Mastering AI-Powered Application Security Assessments
You're not behind. But you're not ahead either. And in the world of application security, standing still means falling behind. Every day, new AI-driven threats emerge that bypass traditional defenses. Your team relies on you to detect, assess, and neutralize them before they compromise critical systems. Yet most security frameworks were built before AI became a core attack vector. You're expected to lead with tools and methodologies that weren't designed for this new reality. Mastering AI-Powered Application Security Assessments is the only structured pathway to confidently audit, evaluate, and secure AI-integrated applications using modern, battle-tested techniques. This is not theory. It’s the exact process top-tier cybersecurity leaders use to deliver board-ready security assessments in under 30 days - complete with risk scoring, remediation roadmaps, and executive justification. One lead security architect at a Fortune 500 financial services firm used this framework to identify a covert AI model inversion vulnerability in a customer-facing chatbot. His assessment triggered an immediate internal review, prevented a data leak affecting 2.3 million users, and earned him a promotion within six weeks. He didn’t have AI security training. He had this methodology. This course closes the gap between traditional application security and the AI-driven future. No guesswork. No outdated checklists. Just a precise, repeatable system that transforms uncertainty into authority, and authority into career acceleration. You’ll walk away with a comprehensive AI security assessment package - complete with documentation templates, threat modeling matrices, and a Certificate of Completion issued by The Art of Service - all ready to deploy on day one in your organisation. Here’s how this course is structured to help you get there.Course Format & Delivery Details Designed for Maximum Flexibility and Real-World Impact
This is a completely self-paced, on-demand learning experience. There are no fixed schedules, no missed sessions, and no due dates. You control the pace, the timeline, and the depth of engagement - ideal for security professionals balancing full-time roles, compliance demands, and innovation pressure. With immediate online access, you can begin within minutes of enrollment. Most learners complete the core modules in 21–28 hours, with many applying key techniques to live projects in under 10 days. The fastest path from knowledge to impact is built into the curriculum’s phased structure. You gain lifetime access to all course materials, including future updates and enhancements at no additional cost. AI evolves rapidly, and your access evolves with it. Updates to threat models, emerging vulnerability patterns, and new regulatory alignment are delivered automatically. The platform is mobile-friendly and accessible 24/7 from any device, anywhere in the world. Whether you’re reviewing assessment frameworks on a tablet during a commute or finalizing a report from a hotel room, your progress syncs seamlessly across sessions. Full Instructor Support and Professional Validation
Every learner receives direct guidance from certified cybersecurity practitioners with over a decade of experience in AI risk assessment and enterprise penetration testing. You’re not left to figure it out alone. Submit questions, request feedback on draft assessments, and clarify complex attack patterns through secure communication channels. Upon completion, you’ll earn a Certificate of Completion issued by The Art of Service, a globally recognized credential trusted by cybersecurity leaders across financial, healthcare, and government sectors. This certificate validates your mastery of AI-powered assessment methodologies and can be added to your LinkedIn profile, CV, or internal promotion packet immediately. Transparent, Risk-Free Enrollment with Guaranteed Value
Pricing is straightforward with no hidden fees, recurring charges, or unlockable tiers. What you see is everything you get - lifetime access, full curriculum, certification, and support included. We accept all major payment methods, including Visa, Mastercard, and PayPal, ensuring a seamless transaction regardless of your location or finance protocols. Your investment is fully protected by a 30-day, 100% money-back guarantee. If the course doesn’t deliver measurable clarity, practical tools, and immediate applicability to your work, simply request a refund. No questions, no hurdles. After enrollment, you’ll receive a confirmation email. Your access details and login instructions will be sent separately once your course materials are prepared - ensuring a smooth, error-free onboarding experience. “Will This Work for Me?” - Let’s Address the Real Objections
You might be thinking: *I’m not an AI engineer. I don’t code every day. My organisation uses legacy tools. I’m already overwhelmed.* That’s exactly why this course works. It was designed for application security analysts, penetration testers, and risk officers who must assess AI-powered systems - even if they didn’t build them. One senior penetration tester with over 8 years in network security used this program to transition into AI security audits. He had zero prior AI model experience. Within four weeks, he led a successful assessment of a machine learning-powered fraud detection pipeline and was assigned as the internal subject matter expert. This works even if: • You’re not a data scientist • Your team uses proprietary or closed-source AI models • You work in a highly regulated environment • You’ve never written a formal AI security assessment report • You’re under pressure to deliver results fast The framework is tool-agnostic, methodology-first, and built around documented, repeatable processes - not niche coding skills. It turns your existing security expertise into AI-assessment authority. You’re not learning to become an AI developer. You’re learning to become the trusted evaluator of AI systems in high-stakes environments. That’s the skill organisations are desperately seeking - and rewarding.
