Description

This curriculum spans the equivalent of a multi-workshop technical advisory program, addressing the full lifecycle of AI system security with the depth required for internal capability building across engineering, compliance, and operations teams.

Module 1: Threat Modeling and Risk Assessment in Enterprise AI Systems

Conducting asset inventory for AI-specific components including models, training data, and inference endpoints to prioritize protection efforts.
Selecting threat modeling frameworks (e.g., STRIDE, PASTA) based on organizational maturity and AI deployment scale.
Mapping data flows across distributed AI pipelines to identify attack surfaces in data ingestion, preprocessing, and model serving.
Evaluating third-party model risks when integrating external APIs or pre-trained models into internal systems.
Quantifying risk exposure using FAIR methodology for AI-driven decision systems with high operational impact.
Documenting threat scenarios involving model inversion, membership inference, and training data extraction attacks.
Establishing risk acceptance thresholds for AI systems that influence financial, legal, or safety-critical outcomes.
Integrating threat modeling outputs into CI/CD pipelines for automated security validation during model retraining.

Module 2: Secure AI Development Lifecycle (SDL-AI)

Implementing code signing and artifact provenance for machine learning models using tools like Sigstore or in-house PKI.
Enforcing mandatory peer review of data preprocessing logic to prevent data leakage or bias amplification.
Embedding security unit tests in ML training pipelines to detect adversarial vulnerability during development.
Configuring isolated development environments with restricted data access based on role-based permissions.
Validating model inputs against schema and distribution constraints to mitigate data poisoning risks.
Requiring security sign-off before promoting models from staging to production inference environments.
Logging and monitoring all model version transitions to support auditability and rollback capability.
Enforcing secure coding standards for Python and containerized components used in AI workloads.

Module 3: Data Protection and Privacy in AI Workflows

Applying differential privacy parameters during model training based on sensitivity of input data and regulatory requirements.
Implementing tokenization or format-preserving encryption for structured sensitive data used in feature engineering.
Designing data minimization strategies that restrict training set scope to only what is necessary for model performance.
Conducting privacy impact assessments (PIA) for AI systems processing personally identifiable information (PII).
Configuring secure data masking in non-production environments used for model testing and validation.
Managing data retention policies for training datasets and model artifacts in compliance with GDPR or CCPA.
Using synthetic data generation with statistical fidelity checks when real data cannot be legally shared.
Enforcing data lineage tracking from source to model input to support breach investigation and compliance audits.

Module 4: Model Integrity and Adversarial Robustness

Implementing cryptographic hashing and digital signatures to verify model integrity during deployment.
Integrating adversarial example detection layers in deep learning inference pipelines using defensive distillation or input purification.
Running periodic red team exercises to test model resilience against evasion, poisoning, and extraction attacks.
Selecting robustness evaluation metrics (e.g., accuracy under perturbation, certified radius) for model validation.
Deploying runtime monitoring to detect anomalous input patterns indicative of adversarial probing.
Configuring model rollback procedures in response to integrity compromise or performance degradation.
Choosing between defensive pruning, adversarial training, and input randomization based on model type and use case.
Documenting model limitations and known vulnerabilities in internal security playbooks.

Module 5: Access Control and Identity Management for AI Systems

Implementing attribute-based access control (ABAC) for model endpoints handling sensitive inference requests.
Integrating AI service identities into enterprise IAM systems using short-lived credentials and federated trust.
Enforcing least privilege for data scientists accessing production model APIs and training infrastructure.
Configuring audit logging for all access to model weights, configuration files, and inference logs.
Managing role transitions when personnel move between research, development, and operations teams.
Securing service-to-service communication between data stores, training clusters, and inference servers using mTLS.
Implementing just-in-time (JIT) access for third-party vendors supporting AI infrastructure.
Enforcing multi-factor authentication for administrative access to model management consoles.

Module 6: Monitoring, Logging, and Incident Response for AI Deployments

Designing log schemas that capture model inputs, outputs, metadata, and confidence scores for forensic analysis.
Deploying anomaly detection on inference traffic to identify data drift, concept drift, or malicious probing.
Integrating AI system alerts into existing SIEM platforms with correlation rules for suspicious behavior patterns.
Establishing incident playbooks specific to model compromise, data leakage, or adversarial manipulation.
Conducting tabletop exercises for scenarios involving AI-driven decision failures with regulatory implications.
Preserving chain-of-custody for model artifacts during incident investigations to support legal proceedings.
Configuring real-time alerting on unauthorized model download or export attempts from training environments.
Defining escalation paths for AI-related security events involving legal, compliance, and public relations teams.

Module 7: Regulatory Compliance and Audit Readiness

Mapping AI system controls to regulatory frameworks such as NIST AI RMF, ISO/IEC 42001, or sector-specific mandates.
Preparing documentation packages for auditors covering data provenance, model validation, and security testing results.
Implementing automated control checks for AI systems to maintain continuous compliance posture.
Responding to data subject access requests (DSARs) involving AI-generated decisions or profiling.
Conducting third-party audits of AI vendors using standardized assessment questionnaires (e.g., CAIQ).
Managing jurisdictional data transfer risks when training models across global data centers.
Documenting model bias testing and mitigation efforts to demonstrate fairness compliance.
Updating compliance artifacts following model retraining or significant pipeline changes.

Module 8: Secure Deployment and Infrastructure Hardening

Hardening container images for AI workloads by minimizing base OS footprint and removing unnecessary tools.
Isolating GPU-accelerated training clusters from general corporate networks using microsegmentation.
Implementing secure boot and firmware validation on servers hosting sensitive model training jobs.
Configuring network policies to restrict outbound traffic from inference endpoints to approved destinations only.
Using hardware security modules (HSMs) or trusted platform modules (TPMs) for key management in model encryption.
Enforcing runtime protection on Kubernetes clusters running AI microservices using policy engines like OPA.
Performing vulnerability scanning on AI dependencies (e.g., PyTorch, TensorFlow) before deployment.
Designing fail-safe mechanisms for AI systems that degrade gracefully under denial-of-service conditions.

Module 9: Governance and Cross-Functional Coordination

Establishing AI governance boards with representation from security, legal, data science, and business units.
Defining ownership roles for model risk management across development, operations, and compliance teams.
Creating standardized risk rating criteria for AI projects based on impact, data sensitivity, and autonomy level.
Implementing change advisory boards (CABs) for approving high-risk model updates or infrastructure changes.
Developing escalation protocols for AI-related security events that cross departmental boundaries.
Conducting cross-training sessions between security teams and data scientists to align threat models and terminology.
Managing vendor risk for cloud-based AI platforms by reviewing shared responsibility models and audit reports.
Updating enterprise risk registers to include AI-specific threats and control gaps.