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Artificial Intelligence Security Toolkit

Artificial Intelligence Security Toolkit

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The Executive Diagnostic and Governance Toolkit

Artificial Intelligence Security Toolkit

Score your own artificial Intelligence Security 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.

$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.
You're accountable for AI security—but you can't point to a clear map of where you stand or why your priorities are what they are.

The situation this is built for

Every day, new AI systems go live without clear ownership of security outcomes. You’re expected to know where risks live, how to rank them, and justify your focus—yet there’s no standard way to assess maturity across data, models, access, and compliance. When leadership asks why you’re fixing one problem over another, you need more than intuition. You need evidence, structure, and a defensible order of operations. Without it, your function appears reactive, not strategic.

Who this is for

The leader who owns artificial intelligence security within their organization. They are responsible for risk assessment, incident planning, compliance readiness, and resource allocation across AI systems. They report to technical or risk leadership and must justify priorities in cross-functional reviews.

Who this is not for

This is not for individual contributors implementing AI models, data scientists tuning algorithms, or vendors selling security tools. It is not for general cybersecurity teams without specific AI system oversight.

What you walk away with

  • Map your organization’s AI security maturity across data, models, and infrastructure
  • Rank risks by impact and urgency using a repeatable framework
  • Defend your priority order in budget and planning meetings
  • Align AI security decisions with regulatory requirements like the right to be forgotten
  • Produce an auditable record of your assessment and roadmap

How this maps to your situation

  • Assessing current state
  • Prioritizing next actions
  • Defending decisions
  • Sustaining improvements

Before vs. after

Before
You're reacting to AI security demands without a clear framework to assess, prioritize, or defend your choices.
After
You lead with a documented, defensible assessment of your AI security posture and a ranked roadmap aligned to risk and business impact.

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 completion over 12 weeks with practical application between modules.

If nothing changes
Without a structured approach, AI security remains reactive and vulnerable to scrutiny. Leaders will question your priorities, compliance gaps may go unnoticed, and incidents will be harder to contain—exposing the organization to financial, legal, and reputational harm.

How this compares to the alternatives

Unlike general cybersecurity courses or vendor-specific training, this program focuses exclusively on the leadership work of AI security: assessment, prioritization, and defense of decisions. It does not teach coding, tool configuration, or product features.

