A tailored course, built for your situation
Mastering CIS Controls for Senior AI and Deep Learning Engineers
Turn deep learning expertise into strategic control frameworks that attract premium engagements
$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.
Most AI engineers operate downstream of compliance, they implement what’s handed to them. But the most strategic practitioners are now leading with control frameworks to shape engagements from the start.
The situation this course is for
Even highly skilled engineers miss opportunities to lead because they’re seen as execution-only. Without a structured way to position control integration as a value multiplier, they stay out of the room where high-budget, high-impact projects are scoped.
Who this is for
Senior AI/ML engineers in regulated or high-assurance environments who want to transition from technical implementers to strategic contributors who shape project selection and architecture.
Who this is not for
Entry-level data scientists, software developers without AI/ML focus, or professionals outside technical roles in compliance, risk, or engineering.
What you walk away with
- Lead AI engagements using CIS Controls as a differentiator to win higher-budget projects
- Design model documentation that satisfies auditors and builds stakeholder trust upfront
- Position control integration as a time-saver, not an overhead tax, in system design reviews
- Build repeatable templates for control mapping across AI pipelines
- Earn first access to strategic initiatives where compliance and innovation intersect
The 12 modules (with all 144 chapters)
Module 1. Introducing CIS Controls in AI System Design
Establish the role of CIS Controls in modern AI deployment, especially in regulated environments. Understand how control-first thinking elevates engineering impact.
12 chapters in this module
- Why CIS Controls matter in AI governance
- The engineer’s strategic advantage
- Mapping control domains to AI pipelines
- Real-world impact at life sciences firms
- Control integration without slowing innovation
- From reactive to proactive compliance
- The stakeholder trust multiplier
- Benchmarking against peer implementations
- Common misconceptions engineers face
- How controls prevent technical debt
- Aligning with enterprise risk appetite
- Setting up your control-first mindset
Module 2. CIS Control 1: Inventory and Control of Hardware Assets
Apply strict asset governance to AI infrastructure. Ensure all compute resources used in training and inference are tracked, authorized, and secured.
12 chapters in this module
- Tracking GPU clusters and edge devices
- Automated hardware inventory tagging
- Secure boot requirements for AI nodes
- Asset ownership in hybrid environments
- Integration with procurement systems
- Decommissioning unapproved hardware
- Cloud instance governance strategies
- Asset logs for audit readiness
- Role-based access to hardware lists
- Tagging AI-specific hardware assets
- Avoiding shadow compute usage
- Building a living hardware register
Module 3. CIS Control 2: Inventory and Control of Software Assets
Maintain a precise, up-to-date inventory of all software used in AI development, from frameworks to dependencies.
12 chapters in this module
- Tracking Python packages and versions
- Detecting unapproved libraries
- Automated dependency scanning
- Version control for model codebases
- License compliance in open-source tools
- Enforcing software whitelists
- Container image governance
- SBOMs for AI pipelines
- Software lifecycle tracking
- Integration with CI/CD pipelines
- Detecting deprecated frameworks
- Building a software truth source
Module 4. CIS Control 3: Data Protection
Implement robust data handling practices across AI training and inference workflows, especially with sensitive genomic and health data.
12 chapters in this module
- Classifying data sensitivity levels
- Encryption at rest and in transit
- Masking sensitive training data
- Data retention policies
- Secure data sharing protocols
- Access logging for data sets
- Tokenization strategies
- Data provenance tracking
- Minimizing data footprint
- Anonymization techniques
- Data use agreements
- Audit trails for data access
Module 5. CIS Control 4: Secure Configuration
Ensure all systems involved in AI workflows follow hardened, secure configuration baselines.
12 chapters in this module
- Hardening OS images for AI nodes
- Disabling unnecessary services
- Secure container configurations
- Default-deny network policies
- Kernel parameter tuning
- SSH and remote access security
- Automated config compliance checks
- Baseline templates for AI clusters
- Managing configuration drift
- Patch verification workflows
- Secure boot enforcement
- Configuration as code
Module 6. CIS Control 5: Account Management
Control access to AI systems with strong identity governance and least-privilege principles.
