Skip to main content
Image coming soon

GEN4109 Engineering Trusted AI Systems Within Regulated IT Environments

$199.00
Adding to cart… The item has been added

What is the Engineering Trusted AI Systems Within course about?

A step-by-step implementation guide for CISOs leading AI integration in high-compliance environments 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.

Who is the Engineering Trusted AI Systems Within course for?

Chief Information Security Officer in a regulated or highly integrated technology environment, responsible for sign-off on novel systems with compliance exposure.

What do you take away from the Engineering Trusted AI Systems Within course?

Command of OWASP AI Application Security Verification Standard (ASVS) in practice Ability to pre-validate AI system design against compliance-ready benchmarks Reduction in pre-audit rework cycles for AI-related controls Clear lineage from development decisions to runtime assurance artefacts Implementation-grade documentation that withstands regulator and internal review.

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.

What does the Engineering Trusted AI Systems Within cover on delivery and format?

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, designed for completion on weekends or off-hours.

How does this compare to the alternatives?

Unlike generic AI ethics courses or high-level compliance overviews, this program delivers implementation-grade detail on OWASP standards specifically for securing AI systems in regulated IT environments.

What does the Engineering Trusted AI Systems Within cover on frequently asked?

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.

How is the Engineering Trusted AI Systems Within delivered?

The Engineering Trusted AI Systems Within is fully self-paced with immediate online access after enrolment. Access does not expire and future updates are included at no cost. A certificate of completion is issued by The Art of Service when you finish.

Closely related courses: Zero Trust Network Architecture within distributed, Engineering AI Governance and Zero Trust Within Regulated.

More answers: what you get with every course, refund policy, all help answers.

A tailored course, built for your situation

Engineering Trusted AI Systems Within Regulated IT Environments

A step-by-step implementation guide for CISOs leading AI integration in high-compliance environments

$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.

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.
Control documentation that keeps needing rework during audit cycles

The situation this course is for

AI components enter the environment without clear validation lineage, forcing last-minute evidence assembly under stakeholder review.

Who this is for

Chief Information Security Officer in a regulated or highly integrated technology environment, responsible for sign-off on novel systems with compliance exposure

Who this is not for

Engineers looking for introductory AI training or teams still evaluating whether to adopt AI in production

What you walk away with

  • Command of OWASP AI Application Security Verification Standard (ASVS) in practice
  • Ability to pre-validate AI system design against compliance-ready benchmarks
  • Reduction in pre-audit rework cycles for AI-related controls
  • Clear lineage from development decisions to runtime assurance artefacts
  • Implementation-grade documentation that withstands regulator and internal review

The 12 modules (with all 144 chapters)

