A tailored course, built for your situation
Mastering CIS Controls for AI Research Engineers in Core Machine Learning
A step-by-step framework for securing high-impact AI systems with precision and authority
$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 demands in AI are outpacing traditional compliance playbooks, practitioners need more than policy templates.
The situation this course is for
AI teams are being asked to 'secure faster' without clarity on what 'secure' actually means across infrastructure, dependencies, and access controls, especially when regulators begin probing model provenance and training data integrity.
Who this is for
Senior AI research engineer operating at the intersection of ML systems, data integrity, and infrastructure security, often pulled into security reviews without formal authority or framework fluency.
Who this is not for
This course is not for junior compliance staff, external auditors, or engineers focused solely on model accuracy with no cross-functional security exposure.
What you walk away with
- Select and justify priority CIS Controls for AI training environments
- Map model development workflows to control objectives with confidence
- Produce audit-ready documentation that withstands internal security review
- Anticipate and redirect low-leverage security requests using control mapping
- Position yourself as the go-to practitioner for infrastructure-hardening decisions
The 12 modules (with all 144 chapters)
Module 1. CIS Controls and the AI Research Context
Understand how CIS Controls apply beyond IT operations to AI infrastructure, particularly in data pipelines and model deployment environments.
12 chapters in this module
- What CIS Controls are not
- Where AI differs from general IT
- The core control families
- Mapping AI risks to controls
- Why version 8 matters
- Control specificity vs generality
- Integration with ML lifecycle
- Common misapplications
- Signal vs noise in control selection
- The role of automation
- Human oversight touchpoints
- Foundational assumptions
Module 2. Identifying Critical Assets in AI Systems
Define what counts as a critical asset in ML contexts: models, data, compute, and dependencies.
12 chapters in this module
- Model as asset
- Training data provenance
- Checkpoint repositories
- Access to GPU clusters
- Third-party libraries
- Pipeline orchestration tools
- Feature stores
- Metadata databases
- Model serving endpoints
- Logging and telemetry
- MLOps platforms
- Vendor SDKs
Module 3. Inventory and Control of Hardware Assets
Apply CIS Control 1 to distributed AI infrastructure, including cloud instances and on-prem clusters.
12 chapters in this module
- Tracking GPU nodes
- Cloud instance tagging
- Auto-scaling group visibility
- Firmware version tracking
- Decommissioning protocols
- Hardware lifecycle stages
- Ownership assignment
- Host-level inventory
- Physical vs virtual
- Cloud provider reporting
- Hardware trust chains
- Hardware-based security modules
Module 4. Inventory and Control of Software Assets
Map software components in AI systems including frameworks, dependencies, and tools.
12 chapters in this module
- Python package tracking
- Container image provenance
- ML framework versions
- CUDA driver tracking
- Dependency trees
- Software bill of materials
- Open source license mapping
- Version control integration
- Automated drift detection
- Software ownership model
- Approved software list
- Shadow AI detection
Module 5. Secure Configurations for General-Purpose Systems
Implement hardened baselines for systems used in AI development and deployment.
12 chapters in this module
- OS-level hardening
- SSH access policies
- Firewall baseline rules
- Disk encryption standards
- User privilege defaults
- Service account isolation
- Time synchronization
- Log aggregation setup
- Network segmentation
- DNS configuration
- Kernel parameter tuning
- Host-based intrusion prevention
Module 6. Secure Configurations for Mobile and Endpoint Devices
Apply endpoint controls to developer and researcher devices used in AI work.
12 chapters in this module
- Laptop encryption
- Remote wipe capability
- Endpoint detection tools
- USB device control
- Wi-Fi security policy
- VPN usage rules
- Screen lock timing
- Device ownership tracking
- Personal device policy
- Developer workflow impact
- Browser security
- Email client hardening
Module 7. Controlled Use of Administrative Privileges
Manage admin access for AI infrastructure without impeding innovation.
12 chapters in this module
- Just-in-time access
- Privileged account inventory
- Session logging
- Break glass procedures
- Role-based access
- Sudo policy design
- Credential rotation
- Multi-factor for admin
- Admin workstation hardening
- Escalation workflows
- Audit trail integration
- Privilege creep monitoring
Module 8. Maintenance, Monitoring, and Analysis of Audit Logs
Ensure visibility into AI system operations through effective logging.
12 chapters in this module
- Model training logs
- Data access logs
- GPU utilization
- Authentication events
- Model upload events
- Pipeline trigger logs
- Log retention policies
- Centralized collection
- Log format standards
- Log integrity checks
- Anomaly detection
- Incident response readiness
Module 9. Email and Web Browser Protections
Secure high-trust communication channels used by AI teams.
12 chapters in this module
- Phishing-resistant MFA
- Email filtering rules
- Link scanning
- Browser sandboxing
- Extension control
- Ad tracking prevention
- Certificate validation
- Secure browsing policies
- Team communication apps
- Secure file sharing
- Code repository access
- Internal documentation tools
Module 10. Malware Defenses and System Protection
Implement layered defenses tailored to AI development environments.
12 chapters in this module
- Antivirus on dev machines
- Container image scanning
- Model poisoning detection
- Data integrity checks
- Network intrusion detection
- GPU driver verification
- Model checksum validation
- Training data sanitization
- Sandboxed inference
- Zero-day response
- Patch deployment cycles
- Threat intelligence feeds
Module 11. Data Protection and Encryption at Rest
Apply encryption best practices to sensitive AI datasets and models.
12 chapters in this module
- Model encryption keys
- Training data at rest
- Checkpoint encryption
- Feature store encryption
- Key management systems
- Access control integration
- Decryption audit
- Data residency rules
- Encryption policy alignment
- Hardware security modules
- Key rotation
- Recovery procedures
Module 12. Integration and Leadership Through CIS Controls
Use CIS Controls as leverage to lead cross-functional security initiatives.
12 chapters in this module
- Positioning as security lead
- Engaging non-security teams
- Presenting control rationale
- Documenting implementation
- Creating reference playbooks
- Training team members
- Reporting to leadership
- Influencing roadmap
- Vendor security reviews
- Contractual obligations
- Future regulation readiness
- Thought leadership positioning
How this maps to your situation
- When joining a new AI project with undefined security posture
- Before a model audit or external review
- During infrastructure redesign or migration
- When responding to an internal security escalation
Before vs. after
Before
Reactive on security, pulled into reviews without control mapping, relying on others to define scope.
After
Proactive leadership, owning security posture definition and shaping project direction through structured CIS application.
What's included with your purchase
- 12 modules with 12 chapters each (144 chapters total)
- 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 3 hours per module, designed to be completed alongside full-time work over 6-8 weeks.
If nothing changes
Without structured security fluency, high-impact AI projects will default to generic compliance pathways, limiting your influence and relegating you to technical contributor status.
How this compares to the alternatives
Unlike generic compliance courses, this program is tailored to AI research engineers, focusing on real decisions like model checkpoint encryption, GPU access control, and dependency hardening, not generic enterprise IT scenarios.
Frequently asked
Is this course about passing a certification?
No. This is not a certification prep course. It's designed for practitioners who need to apply CIS Controls effectively in high-stakes AI environments.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me lead security initiatives?
Yes. The course is designed to position you as the go-to practitioner for security decisions in AI projects.
$199 one-time. Approximately 3 hours per module, designed to be completed alongside full-time work over 6-8 weeks..
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