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GEN7674 Certified AI Penetration Testing Methodology for Technology Leaders

$199.00
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A tailored course, built for your situation

Certified AI Penetration Testing Methodology for Technology Leaders

Deliver auditable, repeatable AI security assessments with confidence

$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.
AI audit packages rejected or delayed due to inconsistent test design or missing certification benchmarks

The situation this course is for

Technical teams invest significant time in AI security assessments, only to face rework when deliverables don’t meet procurement, regulatory, or client-specific certification thresholds. Without a standardized, recognized methodology, even well-executed tests fail to gain traction in high-stakes review cycles.

Who this is for

Technology and advisory professionals delivering AI governance, security validation, or compliance services, especially those under pressure to produce credible, repeatable AI risk assessments for external scrutiny.

Who this is not for

This course is not for entry-level security analysts or researchers focused on theoretical AI safety. It’s designed for practitioners delivering client-facing or audit-ready AI penetration test outcomes.

What you walk away with

  • Produce AI penetration test reports that meet formal certification benchmarks
  • Reduce report rework by using validated templates and methodology
  • Position yourself to lead high-margin AI security engagements
  • Deliver assessments that align with emerging regulatory and procurement demands
  • Differentiate your offerings with a recognized, implementable methodology

The 12 modules (with all 144 chapters)

