediction accuracy across groups

Bias Mitigation Techniques

Pre-processing

• Data Augmentation: Balanced dataset creation
• Resampling: Under/over-sampling techniques
• Feature Selection: Bias-aware feature engineering
• Synthetic Data: Balanced synthetic data generation

In-processing

• Fairness Constraints: Optimization with fairness objectives
• Multi-task Learning: Joint optimization of accuracy and fairness
• Adversarial Debiasing: Adversarial training for fairness
• Regularization: Fairness-aware regularization terms

Post-processing

• Threshold Optimization: Group-specific decision thresholds
• Calibration: Post-hoc probability calibration
• Output Modification: Fairness-aware prediction adjustment

AI Privacy & Compliance

Privacy-Preserving Techniques

Homomorphic Encryption

• Fully Homomorphic Encryption: Computation on encrypted data
• Partially Homomorphic: Limited operations on encrypted data
• Application: Private inference and training

Secure Multi-Party Computation

• Secret Sharing: Distributed computation protocols
• Garbled Circuits: Privacy-preserving function evaluation
• Applications: Collaborative ML without data sharing

GDPR Compliance for AI

• Right to Explanation: Explainable AI implementations
• Data Minimization: Minimal data collection and processing
• Purpose Limitation: AI use within specified purposes
• Consent Management: Dynamic consent for AI processing