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GEN9708 Mastering AI Agent Governance for Automation Leaders

$200.00
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The Executive Diagnostic and Governance Toolkit

Mastering AI Agent Governance for Automation Leaders

Score your own function red, amber or green, find out which part is weakest, and walk into the next budget round able to defend what you want to fix. Built for leaders reviewing AI agents and workflow automation.

$199 one-time
30-day money-back guarantee Verified against latest insights, updated access provided within 24h

Each order is checked and updated against the latest insights before delivery. That is why access takes up to 24 hours rather than being instant.

What you walk out with
A scored, ranked picture of your own function, and a defensible answer to what to fix first.
1 You stop guessing where you stand.
You finish with a score, not an opinion: every part of your function rated red, amber or green, with the weakest ranked first. Evidence: a Quick Scan for the shape of it, then seven domain assessments of 30 scored questions each, 210 in all, rolled into one scorecard, plus a maturity radar and a current-versus-target gap analysis.
2 You can defend the decision.
You walk into the budget round with the gap named, the owner named and done defined, instead of a case built on instinct. Evidence: project charter, scope statement, RACI, requirements traceability and work breakdown structure, pre-filled in your domain's language.
3 The work actually moves.
The month after the decision is already built, so nothing stalls waiting for someone to design a form. Evidence: more than 60 project templates across all five PMBOK process groups, plus runbooks, SOPs, a KPI framework, audit checklists and a risk matrix. 55 to 65 files in total.
4 You use it the day it lands.
No blank templates to interpret. Every workbook opens with what it is, who uses it, when, how, a 1 to 5 scoring guide, what good looks like, and a worked example you delete and type over.
The Quick Scan is one sitting. You will know your weakest area before the day is out.
Nothing in it is generic project management: the build rejects any file that could belong to another course. Updated after you enrol, so it reflects where the work stands now. The 144-chapter course is included behind it, for the parts you want to go deeper on.
AI agents are already operating across your applications — but no one has full visibility or clear ownership.

The situation this is built for

Autonomous agents are executing tasks, moving data, and triggering actions outside traditional workflow boundaries. Your team launched them to accelerate operations, but now you can't track which agents have access to sensitive systems, who approved their permissions, or how they interact with zero-trust policies. Security flags unapproved data transfers. Compliance teams demand audit trails. Executives ask if these agents introduce uncontrollable risk. You need a way to assess what exists, standardize controls, and prove governance — without halting progress.

Who this is for

Head of Automation in mid-to-large enterprises, responsible for overseeing intelligent process automation, digital workforce integration, and cross-system orchestration involving both human and machine actors.

Who this is not for

Individual contributors focused only on building bots, developers working in isolated sandboxes, or leaders interested in theoretical AI ethics without operational impact.

What you walk away with

  • Complete inventory of all active AI agents across business units
  • Standardized ownership model for agent lifecycle accountability
  • Access boundary framework aligned with zero-trust network principles
  • Audit-ready documentation package for compliance and security reviews
  • Executive briefing template to align leadership on agentic risk posture

How this maps to your situation

  • You don’t know how many agents exist today
  • Ownership is unclear when agents go wrong
  • Security cannot inspect agent-to-app data flows
  • Compliance demands proof you’re not exposed

Before vs. after

Before
Fragmented oversight, invisible agent populations, reactive responses to breaches, misaligned stakeholder expectations.
After
Unified governance model, complete agent visibility, proactive risk management, and executive confidence in automation integrity.

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 3–4 hours per module, designed to be completed over 12 weeks with one module per week, or accelerated based on team availability.

If nothing changes
Without structured governance, AI agents will continue to operate beyond control boundaries, increasing the likelihood of undetected data exfiltration, regulatory penalties, and systemic failures that erode trust in automation programs.

How this compares to the alternatives

Unlike vendor-specific certifications or academic AI courses, this program focuses exclusively on operational governance practices for deployed agents, providing actionable frameworks rather than conceptual overviews or product training.

Also included: the full course, for when you want the reasoning behind a finding (12 modules, 144 chapters)

Depth reference. The diagnostic and the templates stand on their own; this is what to read when you want the reasoning behind a finding.

