A tailored course, built for your situation
Mastering CIS Controls for Data & AI Technology Leaders
Build auditable, scalable security foundations that align with enterprise risk posture and unlock premium project mandates.
$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 technical leader in data, AI, or cloud infrastructure within a global enterprise, responsible for system resilience, compliance-adjacent deliverables, and cross-functional coordination.
Who this is not for
Junior engineers, auditors focused only on checklist compliance, or practitioners without influence over architecture or deployment decisions.
What you walk away with
- Articulate CIS Controls in engineering terms that accelerate sign-off from risk and compliance teams
- Design reusable control implementation patterns for AI/ML pipelines and data platforms
- Position yourself as the internal authority on secure-by-design system rollouts
- Unlock engagement priority on high-visibility transformation initiatives
- Produce documented, defensible playbooks that survive leadership changes
The 12 modules (with all 144 chapters)
Module 1. Understanding CIS Controls Framework Structure
Establish a working mental model of the CIS Critical Security Controls, including v8 hierarchy, implementation groups, and mapping to data and AI infrastructure roles.
12 chapters in this module
- Overview of CIS Controls evolution and industry adoption trends
- Differentiating between IG1, IG2, and IG3 implementation expectations
- How the 18 control families align with data pipeline architecture
- Mapping control objectives to AI system development lifecycle
- Key terminology and decision boundaries for technology leaders
- Understanding the relationship between CIS and NIST CSF
- Role-based responsibilities in control implementation and validation
- Integrating control requirements into sprint planning cycles
- Using CIS Controls to prioritize technical debt reduction
- Benchmarking current posture against implementation groups
- Common misalignments in cloud-native environments
- Establishing scope for first control implementation phase
Module 2. Inventory and Control of Enterprise Assets
Implement automated, accurate tracking of physical and virtual assets across hybrid environments, including containerized AI workloads.
12 chapters in this module
- Defining asset ownership in distributed data teams
- Automating discovery of AI training environments
- Maintaining secure configuration baselines for ML platforms
- Handling ephemeral compute instances in model training
- Integrating asset inventory with CI/CD pipelines
- Establishing approval workflows for new data environments
- Mapping assets to business criticality tiers
- Using tags to enforce control compliance at scale
- Integrating with existing IBM asset management systems
- Handling shadow AI deployments in research teams
- Validating control effectiveness through automated checks
- Reporting asset compliance to executive leadership
Module 3. Secure Configuration of Enterprise Assets
Enforce hardened configurations across servers, cloud instances, containers, and development environments used in AI workflows.
12 chapters in this module
- Defining secure baselines for AI development environments
- Automating configuration drift detection in Kubernetes clusters
- Applying hardened images to ML pipeline components
- Managing approved software lists for data science teams
- Integrating configuration policies with GitOps workflows
- Enforcing encryption settings across distributed storage
- Securing Jupyter Notebook server configurations
- Validating container image compliance before deployment
- Handling exceptions for research and experimentation
- Monitoring configuration changes in real time
- Documenting compliance for audit evidence packages
- Reducing attack surface through minimal open ports
Module 4. Continuous Vulnerability Management
Establish prioritized, risk-based vulnerability detection and remediation cycles tailored to AI system dependencies.
12 chapters in this module
- Scanning AI model dependencies for known vulnerabilities
- Prioritizing patching based on exploit availability and asset criticality
- Integrating vulnerability data into incident response planning
- Automating vulnerability detection in CI/CD pipelines
- Managing open source risk in ML training frameworks
- Establishing SLAs for vulnerability remediation
- Validating patches without disrupting model training
- Tracking vulnerabilities in third-party AI services
- Generating executive summaries from technical findings
- Integrating with existing security orchestration tools
- Reducing false positives in containerized environments
- Reporting progress to compliance stakeholders
Module 5. Controlled Use of Administrative Privileges
Restrict and monitor elevated access to critical data and AI systems while preserving operational efficiency.
12 chapters in this module
- Defining administrative roles in data platform teams
- Implementing just-in-time access for AI system maintenance
- Monitoring privileged session activity in real time
- Using PAM solutions with AI development workflows
- Enforcing multi-factor authentication for admin accounts
- Auditing access to model training environments
- Managing emergency account procedures
- Integrating privileged access with identity providers
- Reducing standing admin privileges in cloud platforms
- Establishing approval workflows for privilege escalation
- Detecting anomalous administrative behavior
- Reporting compliance to audit committees
Module 6. Maintenance, Monitoring, and Analysis of Audit Logs
Ensure comprehensive logging of security-relevant events across data pipelines and AI systems for threat detection and compliance.
