A tailored course, built for your situation
Mastering CIS Controls for Product Managers in AI Platforms
Build trusted, secure AI agents with structured control implementation that stands up to enterprise scrutiny
$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 rework
The situation this course is for
AI product teams frequently face delays when security review packages lack clear mappings to foundational controls. Without a repeatable method, teams burn cycles reconciling gaps during internal audit cycles, undermining momentum for production deployment.
Who this is for
Product Manager in AI or data platforms working at a large tech or enterprise software firm, responsible for delivering secure, compliant AI features under internal scrutiny
Who this is not for
Engineers focused solely on model tuning, entry-level PMs not owning security review cycles, or practitioners outside AI/software product development
What you walk away with
- Produce consistent, audit-ready security evidence for AI agent deployments
- Reduce time spent on internal security reviews by standardizing control mappings
- Position yourself as the go-to internal resource for secure AI implementation
- Accelerate approval cycles for new AI features by aligning early with security expectations
- Build confidence with security and compliance stakeholders through structured, repeatable deliverables
The 12 modules (with all 144 chapters)
Module 1. Understanding the CIS Controls Framework for AI Products
Lay the foundation by exploring how the CIS Critical Security Controls apply specifically to AI agent development and deployment environments.
12 chapters in this module
- Overview of the CIS Controls and their relevance to AI systems
- Differentiating between foundational and enhanced controls
- Mapping control categories to AI product components
- Identifying high-impact controls for AI agent security
- How CIS compares with NIST CSF and ISO 27001 in AI contexts
- Integrating CIS into product development lifecycle gates
- Role of product management in control ownership
- Common misconceptions about compliance frameworks in AI
- Balancing velocity and security in early-stage AI builds
- Establishing control baselines for MVP deployments
- Working with security teams on control interpretation
- Documenting control alignment for internal audits
Module 2. Control Inventory and Asset Management for AI Agents
Establish clear visibility into AI components and dependencies to satisfy basic control requirements.
12 chapters in this module
- Defining what constitutes a managed asset in an AI agent
- Tracking model versions, APIs, and data pipelines
- Maintaining accurate inventory across deployment environments
- Automating asset discovery in dynamic cloud environments
- Linking assets to ownership and accountability
- Classifying assets by sensitivity and criticality
- Handling ephemeral infrastructure in asset tracking
- Using CMDB integrations for real-time visibility
- Validating inventory completeness during security review
- Avoiding gaps in containerized and serverless deployments
- Documenting exceptions with justification
- Reporting asset compliance status to stakeholders
Module 3. Secure Configuration of AI Development Environments
Ensure developer workspaces and build pipelines adhere to hardened baselines.
12 chapters in this module
- Establishing secure baseline configurations for AI workstations
- Hardening Jupyter notebooks and development IDEs
- Managing secrets and credentials in AI projects
- Enforcing secure coding practices in AI scripts
- Integrating linters and static analysis into CI/CD
- Controlling access to model training environments
- Auditing configuration changes across environments
- Using Infrastructure as Code for consistency
- Managing third-party libraries and dependencies
- Preventing accidental exposure of training data
- Versioning configuration alongside code
- Automating compliance checks in pull requests
Module 4. Account Management and Access Control for AI Teams
Implement least privilege access models across AI development and deployment roles.
12 chapters in this module
- Defining distinct roles for AI developers, MLOps, and product owners
- Implementing role-based access to model repositories
- Managing service accounts for AI pipelines
- Enforcing multi-factor authentication for privileged access
- Regular access review processes for AI platforms
- Handling access provisioning and deprovisioning
- Integrating with enterprise identity providers
- Monitoring for unauthorized account creation
- Segregating duties between development and production
- Managing temporary access escalations
- Auditing access changes for compliance
- Documenting access policies for auditors
Module 5. Vulnerability Management in AI Systems
Proactively identify and remediate security weaknesses in AI components and dependencies.
12 chapters in this module
- Scanning AI models for known vulnerabilities
- Assessing risks in open source ML libraries
- Integrating SCA tools into model development
- Prioritizing vulnerabilities by impact on AI behavior
- Establishing remediation SLAs for critical risks
- Tracking patching progress across environments
- Handling unpatched systems with compensating controls
- Assessing model drift as a potential vulnerability
- Monitoring for adversarial attacks on models
- Reporting vulnerability status to product leadership
- Validating fixes through automated retesting
- Creating dashboards for executive visibility
Module 6. Audit Log Management for AI Agent Activity
Ensure all AI agent actions are recorded, retained, and available for review.
12 chapters in this module
- Defining required log sources for AI agents
- Capturing model inputs, outputs, and decisions
- Ensuring log integrity and tamper resistance
- Centralizing logs from distributed AI components
- Meeting retention requirements for audit purposes
- Configuring alerts for suspicious activity
- Designing log schemas for regulatory needs
- Integrating with SIEM for enterprise monitoring
- Testing log capture under failure conditions
- Validating completeness with control testing
- Responding to audit requests for log data
- Documenting log architecture for reviewers
Module 7. Email and Web Browser Protections for AI Development
Secure common attack vectors used in targeting AI teams and infrastructure.