Module 1. Understanding the Modern Agentic Landscape
Define what constitutes an AI agent in your enterprise context and distinguish between types based on autonomy, scope, and integration depth.
12 chapters in this module
  1. Defining AI agents beyond robotic process automation
  2. Classifying agents by decision authority and action range
  3. Mapping agent types to business function domains
  4. Identifying embedded agents within SaaS applications
  5. Distinguishing supervised from autonomous execution paths
  6. Assessing agent interaction patterns with APIs and UIs
  7. Recognizing agent proliferation through low-code platforms
  8. Evaluating legacy automation debt versus new agent builds
  9. Documenting agent creation triggers and use case drivers
  10. Inventorying common agent development toolchains in use
  11. Tracking agent deployment frequency across departments
  12. Establishing baseline terminology for cross-functional alignment
Module 2. Agent Visibility and Discovery Techniques
Implement systematic methods to detect and catalog all AI agents currently operating across your digital estate.
12 chapters in this module
  1. Designing network traffic queries to surface agent behavior
  2. Using identity logs to trace non-human account activity
  3. Configuring SIEM rules for anomalous bot-to-app sequences
  4. Leveraging endpoint telemetry for local agent detection
  5. Auditing service accounts tied to automation frameworks
  6. Reviewing CI/CD pipelines for agent deployment artifacts
  7. Scanning browser automation extensions company-wide
  8. Interpreting cloud workload identities as agent proxies
  9. Cross-referencing IAM roles with known automation tools
  10. Conducting departmental self-reporting campaigns safely
  11. Validating discovered agents against application whitelists
  12. Building a centralized agent registry schema
Module 3. Ownership Models for Autonomous Systems
Assign clear accountability for each agent's lifecycle, including maintenance, updates, and decommissioning.
12 chapters in this module
  1. Creating RACI matrices for agent development teams
  2. Defining owner versus operator responsibilities clearly
  3. Linking agent ownership to existing ITIL service records
  4. Setting escalation paths for agent malfunction events
  5. Establishing SLAs for agent performance and reliability
  6. Requiring owners to file annual agent health attestations
  7. Integrating agent ownership into capital planning cycles
  8. Enforcing owner sign-off on permission change requests
  9. Mapping agent dependencies to business process owners
  10. Designing agent retirement workflows with legal input
  11. Auditing ownership assignments quarterly for accuracy
  12. Publishing ownership directories to security stakeholders
Module 4. Access Control and Permission Boundaries
Apply least privilege and just-in-time access principles to AI agents interacting with enterprise systems.
12 chapters in this module
  1. Applying zero-trust access models to non-human identities
  2. Segmenting agent access using micro-perimeter policies
  3. Implementing time-bound credentials for task-limited agents
  4. Restricting clipboard and download functions in browsers
  5. Blocking unauthorized screen capture by automation tools
  6. Enforcing MFA equivalency for agent authentication flows
  7. Monitoring privilege creep in long-lived service accounts
  8. Automating credential rotation for agent integrations
  9. Creating sandboxed environments for testing new agents
  10. Integrating DLP policies into agent runtime execution
  11. Detecting and alerting on lateral movement attempts
  12. Logging all elevated permission usage by agents
Module 5. Risk Assessment Framework Development
Build a repeatable scoring system to evaluate the risk level of each AI agent based on data exposure and action impact.
12 chapters in this module
  1. Defining risk dimensions for data sensitivity and reach
  2. Scoring agents by potential blast radius of failure
  3. Categorizing agents based on PII handling requirements
  4. Assessing financial transaction authority levels per agent
  5. Evaluating third-party data sharing via agent channels
  6. Rating agents on ability to trigger irreversible actions
  7. Incorporating supply chain exposure into risk scores
  8. Factoring in training data provenance and lineage
  9. Weighting risk based on system criticality rankings
  10. Adjusting scores dynamically based on threat intel
  11. Benchmarking agent risk against industry peer norms
  12. Reporting aggregate risk posture to executive leadership
Module 6. Compliance and Audit Trail Engineering
Ensure every AI agent generates tamper-evident logs sufficient for regulatory scrutiny and forensic investigation.
12 chapters in this module
  1. Designing immutable logging pipelines for agent output
  2. Capturing full session recordings for high-risk agents
  3. Including contextual metadata in all agent event logs
  4. Aligning log retention periods with compliance mandates
  5. Verifying log integrity using cryptographic hashing
  6. Ensuring logs survive agent decommissioning events
  7. Mapping agent activities to GDPR right-to-explanation
  8. Supporting SOX controls with agent transaction trails
  9. Preparing audit packages for external reviewers
  10. Testing log retrieval speed under investigation loads
  11. Redacting sensitive payloads while preserving context
  12. Integrating agent logs into central GRC platforms
Module 7. Integration with Zero Trust Network Access
Embed agent traffic into ZTNA policies so automated workflows follow the same verification rules as humans.
12 chapters in this module
  1. Onboarding non-human identities to ZTNA enrollment
  2. Enforcing device posture checks for agent hosts
  3. Validating agent certificates within trust brokers
  4. Applying user-like session timeouts to agent connections
  5. Requiring re-authentication after policy changes
  6. Inspecting encrypted agent traffic without decryption
  7. Blocking agent access during conditional access denials
  8. Extending continuous authentication to agent sessions
  9. Mapping agent flows to zero-trust segmentation gates
  10. Integrating agent identity into dynamic policy engines
  11. Testing fail-closed behaviors for agent connectivity
