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SEC2635 Mastering CIS Controls; A Step-by-Step Guide to ML Infrastructure Hardening

$199.00
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A tailored course, built for your situation

Mastering CIS Controls; A Step-by-Step Guide to ML Infrastructure Hardening

A complete implementation pathway for securing machine learning systems at scale

$199 one-time
24-hour access provisioning 30-day money-back guarantee Hand-built implementation playbook
12 modules. 12 chapters per module. 144 chapters total.
12 modules, each with 12 chapters (144 chapters total), text-based, plus downloadable templates and a hand-built implementation playbook delivered alongside course access.
Control documentation that requires rework during red team reviews

Who this is for

Senior ML Engineers leading infrastructure hardening efforts in high-velocity AI organizations where security, scalability, and reproducibility intersect

Who this is not for

Engineers focused solely on model accuracy tuning or data pipeline logistics without ownership of deployment-hardening workflows

What you walk away with

  • Produce control-ready ML infrastructure documentation on the first pass
  • Reduce pre-deployment review time from 80+ hours to under one workday
  • Design systems that pass internal red team scrutiny without rework loops
  • Establish a reusable template for secure model deployment across teams
  • Become the internal reference for hardened ML architecture patterns

The 12 modules (with all 144 chapters)

Module 1. Foundations of CIS Controls in ML Systems
Establish the core mapping between CIS Control domains and ML infrastructure layers, focusing on asset inventory, secure configuration, and access enforcement specific to model serving environments.
12 chapters in this module
  1. How CIS Controls apply to ML model serving endpoints
  2. Mapping control language to infrastructure-as-code resources
  3. Identifying ML-specific scope boundaries for compliance
  4. Common misalignments between security baselines and MLOps workflows
  5. Integrating control objectives into Sprint Zero checklists
  6. Defining 'in-scope' components in distributed training jobs
  7. Baseline hardening for GPU node configurations
  8. Secure defaults for containerized inference workloads
  9. Control alignment for feature store access patterns
  10. Documenting system boundaries for audit readiness
  11. Versioning controls alongside model version updates
  12. Automating control validation at pipeline trigger time
Module 2. Asset Inventory and Device Tracking for ML Environments
Implement automated, real-time tracking of all ML-related hardware and virtual assets to satisfy CIS Control 1 requirements while reducing manual reconciliation effort.
12 chapters in this module
  1. Auto-discovery of ephemeral training instances
  2. Tagging strategy for Kubernetes jobs in ML clusters
  3. Integration with internal CMDB via metadata injection
  4. Detecting orphaned GPU workloads in staging environments
  5. Lifecycle tagging from notebook to production model
  6. Enforcing mandatory asset labeling at job submission
  7. Real-time inventory reporting for red team requests
  8. Mapping model versions to underlying compute nodes
  9. Handling spot instance volatility in asset tracking
  10. Automated stale job cleanup based on control thresholds
  11. Cross-referencing assets with access review cycles
  12. Building inventory exports for compliance dashboards
Module 3. Secure Configuration of ML Compute Nodes
Apply hardened configuration baselines to GPU and CPU instances used in training and inference, ensuring alignment with CIS Control 2 and reducing drift.
12 chapters in this module
  1. Hardened AMI templates for PyTorch training jobs
  2. Disabling unused services on inference containers
  3. SSH access hardening for debug sessions
  4. Kernel parameter tuning for ML workloads
  5. Ensuring consistent iptables rules across nodes
  6. Automated drift detection for node configurations
  7. Baseline configuration for Spot Instance reuse
  8. Handling OS patching in long-running experiments
  9. Secure boot validation in virtualized environments
  10. Logging configuration changes to central store
  11. Role-based image selection at job launch
  12. Version-controlled configuration rollouts
Module 4. Continuous Vulnerability Management in ML Pipelines
Embed proactive vulnerability scanning into training and deployment pipelines to meet CIS Control 3, reducing exposure windows for model dependencies.
12 chapters in this module
  1. Scanning Python dependencies in requirements.txt
  2. Detecting known CVEs in base Docker images
  3. Automated reporting of critical vulnerabilities
  4. Setting policy thresholds for pipeline blocking
  5. Integrating OSS vulnerability feeds into CI
  6. Handling false positives in ML library stacks
  7. Prioritizing fixes based on model criticality
  8. Tracking vulnerability age across experiments
  9. Enforcing fix timelines for high-risk components
  10. Generating exception requests for research libraries
  11. Reporting uptime between scans
  12. Benchmarking scan completeness across teams
Module 5. Controlled Use of Administrative Privileges
Implement least-privilege access patterns for ML infrastructure management, satisfying CIS Control 4 while maintaining developer velocity.
12 chapters in this module
  1. Role definition for MLOps engineers
  2. Time-bound access for production debugging
  3. Elevation workflows for cluster admin tasks
  4. Audit logging for privileged command execution
  5. Segregation of duties in model deployment
  6. Just-in-time access for incident response
