1.1 Adversarial Machine Learning Attacks

• Evasion attacks (inference-time manipulation)

• Poisoning attacks (training-time data corruption)

• Backdoor attacks (hidden triggers)

• Model extraction attacks (stealing model functionality)

• Model inversion attacks (reconstructing training data)

• Transferability of attacks across models

1.2 AI System Vulnerabilities

• Data vulnerabilities (bias, poisoning, sensitive data)

• Model vulnerabilities (overfitting, brittle boundaries)

• Infrastructure vulnerabilities (unpatched ML frameworks)

• Operational vulnerabilities (poor monitoring, weak controls)

• API and endpoint vulnerabilities

• Supply chain vulnerabilities

1.3 Threat Actors & Motivations

• Competitors (model/data theft for advantage)

• Cybercriminals (ransom, fraud, resale)

• Nation-states (espionage, strategic advantage)

• Insider threats (data/model theft, sabotage)

• Researchers (demonstrating vulnerabilities)

• Threat actor capabilities and tactics

1.4 Attack Surface Analysis

• Data collection and preparation attack vectors

• Model training attack vectors

• Model storage and distribution vectors

• Inference and serving attack vectors

• Monitoring and operations blind spots

• Supply chain and dependency risks

2.1 Secure Data Collection & Preparation

• Data source vetting and validation

• Data quality validation and anomaly detection

• Data sanitization and PII removal

• Synthetic data generation for testing

• Secure data storage and access controls

• Audit logging of data access

2.2 Secure Training Infrastructure

• Training environment isolation

• Secrets management (API keys, credentials)

• Training data encryption and protection

• Model validation and backdoor prevention

• Supply chain security (libraries, dependencies)

• Reproducibility for audit trails

2.3 Secure Model Deployment

• Model hardening and compression

• Input/output validation and sanitization

• Model signing for integrity verification

• Encrypted model storage

• Version control and rollback

• Access controls and audit logging

2.4 Monitoring & Observability

• Model behavior monitoring (prediction distribution changes)

• Data flow monitoring (query patterns)

• Infrastructure monitoring (resource usage, network)

• Security event alerting

• Integration with SIEM systems

• Incident detection and playbooks

3.1 Model Security & Intellectual Property

• Model confidentiality and encryption

• Model integrity and cryptographic signing

• Model authenticity and source verification

• Watermarking for ownership proof

• IP protection strategies

• Audit logging of model access

3.2 Training Data Privacy

• Data minimization principles

• Differential privacy techniques

• Federated learning approaches

• Data anonymization and de-identification

• Privacy-preserving aggregation

• Secure multi-party computation

3.3 Inference Data Protection

• Input data encryption and validation

• Output data obfuscation for privacy

• PII protection in predictions

• Prediction logging security

• User consent and transparency

• GDPR "right to be forgotten" implementation

3.4 Detection of Data & Model Attacks

• Poisoning detection (statistical anomalies)

• Extraction attack detection (query patterns)

• Model inversion detection

• Data leakage indicators

• Countermeasures (perturbation, limiting)

• Automated response procedures

4.1 Anomaly Detection for AI Systems

• Statistical anomaly detection

• Behavioral anomaly detection

• Time-series anomaly detection

• Machine learning-based detection

• Baseline establishment and drift

• Anomaly scoring and alerting

4.2 Threat Intelligence for AI

• AI threat landscape and emerging attacks

• Vulnerability intelligence (ML frameworks)

• Threat actor tracking and attribution

• Campaign tracking and analysis

• Exploit availability assessment

• Intelligence-driven defense

4.3 Forensic Analysis & Investigation

• Evidence collection and preservation

• Timeline reconstruction

• Artifact analysis (models, code, data)

• Impact assessment and quantification

• Root cause analysis

• Breach scope determination

4.4 Incident Response for AI Systems

• Incident classification and severity

• Containment strategies

• Model and data recovery procedures

• System restoration and verification

• Post-incident lessons learned

• External notification requirements

5.1 AI Regulatory Landscape

• EU AI Act (risk tiers, high-risk requirements)

• GDPR and data protection requirements

• CCPA and regional privacy laws

• Sectoral regulations (HIPAA, GLBA, FCA)

• Export control and sanctions

• AI-specific regulatory requirements

5.2 AI Governance & Policies

• AI governance framework structure

• AI use case approval process

• High-risk use case requirements

• Data governance policies

• Model management standards

• Security and compliance requirements

5.3 Audit & Compliance Verification

• Risk-based audit approach

• Control testing and evidence collection

• Documentation review (model cards, system docs)

• Security testing and penetration testing

• Fairness and bias testing

• Audit findings and remediation

5.4 Standards & Certifications

• ISO 27001 (information security)

• ISO/IEC 42001 (AI management systems)

• NIST AI Risk Management Framework

• IEEE AI standards

• Cloud provider attestations (SOC 2)

• Compliance audit and certification

6.1 AI Bias & Fairness

• Types of bias (data, algorithmic, selection, measurement)

• Fairness definitions and trade-offs

• Bias detection and measurement

• Bias mitigation strategies

• Intersectional analysis

• Fairness in model monitoring

6.2 Model Transparency & Explainability

• Inherently interpretable models

• Model explanations (LIME, SHAP)

• Algorithm cards and model cards

• Explanation to non-technical stakeholders

• Explainability challenges (deep learning, LLMs)

• Transparency requirements and documentation

6.3 AI Safety & Robustness

• Out-of-distribution detection and handling

• Robustness testing (adversarial, natural)

• Edge case testing and failure modes

• Uncertainty estimation

• Human-in-the-loop safety

• Graceful degradation and rejection options

6.4 Responsible AI Practices

• Transparency and user disclosure

• Accountability mechanisms

• User consent and control

• Data rights and deletion

• Environmental sustainability

• Ethical AI principles