A tailored course, built for your situation
Mastering CIS Controls for Machine Learning Tech Leads
A step-by-step path to owning the security posture of AI/ML systems in production
$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.
Security review fatigue for deployed ML systems
The situation this course is for
ML teams face recurring cycles of last-minute evidence collection, misaligned control expectations, and cross-functional friction when audit timelines hit. The burden falls heaviest on tech leads who must reconcile innovation speed with hard security boundaries.
Who this is for
Senior technical leader in AI/ML at a large technology company, responsible for model deployment at scale, increasingly pulled into security and compliance conversations without formal frameworks to guide implementation.
Who this is not for
Entry-level engineers, non-technical compliance staff, or leaders without hands-on responsibility for deployed ML systems. This is not for teams using AI only in experimental or research phases.
What you walk away with
- Produce audit-ready security evidence for ML systems in under one business day
- Lead cross-functional security reviews with clear control ownership and documented mappings
- Implement repeatable hardening playbooks across model pipelines
- Reduce rework cycles between security and ML teams by aligning on CIS baselines
- Own the security sign-off criteria for new model deployments
The 12 modules (with all 144 chapters)
Module 1. Introduction to CIS Controls in Machine Learning Environments
Lays the foundation for applying CIS benchmarks to AI/ML systems, emphasizing the shift from general IT compliance to model-specific security postures.
12 chapters in this module
- Understanding the evolution of CIS Controls in high-scale tech environments
- Mapping CIS v8 structure to machine learning infrastructure components
- Key differences between general IT hardening and ML system hardening
- The role of the ML tech lead in setting security baseline expectations
- How Meta's production environment complexity shapes control priorities
- Identifying high-impact CIS controls for model training and serving layers
- Integrating CIS with existing MLOps workflows without slowing deployment
- Common misconceptions about security frameworks slowing innovation
- Establishing control ownership in cross-functional AI teams
- Measuring maturity against Level 1 and Level 2 CIS benchmarks
- Using CIS as a communication tool between security and engineering
- Setting expectations for audit readiness in quarterly review cycles
Module 2. Asset Inventory and Model Lineage Tracking
Covers exact methods for maintaining real-time visibility into deployed models and their dependencies, a core CIS requirement.
12 chapters in this module
- Defining what constitutes a 'system' in ML infrastructure for inventory purposes
- Automating discovery of training jobs across distributed compute clusters
- Tracking model versions from development to production deployment
- Maintaining an up-to-date inventory of inference endpoints and APIs
- Linking model assets to underlying data pipelines and storage locations
- Using metadata tagging strategies to support CIS control 1.4
- Implementing automated alerts for unauthorized model deployment
- Integrating asset inventory with internal configuration management databases
- Handling ephemeral workloads and short-lived containers in audits
- Documenting shadow AI projects without central governance
- Standardizing naming conventions across research and production teams
- Producing auditable lineage reports on demand
Module 3. Secure Configuration for ML Infrastructure
Focuses on hardening compute, storage, and networking layers used in ML workflows.
12 chapters in this module
- Applying CIS Benchmark 4.0 to GPU-accelerated compute nodes
- Standardizing base images for containerized training environments
- Disabling unnecessary services on machine learning instances
- Configuring secure defaults for distributed training frameworks
- Enforcing encryption in transit for model data pipelines
- Managing SSH access controls on Jupyter host servers
- Securing model checkpoint storage in cloud object stores
- Hardening Kubernetes clusters used for model serving
- Applying network segmentation to isolate training workloads
- Controlling container runtime permissions in CI/CD pipelines
- Validating secure configurations using automated scanning tools
- Maintaining compliance across hybrid cloud and on-prem environments
Module 4. Access Control for Model Development and Deployment
Details role-based access strategies tailored to ML team structures and workflows.
12 chapters in this module
- Designing least privilege access for data scientists on training clusters
- Separating duties between model development and production deployment
- Implementing time-bound access for external collaborators
- Managing service account permissions for automated pipelines
- Enforcing multi-factor authentication for model publishing APIs
- Auditing access changes during incident response workflows
- Integrating identity providers with ML platform dashboards
- Handling access revocation for departing team members
- Defining admin roles for model registry management
- Controlling access to sensitive feature stores and datasets
- Using attribute-based access control for cross-project resources
- Documenting access policies for internal audit requests
Module 5. Vulnerability Management in Model Pipelines
Covers identifying, prioritizing, and remediating security flaws in ML software dependencies.
12 chapters in this module
- Scanning Python and R dependencies for known CVEs in training images
- Integrating SCA tools into model build pipelines
- Assessing risk of third-party ML libraries before adoption
- Prioritizing patching based on model criticality and exposure
- Tracking open-source license compliance as part of vulnerability review
- Managing technical debt in legacy model serving systems
- Coordinating security patches with model retraining schedules
- Using automated dependency update pull requests in GitHub
- Establishing SLAs for vulnerability remediation in ML systems
- Documenting risk acceptance decisions for unavoidable vulnerabilities
- Integrating with internal bug bounty programs for AI systems
- Producing evidence of patching cadence for compliance reviews
Module 6. Audit Log Management for Model Behavior
Teaches implementation of comprehensive logging across ML systems for security monitoring.
12 chapters in this module
- Identifying high-risk events to log in model inference pipelines
- Ensuring integrity of logs from distributed training jobs
- Centralizing logs from containerized ML workloads
- Protecting log access with role-based permissions
- Maintaining log retention periods aligned with policy
- Using structured logging formats for machine learning events
- Detecting anomalies in model prediction patterns through logs
- Correlating security events across data access and model calls
- Generating audit trails for model performance drift incidents
- Supporting forensic investigations with complete log sets
- Validating log completeness before audit cycles
- Automating report generation from security log data
Module 7. Email and Defense Against Social Engineering
Addresses human-factor risks specific to high-trust ML teams.