  7. Credential rotation for service accounts
  8. Multi-party approval for namespace creation
  9. Mapping RBAC to team-level responsibilities
  10. Monitoring sudo usage in shared environments
  11. Automated revocation after task completion
  12. Access attestation integration with HR cycles
Module 6. Maintenance, Monitoring, and Logging for Model Systems
Design comprehensive logging, monitoring, and alerting architectures that satisfy CIS Control 6 and support rapid incident response in ML environments.
12 chapters in this module
  1. Centralized log ingestion for training jobs
  2. Structured logging schema for model inference
  3. Alert thresholds for GPU memory leaks
  4. Retention policies for debug artifacts
  5. Correlating logs across pipeline stages
  6. Anomaly detection in batch prediction latency
  7. Exporting logs for forensic analysis
  8. Monitoring for unauthorized export attempts
  9. Automated log review summaries
  10. Integrating with internal SOC workflows
  11. Handling PII in debug logs
  12. Cost-aware sampling for high-volume endpoints
Module 7. Email and Web Browser Protections for ML Teams
Extend endpoint security practices from CIS Control 7 to data science workstations and notebook environments used by ML teams.
12 chapters in this module
  1. Hardening browser extensions for JupyterHub
  2. Phishing-resistant configurations for research laptops
  3. Email attachment scanning for notebook files
  4. Browser policy enforcement in remote desktop sessions
  5. Blocking malicious JavaScript in shared notebooks
  6. Secure handling of model artifact downloads
  7. URL filtering for dataset sources
  8. Extension whitelisting for visualization tools
  9. Session timeout policies in cloud workstations
  10. Device trust validation at login
  11. Password manager integration for team accounts
  12. Reporting suspicious activity from IDEs
Module 8. Malware Defense in Development Environments
Implement layered defenses against malware in ML development and deployment pipelines, satisfying CIS Control 8 without impeding innovation.
12 chapters in this module
  1. Scanning uploaded datasets for embedded scripts
  2. Container image signing and verification
  3. Runtime protection for Python interpreters
  4. Detecting obfuscated code in model definitions
  5. Behavioral analysis of training job anomalies
  6. Network-level blocking of beaconing attempts
  7. Integrating with enterprise EDR platforms
  8. Sandboxing untrusted model imports
  9. Malware scanning for saved model checkpoints
  10. Detecting crypto-mining in GPU workloads
  11. Automated quarantine of suspicious jobs
  12. Incident response playbook for infected nodes
Module 9. Data Protection in ML Workflows
Apply encryption, access controls, and data handling policies to model training data and outputs in line with CIS Control 13.
12 chapters in this module
  1. Encryption of data at rest in feature stores
  2. In-transit encryption for model training jobs
  3. PII detection in unstructured training sets
  4. Access controls for sensitive model outputs
  5. Data retention policies for experiment logs
  6. Anonymization techniques for debugging
  7. Secure sharing of model artifacts
  8. Audit trails for dataset access
  9. Handling regulated data in cross-border teams
  10. Data classification metadata tagging
  11. Automated alerts for PII exposure
  12. Policy enforcement at inference time
Module 10. Secure Network Architectures for Distributed Training
Design zero-trust network topologies for ML clusters that satisfy CIS Control 12 and reduce attack surface.
12 chapters in this module
  1. Micro-segmentation for multi-tenant clusters
  2. Default-deny policies for inter-node traffic
  3. Encryption of distributed training comms
  4. Firewall rules for GPU direct memory access
  5. DNS filtering for external dataset sources
  6. Network visibility into model serving paths
  7. Automated peering validation
  8. Isolation of debugging sessions
  9. Handling multicast in large-scale training
  10. VPC flow log analysis for anomalies
  11. Service mesh integration for mTLS
  12. Network policy as code reviews
Module 11. Automated Policy Enforcement in CI/CD
Embed CIS Control validations directly into ML CI/CD pipelines, ensuring continuous compliance without manual overhead.
12 chapters in this module
  1. Gate checks for control compliance
  2. Automated policy evaluation at PR merge
  3. Baseline drift detection in staging
  4. Policy-as-code syntax for ML teams
  5. Integrating with existing linting workflows
  6. Fail-fast mechanisms for non-compliant code
  7. Reporting compliance status to leadership dashboards
  8. Versioning control policies alongside models
  9. Handling exceptions with audit trails
  10. Developer feedback loops for failed checks
  11. Custom rule creation for in-house patterns
  12. Integrating with internal compliance APIs
Module 12. Scaling Hardened ML Practices Across Teams
Extend proven security patterns from pilot teams to organization-wide ML adoption, fulfilling the Reach benefit through reusable design systems.
12 chapters in this module
  1. Template library for hardened model deployments
  2. Internal documentation hub for control patterns
  3. Onboarding process for new ML teams
  4. Cross-team design review cadence
  5. Metrics for measuring adoption velocity
  6. Integrating with internal training programs
  7. Feedback loop from red team findings
  8. Quarterly control refinements
  9. Leadership reporting on security posture
  10. Scaling automation to new regions
  11. Handling regulatory variations by market
  12. Building internal certification for ML engineers

