A tailored course, built for your situation
Mastering CIS Controls for Senior IT and AI Leadership Roles
Build unshakable control frameworks that scale with AI-driven data environments
$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.
Who this is for
Senior IT and AI leader in regulated life sciences environments managing data integrity, security, and AI deployment at scale
Who this is not for
Entry-level analysts, non-technical compliance staff, or practitioners outside data-intensive biotech or computational biology domains
What you walk away with
- Map CIS Controls precisely to AI/ML pipeline stages in genomic data processing
- Produce audit-ready control documentation aligned with NIST and SOC 2 expectations
- Customize baseline configurations for secure RNA analysis workloads
- Lead cross-functional security reviews with authority and clarity
- Automate control validation steps within Databricks and AWS environments
The 12 modules (with all 144 chapters)
Module 1. CIS Controls Overview and Relevance to Genomics
Understand how the CIS Controls framework aligns with high-stakes data environments like genomic research and AI-driven discovery. Establish foundational mapping to Illumina-scale data workflows.
12 chapters in this module
- Framework origins and evolution
- Role in life sciences security
- Key differences from ISO 27001
- Control prioritization logic
- Mapping to AI/ML lifecycle
- Integration with Databricks
- AWS infrastructure alignment
- Data classification levels
- Regulatory crosswalks
- Implementation milestones
- Stakeholder alignment model
- Baseline assessment design
Module 2. Inventory and Control of Hardware Assets
Establish complete visibility over compute resources used in RNA analysis pipelines. Implement asset tracking that survives cloud elasticity and lab rotation.
12 chapters in this module
- Hardware asset taxonomy
- Cloud instance tagging standards
- On-prem sequencing machines
- Auto-discovery configuration
- Decommissioning workflows
- Labeling automation rules
- Virtual machine tracking
- Container lifecycle mapping
- Shadow IT detection
- Hardware compliance reports
- Integration with ServiceNow
- Audit trail setup
Module 3. Inventory and Control of Software Assets
Track and govern AI modeling software across development, testing, and production. Enforce approved libraries in machine learning workflows.
12 chapters in this module
- Software registry design
- Python package controls
- R and Bioconductor governance
- Model version tracking
- Container image signing
- Jupyter notebook policies
- DevOps toolchain audit
- Third-party dependency checks
- License compliance automation
- CI/CD integration
- Open-source risk scanning
- Approved software list
Module 4. Secure Configurations for Hardware and Software
Implement hardened baselines for servers, workstations, and containers used in computational biology. Automate enforcement across hybrid environments.
12 chapters in this module
- CIS Benchmarks usage
- OS baseline design
- Firmware update policy
- Secure boot enforcement
- File integrity monitoring
- System hardening scripts
- Compliance as code
- Configuration drift alerts
- Golden image maintenance
- Patch cadence standards
- Automated remediation
- Validation reporting
Module 5. Continuous Vulnerability Management
Detect and prioritize vulnerabilities in genomic AI infrastructure. Streamline patching in high-availability research systems.
12 chapters in this module
- Scan frequency planning
- Critical system exemptions
- CVE severity mapping
- Automated ticketing
- Container vulnerability scan
- Docker image scanning
- Kubernetes node checks
- Dependency vulnerability tools
- Remediation SLAs
- False positive handling
- Reporting to leadership
- Integration with Jira
Module 6. Controlled Use of Administrative Privileges
Limit and monitor privileged access in AI/ML environments. Apply least privilege to data scientists and infrastructure teams.
12 chapters in this module
- Admin account taxonomy
- Just-in-time access
- Privileged session logging
- Password vault integration
- Break-glass procedures
- Role-based access control
- Sudo command logging
- Emergency override policy
- Review frequency standards
- Session recording
- PAM tool evaluation
- Access revocation triggers
Module 7. Maintenance, Monitoring, and Analysis of Audit Logs
Ensure complete logging coverage across AI training jobs, data access, and pipeline execution. Enable forensic readiness for regulatory audits.
12 chapters in this module
- Log source identification
- RNA data access logging
- Model training audit trail
- Centralized log aggregation
- Retention policies
- SIEM integration
- Query templates for audits
- Log integrity protection
- Anomaly detection rules
- Cross-system correlation
- Compliance report output
- Incident investigation path
Module 8. Email and Web Browser Protections
Secure endpoints used for collaboration and data sharing in genomics research. Reduce phishing risk without disrupting scientific productivity.
12 chapters in this module
- Browser security settings
- Email filtering rules
- Link scanning systems
- Attachment sandboxing
- Phishing drill frequency
- User training intervals
- Domain impersonation checks
- Secure browsing policies
- Extension governance
- Certificate validation
- Mobile device protection
- Incident response linkage
Module 9. Malware Defenses and Endpoint Protection
Deploy layered defenses across research workstations and AI compute clusters. Prevent compromise of sensitive genomic datasets.
12 chapters in this module
- EDR solution selection
- Antivirus deployment
- Behavioral monitoring
- Ransomware rollback plan
- Quarantine automation
- Signature update policy
- Zero-day detection
- Endpoint compliance checks
- Threat intelligence feeds
- False positive review
- Isolation procedures
- Recovery validation
Module 10. Data Recovery and Backup Integrity
Ensure recoverability of AI/ML models and genomic datasets. Validate backup integrity across distributed storage systems.
12 chapters in this module
- Backup frequency tiers
- RNA data replication
- Model checkpointing
- Air-gapped backup design
- Encryption in transit
- Retention period logic
- Test restore cadence
- Backup validation script
- Cloud-to-cloud backup
- Disaster recovery drill
- Chain of custody
- Audit readiness check
Module 11. Secure Network Architectures
Design network segmentation for AI/ML environments. Protect genomic data in transit and isolate compute clusters.
12 chapters in this module
- Network zoning model
- Microsegmentation design
- Firewall rule standardization
- VPC architecture
- DNS filtering
- Zero trust readiness
- Segmentation testing
- Network logging
- Traffic anomaly detection
- Cloud interconnect security
- Hybrid network policies
- Load balancer controls
Module 12. Implementation and Continuous Improvement
Operationalize CIS Controls within existing IT and AI governance structures. Sustain compliance through change management and leadership alignment.
12 chapters in this module
- Gap assessment process
- Prioritization matrix
- Stakeholder engagement plan
- Control ownership model
- Metrics dashboard
- Executive reporting
- Third-party audit prep
- Continuous monitoring
- Policy update cycle
- Training refresh schedule
- Lessons learned capture
- Next version planning
How this maps to your situation
- Genomic data pipeline security
- AI/ML model lifecycle governance
- High-assurance research infrastructure
- Regulated biotech IT leadership
Before vs. after
Before
Manual mapping of security controls to complex data workflows, inconsistent implementation across teams, reactive audit preparation
After
Systematic, repeatable application of CIS Controls across AI and genomics infrastructure, with documentation ready for regulators and leadership
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 45 minutes per module, designed to fit around executive schedules over six weeks
How this compares to the alternatives
Unlike generic compliance courses, this program is tailored to the specific data and infrastructure patterns of senior IT and AI leaders in life sciences , with concrete examples from RNA analysis and AI/ML deployment environments.
Frequently asked
Is this course relevant to non-IT security roles?
It's designed for senior technical leaders with responsibility for data integrity, AI deployment, and infrastructure oversight in regulated environments like genomics.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Can I apply this to cloud-based AI workloads?
Yes , examples and templates are built for AWS, Databricks, and hybrid cloud environments common in computational biology.
$199 one-time. Approximately 45 minutes per module, designed to fit around executive schedules over six 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