  12. Measuring ZTNA coverage across agent population
Module 8. Monitoring and Alerting Strategy Design
Develop targeted detection logic to identify suspicious or out-of-bounds agent behavior in real time.
12 chapters in this module
  1. Creating baselines for normal agent request volume
  2. Flagging deviations in agent execution timing patterns
  3. Detecting unexpected geolocation jumps by agents
  4. Alerting on repeated failed access attempts by bots
  5. Correlating agent actions with concurrent human sessions
  6. Identifying bulk data export operations by agents
  7. Monitoring for unusual API call combinations
  8. Setting thresholds for rate-limited resource consumption
  9. Generating alerts for privilege escalation sequences
  10. Triggering incident tickets for policy violation clusters
  11. Suppressing noise from expected batch processing jobs
  12. Prioritizing alerts based on asset criticality tags
Module 9. Incident Response Planning for Agents
Prepare playbooks for responding to compromised, rogue, or malfunctioning AI agents.
12 chapters in this module
  1. Classifying agent incidents by severity and urgency
  2. Defining containment procedures for active agent threats
  3. Isolating infected host machines running rogue agents
  4. Revoking credentials associated with compromised agents
  5. Preserving state snapshots for post-incident analysis
  6. Notifying affected parties about agent data exposures
  7. Executing rollback plans for erroneous agent actions
  8. Engaging legal counsel on agent-generated liabilities
  9. Coordinating communication across IR, SecOps, and IT
  10. Documenting root cause findings for future prevention
  11. Updating detection rules based on incident learnings
  12. Running tabletop exercises for agent breach scenarios
Module 10. Policy Development and Enforcement
Create enforceable standards that guide acceptable agent design, deployment, and operation across the organization.
12 chapters in this module
  1. Drafting enterprise-wide AI agent acceptable use policy
  2. Mandating pre-deployment security review checkpoints
  3. Requiring threat modeling for all new agent projects
  4. Establishing code signing requirements for agent binaries
  5. Prohibiting hardcoded secrets in agent configuration files
  6. Enforcing encryption of agent-stored state data
  7. Banning unauthorized peer-to-peer agent communication
  8. Limiting agent access to only documented APIs
  9. Setting standards for error handling and retry logic
  10. Requiring fallback mechanisms for failed agent tasks
  11. Auditing policy compliance during quarterly reviews
  12. Imposing consequences for policy violations systematically
Module 11. Stakeholder Alignment and Communication
Facilitate productive conversations between automation, security, compliance, and business leaders about agentic risk.
12 chapters in this module
  1. Translating technical agent risks into business terms
  2. Presenting risk heat maps to executive decision makers
  3. Aligning agent governance goals with board priorities
  4. Facilitating cross-functional workshops on agent ethics
  5. Negotiating trade-offs between speed and safety
  6. Reporting key metrics to CISO and CIO offices
  7. Educating auditors on differences between bots and users
  8. Managing expectations around full observability limits
  9. Securing budget for agent governance tooling upgrades
  10. Building consensus on acceptable autonomy thresholds
  11. Sharing incident summaries without revealing vulnerabilities
  12. Creating transparency reports for internal trust building
Module 12. Scaling Governance Across the Enterprise
Evolve from ad hoc controls to a sustainable, organization-wide AI agent governance program.
12 chapters in this module
  1. Designing a center of excellence for agentic operations
  2. Integrating agent governance into software development life cycle
  3. Onboarding new business units using phased enablement
  4. Training developers on secure agent coding practices
  5. Certifying third-party vendors’ agent security posture
  6. Standardizing agent monitoring across hybrid environments
  7. Automating compliance checks for faster audits
  8. Developing maturity model for progressive improvement
  9. Benchmarking performance against industry frameworks
  10. Iterating governance policies based on feedback loops
  11. Planning capacity for next-generation agent capabilities
  12. Sustaining momentum through regular governance forums

Frequently asked

Is this course about building AI agents or governing them?
This course is strictly about governance — assessing, controlling, and auditing AI agents already in operation or planned for deployment.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does the course include tools or software?
No. It provides methodological frameworks, assessment templates, and implementation guidance you apply using your existing tech stack.
Can I share the course materials with my team?
Each purchase grants individual access, but team licensing is available upon request.
Will this help me justify budget for agent governance tools?
Yes. The course includes templates for executive briefings and risk reporting that support funding proposals.
What formats do the templates come in?
The implementation playbook downloads as PDF and editable XLSX. The course reads in your learning environment and exports to PDF for offline use. The files are yours to keep.
Can I share this with my team?
The licence is per person. Team pricing opens from three seats: reply to the order confirmation with TEAM and we will set it up.
How quickly can I start?
The diagnostic is one sitting and the templates work straight out of the kit. Account access takes up to 24 hours rather than being instant, because every order is checked and updated against the latest sources before it is delivered.
$199 one-time. Approximately 3–4 hours per module, designed to be completed over 12 weeks with one module per week, or accelerated based on team availability..

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·Know your weakest area today·210 scored questions·Course included· Account access within 24 hours
30-day money-back guarantee, no questions asked.
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