How this maps to your situation

  • ML infrastructure hardening
  • Red team readiness
  • Cross-functional design authority
  • Secure deployment automation

Before vs. after

Before
Spending 80+ hours per deployment cycle reconciling security controls with red team requirements, relying on tribal knowledge and last-minute fixes.
After
Shipping hardened ML systems in under 6 hours of validation time, with documented, repeatable patterns that scale across teams and models.

What's included with your purchase

  • 12 modules with 12 chapters each (144 chapters)
  • Downloadable templates and worked examples for every module
  • Hand-built implementation playbook delivered alongside course access
  • 30-day money-back guarantee

Delivery and format

  • Course and learning environment access provisioned within 24 hours of purchase
  • Hand-built implementation playbook delivered alongside course access

Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.

Time investment: Approximately 90 minutes per module, designed to be consumed in weekly segments over a 12-week implementation cycle.

If nothing changes
Without a structured approach to control implementation, teams will continue to face recurring rework, delayed deployments, and inconsistent security postures, especially under scrutiny from security reviews or regulatory audits.

How this compares to the alternatives

Unlike generic security certifications or academic ML courses, this program delivers actionable, role-specific implementation patterns that integrate directly into existing MLOps workflows, focused on real-world control outcomes, not theory.

Frequently asked

Is this course focused on compliance or engineering implementation?
It's focused entirely on engineering implementation, how to build and document ML systems that naturally satisfy compliance requirements without extra work.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this work for non-US teams?
Yes, the control patterns are global, and the course includes guidance for adapting to regional data handling variations.
$199 one-time. Approximately 90 minutes per module, designed to be consumed in weekly segments over a 12-week implementation cycle..

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.

30-day money-back guarantee· 144 chapters· Hand-built playbook included· Account access within 24 hours
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