12 chapters in this module
- Role-based access control design
- Service account lifecycle
- Multi-factor authentication
- Federated identity integration
- Access request workflows
- Privileged account monitoring
- Just-in-time access
- Account deprovisioning
- User access reviews
- Credential rotation
- Break-glass account policies
- Identity provider integration
Module 7. CIS Control 6: Access Control
Enforce least privilege and zero trust principles across AI model deployment and data access.
12 chapters in this module
- Principle of least privilege
- Attribute-based access control
- Network segmentation
- Micro-segmentation for AI services
- API gateway authorization
- Model access auditing
- Data access controls
- Project-level isolation
- Dynamic access policies
- Context-aware access decisions
- Access revocation workflows
- Access control documentation
Module 8. CIS Control 7: Continuous Vulnerability Management
Systematically detect and remediate vulnerabilities in AI software stacks and infrastructure.
12 chapters in this module
- Automated scanning schedules
- Prioritizing AI-relevant CVEs
- Integrating scanners into CI/CD
- Patch deployment workflows
- Vulnerability scoring models
- False positive reduction
- Zero-day coordination
- Developer remediation guidance
- Patch validation
- Remediation SLAs
- Third-party library risks
- Vulnerability reporting
Module 9. CIS Control 8: Audit Log Management
Centralize and secure logging across AI systems for transparency and forensic readiness.
12 chapters in this module
- Identifying critical log sources
- Log retention policies
- Immutable log storage
- Centralized log aggregation
- Real-time alerting
- Correlation across systems
- Log access controls
- Audit trail completeness
- Log format standardization
- Event time synchronization
- Log integrity verification
- Compliance reporting
Module 10. CIS Control 9: Email and Web Browser Protections
Secure endpoints used by engineering teams to reduce attack surface from phishing and malicious content.
12 chapters in this module
- Browser hardening settings
- Email attachment filtering
- URL rewriting services
- Safe browsing policies
- Extension control
- Phishing simulation response
- User training integration
- Endpoint monitoring
- Domain reputation checks
- Malware sandboxing
- Quarantine workflows
- Reporting mechanisms
Module 11. CIS Control 10: Malware Defenses
Deploy layered anti-malware strategies across AI development and deployment environments.
12 chapters in this module
- Endpoint protection platforms
- Behavioral detection
- Signature-based scanning
- Cloud workload protection
- Model poisoning detection
- Supply chain integrity
- Container scanning
- Memory exploit protection
- Heuristic analysis
- Automated quarantine
- Incident response integration
- Threat intelligence feeds
Module 12. Implementing CIS Controls in AI Projects
Apply integrated control practices across real-world AI initiatives to demonstrate value and ensure compliance by design.
12 chapters in this module
- Scoping control integration
- Stakeholder alignment
- Control integration timelines
- Documentation templates
- Audit preparation
- Lessons from life sciences
- Scaling across teams
- Feedback loops
- Metrics for success
- Continuous improvement
- Executive communication
- Course wrap-up and next steps
How this maps to your situation
- When launching a new AI initiative
- Before audit readiness reviews
- During vendor integration
- After control gaps are identified
Before vs. after
Before
Deploying AI models with compliance as an afterthought, reacting to auditor questions, and missing high-impact project invitations.
After
Leading with control frameworks to shape project scope, winning premium engagements, and building trusted, audit-ready AI systems from day one.
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 3 hours per module, designed for flexible, self-paced learning alongside full-time work.
If nothing changes
Continuing to deliver technically excellent models without control integration means staying excluded from strategic planning, missing higher-margin opportunities, and being seen as execution-only rather than a value driver.
How this compares to the alternatives
Unlike generic compliance courses, this program is built specifically for senior AI engineers in regulated environments, with real-world templates and control mappings relevant to life sciences and precision health.
Frequently asked
Who is this course for?
Senior AI and deep learning engineers working in regulated environments who want to lead strategic, high-compliance projects.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Can I apply this to non-healthcare AI projects?
Yes, while examples are drawn from life sciences, the control frameworks apply to any high-assurance AI environment.
$199 one-time. Approximately 3 hours per module, designed for flexible, self-paced learning alongside full-time work..
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