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. Establishing the AI Security Baseline
Define what constitutes AI security in your environment and identify all active systems and data flows.
12 chapters in this module
  1. Identifying all AI systems currently in production
  2. Mapping data sources feeding AI models and pipelines
  3. Documenting ownership and operational responsibility for each system
  4. Assessing model versioning and update frequency
  5. Classifying data types processed by AI systems
  6. Determining where unstructured data resides and how it is accessed
  7. Evaluating access controls for model training environments
  8. Reviewing logging practices for AI inference activity
  9. Inventorying third-party components in AI pipelines
  10. Assessing dependencies on external data providers
  11. Documenting model input and output specifications
  12. Creating a living register of AI assets
Module 2. Assessing Data Integrity and Exposure
Evaluate how securely data is handled throughout the AI lifecycle, from ingestion to output.
12 chapters in this module
  1. Tracing personal data through AI training pipelines
  2. Measuring the volume of sensitive data used in models
  3. Evaluating anonymization effectiveness in training sets
  4. Identifying data leakage points during preprocessing
  5. Assessing data retention policies for model inputs
  6. Determining if synthetic data reduces exposure risk
  7. Auditing data sharing agreements with partners
  8. Evaluating data provenance tracking mechanisms
  9. Measuring data drift and its security implications
  10. Assessing encryption status of data at rest and in transit
  11. Identifying shadow data sources outside governance
  12. Mapping data flow across geographic boundaries
Module 3. Model Behavior and Predictability
Understand how models behave under different conditions and what constitutes a security-relevant deviation.
12 chapters in this module
  1. Defining normal versus anomalous model output
  2. Assessing model confidence thresholds and drift
  3. Evaluating model susceptibility to adversarial inputs
  4. Measuring consistency of outputs across versions
  5. Identifying model bias as a security concern
  6. Assessing model explainability for audit purposes
  7. Evaluating model performance under stress conditions
  8. Determining if model logic can be reverse-engineered
  9. Assessing model sensitivity to input perturbations
  10. Reviewing model retraining triggers and criteria
  11. Measuring output variance over time
  12. Documenting model decision boundaries for review
Module 4. Incident Response Planning for AI Systems
Build a response strategy tailored to AI-specific failure modes and breach scenarios.
12 chapters in this module
  1. Defining what constitutes an AI security incident
  2. Classifying severity levels for model failures
  3. Mapping incident detection points in AI pipelines
  4. Establishing escalation paths for model anomalies
  5. Creating playbooks for data poisoning responses
  6. Developing response steps for model drift detection
  7. Designing rollback procedures for compromised models
  8. Identifying forensic data required after an incident
  9. Assessing notification requirements for affected parties
  10. Integrating AI incidents into existing SOC workflows
  11. Testing incident response with red team exercises
  12. Documenting post-incident model validation steps
Module 5. Compliance in an AI-Driven Environment
Align AI operations with regulatory expectations, including privacy and accountability mandates.
12 chapters in this module
  1. Evaluating GDPR compliance for AI training data
  2. Assessing right to be forgotten implementation gaps
  3. Mapping AI systems to data subject request workflows
  4. Evaluating model auditability for regulatory review
  5. Assessing algorithmic impact on protected groups
  6. Documenting model decisions for explainability audits
  7. Reviewing data minimization in AI pipelines
  8. Assessing cross-border data flow compliance
  9. Evaluating consent mechanisms for data use
  10. Measuring compliance with sector-specific regulations
  11. Preparing for AI-specific regulatory audits
  12. Documenting compliance controls for external assessors
Module 6. Predicting and Prioritizing AI Risks
Develop a method to forecast risk likelihood and impact to guide investment decisions.
12 chapters in this module
  1. Estimating frequency of model degradation events
  2. Quantifying potential damage from data leakage
  3. Assessing probability of adversarial attacks
  4. Measuring impact of model downtime on operations
  5. Predicting exposure from third-party model use
  6. Estimating retraining costs after data breaches
  7. Assessing reputational risk from biased outputs
  8. Measuring compliance penalty exposure
  9. Forecasting incident volume based on system count
  10. Ranking systems by risk surface area
  11. Prioritizing fixes based on business impact
  12. Building a weighted risk scoring model
Module 7. Access and Privilege in AI Systems
Control who can modify, access, or deploy AI models and associated data.
12 chapters in this module
  1. Mapping roles with model deployment authority
  2. Reviewing access logs for model training environments
  3. Evaluating least privilege enforcement for data access
  4. Assessing service account security in pipelines
  5. Identifying overprivileged users in AI workflows
  6. Reviewing model registry access controls
  7. Auditing API key management for inference endpoints
  8. Measuring frequency of privilege escalation requests
  9. Assessing multi-factor authentication coverage
  10. Evaluating break-glass access procedures
  11. Documenting access revocation processes
  12. Tracking third-party vendor access to models
Module 8. Model Lifecycle Security Controls
Secure every phase of the model lifecycle from development to retirement.
12 chapters in this module
  1. Evaluating code review practices for model training
  2. Assessing version control for model artifacts
  3. Reviewing testing coverage for model behavior
  4. Measuring drift detection implementation rate
  5. Assessing model signing and integrity checks
  6. Evaluating rollback capabilities for failed deployments
  7. Reviewing CI/CD pipeline security for AI models
  8. Measuring model documentation completeness
  9. Assessing model deprecation and retirement process
  10. Evaluating monitoring coverage for inference endpoints
  11. Reviewing backup and recovery for model weights
  12. Assessing audit trail coverage across lifecycle
Module 9. Third-Party and Supply Chain Risk
Assess security implications of external models, data, and platforms.
12 chapters in this module
  1. Inventorying third-party AI models in use
  2. Assessing vendor security certifications
  3. Reviewing third-party model audit rights
  4. Evaluating data licensing terms from providers
  5. Measuring transparency of external model behavior
  6. Assessing risk from pre-trained model dependencies
  7. Reviewing contractual liability clauses
  8. Evaluating patching timelines for vendor models
  9. Assessing supply chain provenance for model components
  10. Measuring frequency of third-party security updates
  11. Reviewing exit strategies for vendor-dependent models
  12. Documenting fallback options during service outages
Module 10. Monitoring and Detection Architecture
Design detection systems that catch AI-specific anomalies before they become incidents.
12 chapters in this module
  1. Defining baseline behavior for model inference
  2. Setting thresholds for abnormal output patterns
  3. Measuring coverage of logging across AI components
  4. Assessing real-time monitoring for model drift
  5. Evaluating alerting effectiveness for data shifts
  6. Reviewing correlation between model and data logs
  7. Measuring mean time to detect model anomalies
  8. Assessing integration with existing SIEM tools
  9. Evaluating false positive rates in detection rules
  10. Designing dashboards for AI security posture
  11. Reviewing retention period for security logs
  12. Assessing automated response capabilities
Module 11. Defending Your Priority Order
Justify your risk ranking and investment choices to leadership and budget committees.
12 chapters in this module
  1. Structuring risk presentations for executive review
  2. Translating technical findings into business impact
  3. Building comparative risk heat maps
  4. Documenting assumptions behind risk scores
  5. Creating visualizations for priority trade-offs
  6. Preparing responses to 'why not this?' questions
  7. Aligning AI security priorities with business goals
  8. Demonstrating risk reduction over time
  9. Benchmarking against industry peer expectations
  10. Using historical incident data to justify investment
  11. Linking security efforts to compliance outcomes
  12. Communicating resource needs with clarity
Module 12. Sustaining AI Security Maturity
Turn assessment into ongoing governance with measurable improvement.
12 chapters in this module
  1. Scheduling recurring AI security posture reviews
  2. Assigning ownership for risk remediation tasks
  3. Measuring progress on priority fixes over time
  4. Updating risk models with new system data
  5. Incorporating lessons from incident responses
  6. Reviewing model inventory for obsolescence
  7. Assessing team capability gaps annually
  8. Updating playbooks based on new threats
  9. Measuring leadership satisfaction with reporting
  10. Evaluating automation of assessment steps
  11. Tracking maturity score changes over quarters
  12. Publishing annual AI security transparency report

Frequently asked

Who is this course designed for?
It is designed for leaders who own artificial intelligence security within their organization and must make strategic decisions about risk, resources, and compliance.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this course cover specific tools or platforms?
No. This course focuses on the leadership decisions and artifacts of AI security, not on configuring or selecting technology.
Will I receive a certificate upon completion?
Yes, a certificate of completion is provided after finishing all modules.
Can I access the materials after finishing the course?
Yes, you retain access to all course content and templates indefinitely.
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 hours per module, designed for completion over 12 weeks with practical application between modules..

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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