12 chapters in this module
- Recognizing targeted phishing attempts against ML researchers
- Securing access to model repositories after team member spoofing
- Training engineers to verify requests for data access
- Implementing secure communication channels for model handoffs
- Detecting credential harvesting attempts in open-source forums
- Handling social engineering attempts during external collaborations
- Protecting API keys shared in team documentation
- Responding to impersonation attempts in cross-company projects
- Conducting tabletop exercises for credential compromise scenarios
- Educating new hires on secure collaboration practices
- Monitoring for unauthorized sharing of model architectures
- Establishing protocols for verifying external partner identities
Module 8. Data Protection in Machine Learning Workflows
Focuses on securing training data and model outputs throughout the lifecycle.
12 chapters in this module
- Classifying data sensitivity levels for ML use cases
- Encrypting data at rest in feature stores and data lakes
- Implementing access controls for personally identifiable information
- Masking sensitive data in development and testing environments
- Tracking data provenance for compliance with privacy regulations
- Preventing accidental exposure of training data in model outputs
- Securing model weights that may encode sensitive information
- Handling cross-border data transfer requirements
- Auditing data access patterns in large-scale ML systems
- Implementing data deletion workflows aligned with retention policies
- Validating de-identification techniques in production models
- Producing data governance evidence for external reviewers
Module 9. CIS Controls Integration with MLOps Platforms
Shows how to bake security into CI/CD pipelines and model deployment workflows.
12 chapters in this module
- Embedding CIS checks into automated model testing suites
- Requiring security approval gates before production deployment
- Automating control validation for model registry promotions
- Integrating policy as code tools into ML pipelines
- Running infrastructure as code scans for security misconfigurations
- Validating model serving environments against CIS baselines
- Using canary deployments to test security control stability
- Monitoring drift from approved configurations in production
- Generating compliance reports from pipeline execution data
- Alerting on deviations from established security standards
- Documenting exceptions for rapid experimentation phases
- Maintaining audit trails for configuration changes
Module 10. Incident Response Planning for Model Compromise
Prepares tech leads to respond to security events involving ML systems.
12 chapters in this module
- Defining what constitutes a model security incident
- Establishing communication protocols for ML system breaches
- Containing compromised model endpoints without disrupting service
- Forensic analysis of model poisoning attempts
- Assessing business impact of model performance degradation
- Coordinating with legal and PR teams on disclosure decisions
- Preserving evidence from distributed training environments
- Conducting post-mortems specific to ML system failures
- Updating controls based on incident learnings
- Testing response plans through red team exercises
- Documenting incident handling for regulator review
- Rebuilding trust after model integrity incidents
Module 11. Third-Party Risk Management for ML Tools
Covers assessing security practices of external vendors and open-source projects.
12 chapters in this module
- Evaluating security posture of open-source ML libraries
- Assessing vendor compliance with CIS Controls for cloud AI services
- Managing risk of pre-trained models from external sources
- Conducting due diligence on ML monitoring tool providers
- Reviewing software bills of materials for ML dependencies
- Establishing security requirements for external model collaborators
- Auditing API security in third-party model integration points
- Handling supply chain risks in automated ML pipelines
- Monitoring vendor patching cadence for critical components
- Documenting risk mitigation strategies for unsupported tools
- Negotiating security terms in contracts for ML platform services
- Producing evidence of due diligence for audit purposes
Module 12. Sustaining Compliance at ML Scale
Teaches methods to maintain CIS alignment as model deployment velocity increases.
12 chapters in this module
- Automating control validation across thousands of model variants
- Scaling security reviews without adding headcount
- Using machine learning to detect configuration drift
- Maintaining consistency across global ML teams
- Updating baselines as CIS Controls evolve
- Integrating compliance checks into self-service ML platforms
- Generating executive summaries of ML security posture
- Demonstrating continuous improvement to internal auditors
- Reducing manual effort in compliance reporting
- Building institutional knowledge that survives team changes
- Measuring security maturity over time
- Positioning your team as the standard-bearer for secure AI
How this maps to your situation
- Before deployment: securing development environments
- During deployment: hardening model pipelines
- After deployment: monitoring and incident response
- At scale: maintaining compliance across growing model inventory
Before vs. after
Before
Spending weeks assembling security evidence for audits, reacting to last-minute requests, and mediating conflicts between security teams and ML engineers.
After
Producing audit-ready documentation in hours, leading security reviews with confidence, and owning the hardening criteria for all ML deployments.
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: 90 minutes on a Sunday, plus optional 30-minute review per module to implement templates.
If nothing changes
Without structured security controls, ML systems face higher scrutiny, increased rework, and potential limitations on deployment scope, all of which constrain your ability to lead at the forefront of AI innovation.
How this compares to the alternatives
Unlike generic cybersecurity courses, this program focuses exclusively on the intersection of CIS Controls and machine learning systems, providing actionable, role-specific strategies you can apply immediately as a tech lead.
Frequently asked
Is this relevant if I'm not in a security role?
Yes. This course is designed for ML leaders who must navigate security requirements without shifting into a full-time compliance function.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help with internal Meta audits?
Yes. The implementation playbook includes templates tailored to large-scale tech environments and common audit evidence requirements.
$199 one-time. 90 minutes on a Sunday, plus optional 30-minute review per module to implement templates..
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