AI Security for DevOps Beginners
Equip yourself with the essential skills to secure AI-powered DevOps environments, from threat modeling to incident response.
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Understanding the AI Security Landscape in DevOps
Unit 1: AI Security Fundamentals
Intro to AI Security
AI Threat Landscape
AI Vulnerabilities
Attack Vectors in AI
DevOps Security Basics
Unit 2: AI Security Challenges in DevOps
AI in CI/CD Pipelines
Deployment Workflow Risks
Data Governance Issues
Model Versioning Problems
Access Control Complexities
Unit 3: AI Security Frameworks and Standards
OWASP ML Top 10 Intro
Data Poisoning (OWASP)
Model Evasion (OWASP)
NIST AI Risk Mgmt
AI RMF Core Functions
Secure Coding Practices for AI-Integrated Applications
Unit 1: Preventing Prompt Injection Attacks
Understanding Injection
Input Validation
Sandboxing AI Models
Prompt Engineering
Monitoring & Logging
Unit 2: Mitigating Data Poisoning Attacks
Data Poisoning Defined
Data Validation
Data Sanitization
Robust Training
Monitoring Data Quality
Unit 3: Defending Against Model Evasion Attacks
Evasion Attacks Defined
Adversarial Training
Input Transformation
Defensive Distillation
Ensemble Methods
Unit 4: Differential Privacy Techniques
Privacy Basics
Adding Noise
Privacy-Preserving ML
Data Aggregation
Privacy Auditing
Integrating AI Security into the CI/CD Pipeline
Unit 1: Automated Vulnerability Scanning for AI Models
Intro to Vuln Scanning
Tools for AI Vuln Scan
Configuring the Scanner
Analyzing Scan Results
Remediation Strategies
Unit 2: Anomaly Detection for AI Processes
Intro to Anomaly Detect
Anomaly Detection Methods
Setting Up Anomaly Detect
Responding to Anomalies
Improving Anomaly Detect
Unit 3: Automated Security Testing for AI Apps
Intro to Security Tests
Types of Security Tests
Setting Up Security Tests
Analyzing Test Results
Security Test Frameworks
Securing AI Model Deployment and Management
Unit 1: Access Control and Versioning for AI Models
Model Access Control 101
Versioning Strategies
Secure Model Storage
Model Provenance Tracking
Auditing Access Logs
Unit 2: Monitoring and Logging for Adversarial Attacks
Intro to Model Monitoring
Detecting Data Drift
Adversarial Input Detection
Output Anomaly Detection
Logging Security Events
Unit 3: Secure Model Updates and Rollbacks
Secure Update Strategies
Staged Rollouts
Automated Rollbacks
Blue/Green Deployments
Testing Before Deployment
Unit 4: Robust Logging and Auditing
Centralized Logging
Audit Trail Best Practices
Log Analysis Techniques
Compliance Standards
Alerting and Reporting
Incident Response and Security Frameworks for AI in DevOps
Unit 1: AI Security Incident Response Planning
Intro to IR for AI
Containment Strategies
Remediation Techniques
Recovery Procedures
Post-Incident Analysis
Unit 2: AI Security Frameworks and Standards
NIST AI RMF Overview
OWASP ML Top 10
ISO/IEC 27001 for AI
CSA Guidance for AI
Choosing a Framework
Unit 3: AI Security Forensics
Intro to AI Forensics
Data Collection
Log Analysis
Model Reverse Engineering
Reporting Findings