Module 1. Foundations of Trusted AI in Regulated Environments
Establish the core principles of secure, auditable AI system design within compliance-bound IT landscapes.
12 chapters in this module
  1. Defining 'trusted' beyond marketing: technical, operational, and regulatory dimensions
  2. Mapping common regulatory touchpoints for AI in financial, healthcare, and critical infrastructure
  3. The role of the CISO in bridging innovation velocity and control integrity
  4. Key differences between traditional software assurance and AI system validation
  5. Integrating AI risk posture into existing GRC workflows
  6. Understanding the attack surface expansion from model inference and data feedback loops
  7. Establishing baseline expectations for transparency and explainability
  8. Common failure modes in early AI deployments and how to avoid them
  9. Regulator expectations vs. engineering reality in AI assurance
  10. Building cross-functional alignment between security, data science, and IT operations
  11. Leveraging existing control frameworks as scaffolding for AI-specific requirements
  12. Creating a living assurance roadmap for iterative AI system improvement
Module 2. OWASP AI Security and Privacy Guide Overview
Deep dive into the structure, intent, and practical application of the OWASP AI Security and Privacy Guide.
12 chapters in this module
  1. Origins and evolution of the OWASP AI Security and Privacy Guide
  2. Core objectives: where the guide fills gaps left by general security standards
  3. Navigating the three main sections: threats, controls, and implementation guidance
  4. Understanding the threat taxonomy specific to machine learning systems
  5. Mapping OWASP AI risks to NIST CSF and other enterprise frameworks
  6. How privacy concerns are treated differently in AI vs. conventional applications
  7. Role of red teaming and adversarial testing in validating AI defenses
  8. Using the guide to benchmark internal AI development practices
  9. Integrating OWASP AI guidance into vendor assessment questionnaires
  10. Adapting the guide for domain-specific use cases like fraud detection or clinical decision support
  11. Version control and change tracking for AI-specific security policies
  12. Establishing ownership for maintaining OWASP alignment across the AI lifecycle
Module 3. Threat Modeling for AI Systems
Apply structured threat modeling techniques tailored to AI architectures and data pipelines.
12 chapters in this module
  1. Extending STRIDE to cover model inversion, data poisoning, and prompt injection
  2. Identifying high-risk components in end-to-end AI workflows
  3. Creating data flow diagrams that include training, serving, and feedback loops
  4. Classifying assets unique to AI systems: models, weights, embeddings, and feature stores
  5. Assessing trust boundaries between human operators, automated systems, and external APIs
  6. Model card analysis as part of initial threat assessment
  7. Detecting over-reliance on opaque third-party models or black-box services
  8. Evaluating supply chain risks in pre-trained models and foundation systems
  9. Documenting assumptions about data quality, representativeness, and drift
  10. Incorporating bias and fairness considerations into threat profiles
  11. Linking threat model outputs to specific control requirements
  12. Maintaining living threat models through model version updates
Module 4. Secure Design Patterns for AI Applications
Implement architecture-level controls that bake security into AI system design.
12 chapters in this module
  1. Designing for observability: logging model inputs, outputs, and confidence scores
  2. Enforcing least privilege access to model endpoints and training infrastructure
  3. Segregating development, staging, and production environments for AI workloads
  4. Implementing secure model storage and retrieval mechanisms
  5. Hardening inference servers against prompt manipulation and denial-of-service
  6. Architecting fallback modes for failed or degraded model performance
  7. Protecting sensitive training data through encryption and access governance
  8. Designing human-in-the-loop controls for high-stakes decisions
  9. Mitigating model stealing through rate limiting and obfuscation
  10. Building audit trails for model updates and configuration changes
  11. Securing API gateways used for model serving
  12. Validating input sanitization strategies for unstructured data feeds
Module 5. Data Integrity and Provenance Controls
Ensure data used in AI systems maintains integrity, traceability, and appropriate handling.
12 chapters in this module
  1. Establishing data lineage from source to model training and inference
  2. Implementing cryptographic hashing for dataset version verification
  3. Controlling access to raw, processed, and labeled datasets
  4. Detecting and responding to data poisoning attempts
  5. Validating data representativeness and identifying selection bias
  6. Handling personally identifiable information in training sets
  7. Ensuring compliance with data retention and deletion policies
  8. Auditing data transformation pipelines for unauthorized modifications
  9. Monitoring for concept and data drift in production environments
  10. Creating immutable logs for data access and modification events
  11. Using synthetic data responsibly while preserving statistical fidelity
  12. Verifying third-party data providers meet security and quality standards
Module 6. Model Development and Training Security
Secure the model creation process from code to trained artifact.
12 chapters in this module
  1. Securing development environments used for model experimentation
  2. Managing credentials and secrets in ML pipelines
  3. Version controlling model code, hyperparameters, and dependencies
  4. Isolating training jobs to prevent resource contention or data leakage
  5. Validating container images and base environments for known vulnerabilities
  6. Signing and verifying trained models before promotion
  7. Protecting model weights and architecture files from exfiltration
  8. Implementing peer review processes for model design choices
  9. Testing for backdoors and malicious triggers during training
  10. Documenting model assumptions and limitations in technical specifications
  11. Enforcing reproducibility through deterministic training configurations
  12. Integrating static analysis tools into ML CI/CD pipelines
Module 7. Runtime Protection and Monitoring
Deploy continuous safeguards and monitoring for AI systems in production.
12 chapters in this module
  1. Implementing real-time anomaly detection on model inputs and outputs
  2. Setting thresholds for confidence score degradation
  3. Monitoring for prompt injection and adversarial input patterns