Module 1. Foundations of AI Penetration Testing
Establish the core principles, scope, and boundaries of offensive testing in AI systems.
12 chapters in this module
  1. Defining AI penetration testing versus traditional security assessments
  2. Understanding the attack surface of machine learning models and pipelines
  3. Key differences between AI red teaming and software vulnerability scanning
  4. Regulatory signals driving demand for AI offensive testing
  5. Mapping AI pentest objectives to business risk and client requirements
  6. Overview of certification standards influencing methodology design
  7. Common misconceptions about AI model exploitability
  8. The role of interpretability in planning adversarial tests
  9. Establishing test legitimacy and ethical boundaries
  10. Integrating AI pentesting into broader security assurance programs
  11. Benchmarking readiness for AI offensive testing engagement
  12. Aligning stakeholder expectations with technical feasibility
Module 2. Scoping AI Systems for Security Testing
Learn how to define precise, defensible boundaries for AI penetration tests.
12 chapters in this module
  1. Identifying components of an AI system eligible for penetration testing
  2. Classifying model types and their associated risk profiles
  3. Data pipeline mapping for adversarial input testing
  4. Determining system autonomy level and its impact on test scope
  5. Handling third-party model integrations and API dependencies
  6. Scoping considerations for open-weight versus proprietary models
  7. Defining success criteria for model manipulation and inference attacks
  8. Documenting scope exclusions with justification
  9. Managing client expectations around black-box versus white-box access
  10. Versioning and reproducibility requirements for test environments
  11. Legal and contractual constraints on AI system testing
  12. Preparing scoping documentation for client and auditor review
Module 3. Threat Modeling for AI Workloads
Apply structured threat modeling to anticipate AI-specific attack vectors.
12 chapters in this module
  1. Adapting STRIDE and other frameworks for AI system threats
  2. Identifying high-risk AI behaviors: hallucination, manipulation, bias exploitation
  3. Model inversion and membership inference as core threats
  4. Prompt injection and adversarial prompt engineering scenarios
  5. Data poisoning pathways and training data integrity risks
  6. Supply chain threats in pre-trained models and libraries
  7. Model stealing and unauthorized replication vectors
  8. Privilege escalation within AI agent systems
  9. Mapping threats to MITRE ATLAS and other emerging taxonomies
  10. Prioritizing threats by exploitability and business impact
  11. Integrating threat model outputs into test planning
  12. Documenting threat model assumptions and limitations
Module 4. AI Adversarial Input Generation
Master techniques for crafting inputs that expose model vulnerabilities.
12 chapters in this module
  1. Understanding gradient-based and gradient-free adversarial attacks
  2. Generating perturbed inputs for image and text models
  3. Prompt crafting strategies for large language model manipulation
  4. Using semantic equivalence to evade detection mechanisms
  5. Creating jailbreak prompts with obfuscation and role-playing
  6. Testing model sensitivity to punctuation, formatting, and encoding
  7. Automating adversarial input generation with Python libraries
  8. Validating adversarial examples for realism and practicality
  9. Evaluating model confidence shifts under adversarial conditions
  10. Measuring success: classification flip versus goal completion
  11. Documenting input attack methodology for replication
  12. Reporting adversarial input findings without enabling misuse
Module 5. Model Integrity and Behavior Testing
Evaluate AI model consistency, reliability, and resistance to manipulation.
12 chapters in this module
  1. Designing test cases for model robustness under edge conditions
  2. Assessing model drift and degradation over time
  3. Testing for unintended functionality and hidden capabilities
  4. Validating model adherence to safety guardrails and filters
  5. Measuring output consistency across repeated queries
  6. Checking for memorization of training data
  7. Evaluating model fairness and bias under adversarial conditions
  8. Testing multi-modal models for cross-modal exploits
  9. Assessing autonomous agent decision-making under stress
  10. Benchmarking model performance before and after attack
  11. Documenting behavior deviations with evidence packages
  12. Reporting integrity findings in audit-ready format
Module 6. AI Supply Chain Security Assessment
Audit dependencies, pre-trained models, and development pipelines for risk.
12 chapters in this module
  1. Mapping AI supply chain components and their provenance
  2. Assessing security of model hubs and open-weight repositories
  3. Validating integrity of downloaded models using checksums and signatures
  4. Reviewing third-party library dependencies for known vulnerabilities
  5. Testing for backdoor insertion in pre-trained models
  6. Evaluating model card completeness and accuracy
  7. Auditing dataset sources and licensing compliance
  8. Assessing CI/CD pipelines for AI model deployment
  9. Reviewing fine-tuning data for contamination risks
  10. Checking for unauthorized API access or exfiltration paths
  11. Documenting supply chain test findings with traceability
  12. Reporting recommendations for supply chain hardening
Module 7. Data Poisoning and Training Pipeline Attacks
Simulate attacks that compromise model training and data integrity.
12 chapters in this module
  1. Understanding data poisoning attack mechanics and objectives
  2. Crafting malicious training samples to induce bias or backdoors
  3. Testing federated learning systems for rogue participant influence
  4. Evaluating data validation mechanisms in training pipelines
  5. Assessing data preprocessing steps for manipulation opportunities
  6. Measuring model sensitivity to small, targeted data perturbations
  7. Detecting poisoned models through statistical anomaly detection
  8. Testing retraining procedures for contamination removal
  9. Simulating insider threats in data labeling and curation
  10. Assessing data lineage tracking and provenance controls
  11. Documenting poisoning test design and results transparently