12 chapters in this module
- Defining mandatory log sources for AI infrastructure
- Establishing log retention policies for compliance
- Centralizing logs from distributed training environments
- Detecting anomalies in model access and inference patterns
- Integrating logs with SIEM and SOAR platforms
- Validating log integrity and protection mechanisms
- Creating playbooks for log-based threat hunting
- Meeting audit requirements for log accuracy and completeness
- Handling log volume from large-scale AI operations
- Ensuring GDPR and privacy compliance in logging
- Automating log review for critical control events
- Generating compliance evidence from raw log data
Module 7. Email and Web Browser Protections
Secure communication and browsing channels used by data teams while preserving collaboration and research capabilities.
12 chapters in this module
- Implementing secure browser configurations for data analysts
- Filtering malicious content in data visualization tools
- Protecting against phishing in cloud service portals
- Securing access to public AI model repositories
- Enforcing secure email handling for sensitive data
- Managing browser extensions in development environments
- Integrating email security with incident response
- Educating teams on social engineering risks
- Monitoring for credential exfiltration attempts
- Establishing safe browsing policies for research
- Detecting malicious scripts in web-based IDEs
- Reporting email security metrics to leadership
Module 8. Malware Defenses and Endpoint Protection
Implement layered defenses against malicious code targeting data science and AI engineering workstations.
12 chapters in this module
- Deploying endpoint protection on data science laptops
- Detecting malicious activity in Python environments
- Preventing unauthorized code execution in notebooks
- Integrating EDR with cloud workload protection
- Handling false positives in model training jobs
- Securing model deployment pipelines from tampering
- Monitoring for cryptocurrency mining in clusters
- Establishing clean boot procedures for workstations
- Validating software integrity before execution
- Responding to malware alerts in development environments
- Reporting endpoint compliance to security teams
- Integrating with existing IBM security infrastructure
Module 9. Data Protection and Encryption
Apply consistent encryption and data handling policies across structured and unstructured data assets.
12 chapters in this module
- Classifying sensitive data in AI training sets
- Implementing encryption for data at rest and in transit
- Managing encryption keys for distributed systems
- Protecting model weights and intellectual property
- Securing data sharing between research teams
- Enforcing data retention and deletion policies
- Implementing data masking for development environments
- Complying with cross-border data transfer regulations
- Auditing access to encrypted data repositories
- Integrating with existing data governance tools
- Handling encryption in model inference pipelines
- Documenting data protection compliance
Module 10. Boundary Defense and Network Security
Design and enforce network segmentation and traffic control for AI and data environments.
12 chapters in this module
- Architecting network zones for AI development and production
- Implementing firewall rules for model serving endpoints
- Controlling east-west traffic in containerized environments
- Securing API gateways for AI services
- Monitoring for unusual data exfiltration patterns
- Integrating network security with cloud providers
- Establishing secure connections to external data sources
- Enforcing zero-trust principles in data pipelines
- Detecting lateral movement in hybrid environments
- Validating network segmentation effectiveness
- Responding to network-based security alerts
- Reporting network posture to executive leadership
Module 11. Security Awareness and Skills Training
Develop targeted education programs for data scientists and engineers on secure development practices.
12 chapters in this module
- Assessing current security knowledge in technical teams
- Designing hands-on labs for secure AI development
- Communicating risks in engineering terms
- Creating microlearning modules for busy practitioners
- Measuring training effectiveness through simulations
- Integrating security into onboarding for new hires
- Addressing common misconceptions about AI risks
- Promoting secure coding practices in data pipelines
- Encouraging incident reporting without blame
- Adapting content for different technical roles
- Leveraging internal champions for peer education
- Reporting program impact to compliance teams
Module 12. Implementing and Sustaining CIS Controls
Operationalize control compliance in data and AI environments with minimal overhead.
12 chapters in this module
- Establishing control ownership across teams
- Integrating compliance checks into CI/CD pipelines
- Automating evidence collection for audits
- Reducing manual effort through orchestration
- Aligning control implementation with business cycles
- Managing updates to control specifications
- Conducting internal validation exercises
- Preparing for external compliance assessments
- Building executive dashboards for control posture
- Sustaining compliance through team changes
- Optimizing control implementation over time
- Sharing lessons across the organization
How this maps to your situation
- When first audit review lands on your desk
- Before new AI governance mandate rollout
- After security incident in peer division
- During cloud modernization initiative
Before vs. after
Before
Reactive compliance efforts, fragmented security practices, and limited influence on budget decisions.
After
Proactive control implementation, unified security posture, and leadership in premium engagements.
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 of focused learning, designed for completion on a Sunday morning.
If nothing changes
Continuing without structured control implementation may result in duplicated efforts, audit findings, and missed opportunities to lead high-impact initiatives.
How this compares to the alternatives
Unlike generic compliance training, this course provides actionable, role-specific implementation patterns for data and AI leaders, focusing on leverage, not checklist completion.
Frequently asked
Is this course technical or strategic?
It's designed for technical leaders, blending implementation details with strategic positioning to increase your influence on critical decisions.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help with audits?
Yes, each module includes templates and examples that produce audit-ready evidence while strengthening your team's daily practices.
$199 one-time. 90 minutes of focused learning, designed for completion on a Sunday morning..
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