  4. Logging all inference requests with full context for forensic review
  5. Detecting model drift through statistical performance metrics
  6. Automating alerts for unexpected behavior or service degradation
  7. Rate limiting and quota enforcement for API-based model access
  8. Sandboxing high-risk model interactions
  9. Integrating AI monitoring into existing SIEM workflows
  10. Performing regular integrity checks on deployed model artifacts
  11. Using canary testing to validate updates before full rollout
  12. Maintaining zero-trust posture for inter-service communication
Module 8. Explainability and Interpretability Techniques
Generate meaningful explanations for AI decisions to support audit and oversight.
12 chapters in this module
  1. Selecting appropriate explanation methods based on model type and use case
  2. Implementing LIME and SHAP for local interpretability
  3. Using attention mechanisms to highlight influential input features
  4. Generating natural language summaries of model reasoning
  5. Validating explanation fidelity against ground truth outcomes
  6. Balancing explanation accuracy with performance overhead
  7. Tailoring explanations for different audiences: developers, auditors, end users
  8. Documenting model limitations and edge cases in plain language
  9. Creating standardized report templates for model behavior analysis
  10. Integrating explainability into user-facing interfaces
  11. Testing explanations under adversarial conditions
  12. Archiving explanation outputs for audit trail completeness
Module 9. Validation and Testing Methodologies
Execute rigorous testing protocols to verify AI system safety and reliability.
12 chapters in this module
  1. Developing test suites for functional correctness and edge cases
  2. Creating adversarial test datasets to probe model robustness
  3. Simulating data drift and concept shift scenarios
  4. Testing for fairness across protected attributes
  5. Benchmarking performance against established baselines
  6. Validating model behavior under low-data or high-noise conditions
  7. Conducting red team exercises focused on AI-specific attack vectors
  8. Measuring resilience to prompt injection and jailbreaking attempts
  9. Using metamorphic testing to verify logical consistency
  10. Automating regression tests for model updates
  11. Documenting test coverage and gap analysis
  12. Preparing test evidence packages for internal and external reviewers
Module 10. Compliance Integration and Audit Readiness
Align AI system controls with regulatory expectations and prepare for scrutiny.
12 chapters in this module
  1. Mapping OWASP AI controls to SOC 2, NIST CSF, and other relevant standards
  2. Preparing documentation packages for internal and external audits
  3. Responding to auditor inquiries about AI-specific risks
  4. Demonstrating due diligence in AI system design and operation
  5. Updating risk assessments to reflect AI adoption
  6. Integrating AI controls into existing policy frameworks
  7. Creating standardized narratives for regulator-facing communications
  8. Conducting mock audits to identify evidence gaps
  9. Maintaining up-to-date model inventories and dependency lists
  10. Showing continuous improvement through incident response and lessons learned
  11. Training staff on compliance expectations for AI-managed processes
  12. Archiving artefacts to meet statutory retention requirements
Module 11. Incident Response for AI Systems
Plan and execute effective responses to AI-related security incidents.
12 chapters in this module
  1. Defining what constitutes an AI security incident
  2. Establishing detection capabilities for anomalous model behavior
  3. Activating response teams with AI-specific expertise
  4. Containing compromised models or poisoned datasets
  5. Preserving evidence for root cause analysis
  6. Communicating with stakeholders during AI outages
  7. Rolling back to known-good model versions
  8. Analyzing incident data to improve future resilience
  9. Updating training data to prevent recurrence
  10. Reporting incidents to regulators when required
  11. Conducting post-mortems with cross-functional participation
  12. Incorporating findings into control enhancements and staff training
Module 12. Governance and Continuous Improvement
Establish sustainable oversight and refinement of AI security practices.
12 chapters in this module
  1. Setting up AI governance committees with executive sponsorship
  2. Defining roles and responsibilities for AI system ownership
  3. Creating escalation paths for high-risk decisions
  4. Reviewing model performance and risk posture on a regular schedule
  5. Updating policies to reflect emerging threats and technologies
  6. Benchmarking against industry peers and best practices
  7. Investing in staff training and skill development
  8. Tracking key risk indicators for early warning signals
  9. Publishing transparency reports on AI system usage
  10. Engaging with external experts and advisory boards
  11. Planning for model sunsetting and retirement
  12. Embedding lessons from audits and incidents into ongoing improvement

How this maps to your situation

  • Pre-deployment validation
  • Runtime assurance
  • Audit evidence packaging
  • Cross-team coordination

Before vs. after

Before
Spending weeks assembling fragmented evidence for AI system reviews, reacting to auditor questions without ready references, and managing rework due to unclear validation standards.
After
Producing complete, consistent, and defensible assurance packages in hours, with clear lineage from design to runtime behavior, grounded in OWASP implementation standards.

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, designed for completion on weekends or off-hours.

If nothing changes
Without structured implementation guidance, AI initiatives risk delayed deployment, repeated audit findings, and erosion of stakeholder trust due to inconsistent validation practices.

How this compares to the alternatives

Unlike generic AI ethics courses or high-level compliance overviews, this program delivers implementation-grade detail on OWASP standards specifically for securing AI systems in regulated IT environments.

Frequently asked

How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this course technical enough for hands-on practitioners?
Yes, every module includes implementation checklists, sample documentation, and concrete patterns applicable to real-world AI deployments.
Does the course address recent OWASP AI standard updates?
Yes, the content reflects the latest published guidance and anticipated revisions based on current working drafts.
$199 one-time. Approximately 90 minutes per week over six weeks, designed for completion on weekends or off-hours..

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
!