12 chapters in this module
- Protecting developers from phishing attacks
- Configuring secure browser settings for AI tools
- Blocking malicious domains in development flows
- Managing extensions in data science environments
- Securing communication between AI services
- Implementing DNS filtering for AI platforms
- Detecting and blocking malicious scripts
- Educating teams on social engineering risks
- Enforcing secure email handling practices
- Integrating threat intelligence feeds
- Monitoring for credential exfiltration attempts
- Responding to endpoint compromise incidents
Module 8. Malware Defense in AI Model Pipelines
Prevent and detect malicious code injection across AI development workflows.
12 chapters in this module
- Scanning for malware in training datasets
- Detecting backdoors in pre-trained models
- Validating model integrity before deployment
- Monitoring for cryptomining on training nodes
- Implementing host-based intrusion prevention
- Blocking unauthorized code execution
- Enforcing application whitelisting policies
- Securing model registry access points
- Detecting anomalous behavior in AI workloads
- Responding to confirmed malware incidents
- Conducting post-incident reviews
- Improving defenses based on lessons learned
Module 9. Data Protection Controls for AI Agents
Ensure sensitive data is properly classified, encrypted, and monitored.
12 chapters in this module
- Identifying PII and sensitive data in AI workflows
- Implementing data classification at ingestion
- Encrypting data in transit and at rest
- Masking sensitive outputs from AI agents
- Preventing unauthorized data exfiltration
- Implementing DLP for AI pipelines
- Auditing access to sensitive datasets
- Managing data retention and deletion
- Documenting data flows for compliance
- Validating encryption key management
- Monitoring for anomalous data access
- Reporting data protection status to stakeholders
Module 10. Boundary Defense for AI Infrastructure
Secure network perimeters and micro-segmentation for AI deployments.
12 chapters in this module
- Designing secure network architectures for AI agents
- Implementing firewalls between AI components
- Using micro-segmentation in Kubernetes clusters
- Controlling east-west traffic in AI environments
- Managing API gateways for agent communication
- Enforcing service-to-service authentication
- Monitoring for unauthorized connections
- Responding to perimeter breaches
- Validating network segmentation effectiveness
- Integrating with cloud security posture tools
- Documenting network design for auditors
- Optimizing performance without sacrificing security
Module 11. Incident Response Planning for AI Systems
Prepare for security incidents involving AI agents with clear playbooks.
12 chapters in this module
- Defining incident types specific to AI agents
- Establishing detection capabilities for AI anomalies
- Creating response playbooks for model compromise
- Designating incident response roles
- Integrating AI incidents into broader SOC processes
- Preserving forensic evidence from AI systems
- Communicating during AI-related incidents
- Conducting post-incident analysis
- Updating controls based on lessons learned
- Testing response plans with tabletop exercises
- Reporting incident metrics to leadership
- Maintaining regulator-ready documentation
Module 12. Integration and Automation of CIS Controls in AI Product Management
Embed security control implementation into product management workflows.
12 chapters in this module
- Integrating control requirements into user stories
- Automating evidence collection in sprint cycles
- Creating reusable templates for security reviews
- Tracking control status in product backlogs
- Collaborating with security teams on control gaps
- Reporting control maturity to executives
- Building stakeholder trust through transparency
- Optimizing for speed without sacrificing compliance
- Scaling secure practices across multiple AI products
- Developing internal training for new hires
- Creating feedback loops from audits to product
- Positioning yourself as the go-to expert on AI security
How this maps to your situation
- Security review packages requiring rework
- Internal audit cycles for AI deployments
- Cross-functional alignment between product and security
- Executive expectations for trustworthy AI
Before vs. after
Before
Spending weeks reconciling control gaps during security reviews with no standardized approach to evidence preparation.
After
Producing audit-ready packages in hours by following a repeatable control implementation method tailored to AI products.
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 90 minutes per week over six weeks, with most practitioners completing the course in under two months.
If nothing changes
Without a structured approach to security controls, AI products face delays in deployment, increased friction with security teams, and potential reputational risk if gaps are found post-launch.
How this compares to the alternatives
Unlike generic compliance training or high-level strategy courses, this program delivers actionable, role-specific methods for implementing CIS Controls directly within AI product workflows , turning abstract standards into repeatable, evidence-producing practices.
Frequently asked
Is this course focused on technical implementation or product leadership?
It's designed for product leaders who need to deliver secure AI features. It bridges technical controls and product execution without requiring deep engineering skills.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Can I apply this to non-CIS frameworks?
Yes. The methods are transferable to NIST CSF, ISO 27001, and other standards , the course uses CIS as the anchor but teaches adaptable implementation patterns.
$199 one-time. Approximately 90 minutes per week over six weeks, with most practitioners completing the course in under two months..
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