  12. Reporting data integrity risks with mitigation pathways
Module 8. Prompt Injection and Output Manipulation
Test AI systems for susceptibility to input-based command hijacking.
12 chapters in this module
  1. Classifying prompt injection types: direct, indirect, and chained
  2. Crafting payloads that override system instructions or role settings
  3. Testing for data exfiltration via prompt injection
  4. Exploiting context window manipulation for privilege escalation
  5. Assessing agent systems for plan hijacking and tool misuse
  6. Using indirect injection via document uploads or API inputs
  7. Testing RAG systems for retrieval-based manipulation
  8. Evaluating guardrail effectiveness against evolving injection tactics
  9. Measuring success rate of injection attempts across model versions
  10. Documenting injection test methodology and safeguards
  11. Reporting output manipulation risks without enabling exploit reuse
  12. Recommending architectural and filtering improvements
Module 9. AI Model Extraction and Replication
Assess the feasibility of reconstructing or stealing proprietary models.
12 chapters in this module
  1. Understanding model extraction attack objectives and constraints
  2. Using query-based methods to approximate model functionality
  3. Assessing API rate limits and monitoring for extraction attempts
  4. Testing membership inference to determine data inclusion
  5. Evaluating model fingerprinting techniques for ownership detection
  6. Measuring reconstruction accuracy from API responses
  7. Testing obfuscation methods for model protection
  8. Assessing legal and technical deterrents to model stealing
  9. Benchmarking extraction resistance across model types
  10. Documenting extraction test design and limitations
  11. Reporting exposure risks with protection recommendations
  12. Aligning findings with intellectual property and licensing concerns
Module 10. Reporting and Documentation Standards
Produce certification-grade AI penetration test reports.
12 chapters in this module
  1. Structuring reports for technical and executive audiences
  2. Documenting test scope, methodology, and constraints
  3. Presenting findings with severity ratings and business impact
  4. Including evidence packages with reproducible test steps
  5. Using standardized templates aligned with certification bodies
  6. Protecting sensitive exploit details in client deliverables
  7. Anonymizing data and system details for public sharing
  8. Integrating visualizations and model behavior graphs
  9. Writing executive summaries for procurement and legal teams
  10. Preparing appendices with technical depth and references
  11. Versioning and archiving reports for audit traceability
  12. Obtaining sign-off and finalizing report distribution
Module 11. Certification and Third-Party Validation
Navigate AI penetration testing certification processes.
12 chapters in this module
  1. Overview of existing AI security assessment certification programs
  2. Preparing for external validation of your testing methodology
  3. Auditing your own processes for consistency and repeatability
  4. Gathering evidence for certification body requirements
  5. Addressing feedback from peer review and accreditation bodies
  6. Maintaining certification through continuous improvement
  7. Comparing certification options: cost, scope, and recognition
  8. Leveraging certification to win client contracts
  9. Marketing certified capabilities without overclaiming
  10. Training teams to maintain certification standards
  11. Updating methodology in response to certification feedback
  12. Documenting certification journey for brand credibility
Module 12. Operationalizing AI Penetration Testing
Embed AI security testing into ongoing client delivery and advisory services.
12 chapters in this module
  1. Integrating AI pentesting into service offerings and pricing models
  2. Training junior staff using standardized methodology
  3. Building reusable templates and playbooks for efficiency
  4. Scaling assessments across multiple clients and verticals
  5. Positioning AI pentesting as a premium engagement
  6. Using assessments to unlock follow-on advisory work
  7. Marketing certified AI testing to procurement teams
  8. Developing client onboarding workflows for AI assessments
  9. Establishing refresh cycles for retesting and validation
  10. Collecting client feedback to improve service delivery
  11. Tracking engagement margins and time-to-delivery metrics
  12. Positioning your firm as a leader in implementable AI security

How this maps to your situation

  • AI security validation under procurement scrutiny
  • Client-facing AI audit package delivery
  • Certification-aligned penetration testing
  • High-margin advisory service expansion

Before vs. after

Before
Manual, inconsistent AI security assessments requiring rework under client review
After
Certification-grade, repeatable AI penetration tests delivered in under 10 hours

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 8, 10 hours total, designed for completion in short sessions with immediate application to live engagements.

If nothing changes
Continuing with ad-hoc AI testing risks rejected deliverables, lost contracts, and diminished credibility in a market where procurement teams increasingly demand certified methodology.

How this compares to the alternatives

Unlike generic AI security courses, this program delivers a certification-eligible methodology with templates and documentation standards used in high-stakes client engagements, focused on outcomes, not theory.

Frequently asked

Is this course technical or strategic?
It's implementation-grade and technical, designed for practitioners delivering AI security assessments. It focuses on repeatable testing procedures, not high-level risk concepts.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this prepare me for a certification exam?
It teaches a certification-aligned methodology used in client engagements, though it does not map to a single vendor exam. It prepares you to produce certification-grade work.
$199 one-time. Approximately 8, 10 hours total, designed for completion in short sessions with immediate application to live engagements..

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
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