Description

This curriculum spans the breadth of securing AI systems across the enterprise, comparable in scope to an ongoing internal capability program that integrates security into every phase of the AI lifecycle, from data handling and model development to deployment, monitoring, and governance.

Module 1: Threat Modeling and Risk Assessment in AI Systems

Conducting STRIDE-based threat modeling for machine learning pipelines to identify spoofing, tampering, and repudiation risks at data ingestion points.
Selecting appropriate risk scoring methodologies (e.g., DREAD or CVSS) to prioritize vulnerabilities in AI model endpoints exposed to external users.
Mapping data flow diagrams for AI training environments to detect unauthorized data exfiltration paths across cloud, on-prem, and edge deployments.
Integrating threat intelligence feeds into model development cycles to anticipate adversarial attacks such as model inversion or membership inference.
Assessing third-party model risks when leveraging pre-trained models from public repositories like Hugging Face or TensorFlow Hub.
Documenting residual risks associated with model explainability gaps in high-stakes decision systems (e.g., credit scoring or healthcare diagnostics).
Establishing thresholds for acceptable model drift that trigger re-evaluation of threat models after production deployment.
Coordinating cross-functional workshops with data scientists, infrastructure engineers, and compliance officers to validate threat assumptions.

Module 2: Secure Data Lifecycle Management for AI

Implementing data classification policies to distinguish between PII, sensitive operational data, and public datasets used in model training.
Designing retention schedules for training data that align with GDPR, CCPA, and sector-specific regulations without compromising model reproducibility.
Enforcing cryptographic erasure techniques for training datasets stored in distributed file systems like HDFS or S3 after deletion requests.
Configuring access control lists (ACLs) and bucket policies to restrict access to raw training data in cloud storage environments.
Applying tokenization or pseudonymization to sensitive features before ingestion into shared development environments.
Auditing data lineage logs to verify that datasets used in production models have not been altered post-approval.
Managing secure data sharing agreements when collaborating with external research partners on joint AI initiatives.
Deploying data loss prevention (DLP) tools to monitor and block unauthorized transfers of training datasets via email or USB.

Module 3: Model Integrity and Anti-Tampering Controls

Signing trained models using cryptographic hashes and digital signatures to detect unauthorized modifications during deployment.
Implementing model watermarking techniques to assert ownership and detect illicit redistribution of proprietary AI assets.
Enforcing immutable model registries using blockchain-based or append-only ledger systems for auditability.
Configuring runtime integrity checks to detect model poisoning or parameter manipulation in inference containers.
Validating model inputs against schema constraints to prevent adversarial examples from triggering unexpected behavior.
Isolating model update processes through CI/CD pipelines with mandatory peer review and automated security scanning.
Monitoring model prediction drift to identify potential sabotage or data poisoning in real-time inference systems.
Requiring hardware-based attestation (e.g., Intel SGX or AWS Nitro Enclaves) for high-assurance model execution environments.

Module 4: Access Governance and Identity Management for AI Platforms

Defining role-based access control (RBAC) policies for data scientists, MLOps engineers, and auditors in shared AI workbenches.
Integrating identity providers (IdP) with AI development platforms using SAML or OIDC for centralized authentication.
Enforcing just-in-time (JIT) access to production model endpoints using privileged access management (PAM) tools.
Implementing attribute-based access control (ABAC) for fine-grained data access based on project, clearance, and data sensitivity.
Rotating service account credentials used by automated model training jobs on a scheduled basis with automated key management.
Logging and reviewing access patterns to model artifacts and training logs to detect insider threats or privilege escalation.
Enforcing multi-factor authentication (MFA) for all administrative access to model deployment consoles and cloud AI services.
Mapping access permissions to job functions using least privilege principles during quarterly access recertification cycles.

Module 5: Secure Model Deployment and Inference Protection

Hardening inference APIs with rate limiting, input validation, and TLS 1.3 to prevent denial-of-service and injection attacks.
Deploying models in isolated containers with minimal OS packages and read-only filesystems to reduce attack surface.
Implementing mutual TLS (mTLS) between model servers and upstream data sources to ensure end-to-end trust.
Obfuscating model architecture details in API responses to prevent model extraction or reconstruction attacks.
Configuring network segmentation to restrict inference endpoints from accessing non-essential internal systems.
Using secure enclaves or confidential computing environments for inference on highly sensitive data (e.g., medical records).
Monitoring inference request payloads for anomalous patterns indicative of model probing or reverse engineering attempts.
Applying model quantization or pruning techniques that reduce model size while maintaining security controls.

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

Centralizing logs from training jobs, model servers, and data pipelines into SIEM platforms with AI-specific parsing rules.
Defining detection rules for anomalous model behavior such as sudden accuracy drops or unexpected output distributions.
Establishing incident playbooks for responding to model compromise, data leakage, or adversarial attacks.
Conducting red team exercises to simulate model stealing or data poisoning and validate detection coverage.
Correlating authentication logs with model access patterns to detect credential misuse or lateral movement.
Implementing real-time alerting for unauthorized model export attempts or configuration changes in MLOps tools.
Preserving forensic artifacts such as training data snapshots, model checkpoints, and environment configurations for post-incident analysis.
Integrating AI system alerts into existing SOAR platforms for automated containment and response workflows.

Module 7: Regulatory Compliance and Audit Readiness

Mapping AI system components to compliance frameworks such as NIST AI RMF, ISO/IEC 23894, and EU AI Act requirements.
Documenting model risk assessments and control implementations for internal and external auditors.
Preparing data protection impact assessments (DPIAs) for AI systems processing personal data at scale.
Implementing model cards and data sheets to provide transparency on training data sources, limitations, and fairness metrics.
Configuring audit trails with immutable storage for all model training, evaluation, and deployment activities.
Responding to data subject access requests (DSARs) involving AI-generated decisions by reconstructing model inputs and logic.
Validating that third-party AI vendors provide evidence of compliance with contractual security and privacy obligations.
Conducting annual compliance reviews to update controls in response to evolving regulatory interpretations.

Module 8: Supply Chain Security for AI Components

Scanning open-source model libraries and dependencies for known vulnerabilities using SBOMs and tools like Snyk or Trivy.
Establishing approval workflows for introducing new AI frameworks or pre-trained models into production environments.
Verifying the authenticity of model weights and datasets downloaded from public repositories using checksums and GPG signatures.
Enforcing secure coding standards for custom model code developed in-house to prevent injection and memory corruption flaws.
Requiring software bills of materials (SBOMs) from AI platform vendors for transparency into embedded components.
Isolating development environments used for AI experimentation from production networks to contain supply chain compromises.
Monitoring for typosquatting and malicious packages in Python package indexes that mimic legitimate AI libraries.
Conducting vendor security assessments for cloud AI services to evaluate configuration risks and shared responsibility boundaries.

Module 9: Governance and Organizational Accountability

Establishing an AI governance board with representatives from legal, security, data science, and business units.
Defining escalation paths for reporting model misuse, security incidents, or ethical concerns related to AI outputs.
Assigning data stewards and model owners with clear accountability for security and compliance throughout the AI lifecycle.
Developing policies for acceptable use of AI systems to prevent deployment in prohibited or high-risk domains.
Conducting regular training for technical staff on secure AI development practices and emerging threat vectors.
Implementing model inventory systems to track all active, deprecated, and archived AI models across the enterprise.
Requiring security and privacy reviews before launching new AI initiatives involving customer or employee data.
Performing independent audits of high-risk AI systems by internal or external assessors on an annual basis.