Module 1: Foundations of AI-Powered Application Security - Understanding the shift from traditional to AI-integrated application security
- Core principles of secure-by-design in AI-powered systems
- Differentiating between AI models, pipelines, and applications
- Key differences in threat surfaces: data, training, inference, and feedback loops
- Overview of common AI security failure modes and business impacts
- Regulatory and compliance frameworks relevant to AI security (e.g. NIST, ISO/IEC 23894)
- Mapping organisational risk appetite to AI security controls
- The role of ethics, bias, and fairness in security assessments
- Establishing the scope and boundaries of an AI security assessment
- Defining the assessment lifecycle and stakeholder expectations
Module 2: Threat Modeling for AI Systems - Adapting STRIDE and DREAD methodologies for AI applications
- Identifying unique AI threat vectors: model inversion, membership inference, prompt injection
- Constructing data flow diagrams for AI pipelines
- Threat modeling at each stage: data ingestion, preprocessing, training, serving
- Evaluating third-party model providers and API dependencies
- Mapping privilege escalation risks in AI-enabled microservices
- Identifying shadow AI: unauthorised or undocumented models in production
- Assessing supply chain risks in pretrained models and datasets
- Applying MITRE ATLAS framework to real-world AI threats
- Building custom threat libraries tailored to organisational use cases
Module 3: Data-Centric Security in AI Applications - Securing training data: integrity, provenance, and contamination risks
- Detecting and mitigating poisoned datasets
- Data leakage risks during feature engineering and dimensionality reduction
- Encryption and anonymisation standards for AI data pipelines
- Assessing Personally Identifiable Information (PII) exposure in model outputs
- Implementing differential privacy techniques in assessment design
- Evaluating synthetic data generators for security gaps
- Validating data drift and concept drift detection mechanisms
- Security testing for data augmentation workflows
- Mapping data lineage from source to inference
Module 4: Model Security and Integrity Verification - Assessing model confidentiality: can the model be extracted?
- Techniques for detecting model stealing and replication attacks
- Validating model signing and tamper-proof deployment practices
- Reverse engineering risks in public model endpoints
- Attacker advantage analysis: quantifying model exposure
- Assessment of model versioning and rollback security
- Secure model storage and access control in cloud environments
- Black-box testing for model robustness against adversarial inputs
- Gray-box techniques to evaluate internal logic exposure
- Conducting model integrity audits using cryptographic verification
Module 5: AI Vulnerability Assessment Methodologies - Systematic approach to identifying AI-specific vulnerabilities
- Creating a repeatable vulnerability classification schema
- Scoring AI vulnerabilities using modified CVSS standards
- Automated scanning vs manual assessment: when to use each
- Evaluating model robustness under distributional shift
- Testing for adversarial example susceptibility in image and text models
- Assessing model bias as a security risk factor
- Identifying overconfidence and hallucination patterns in LLMs
- Penetration testing strategies for AI-powered APIs
- Validating input sanitisation and output filtering mechanisms
Module 6: Prompt Injection and AI-Driven Attack Surface Analysis - Understanding prompt injection as a first-class security threat
- Classifying prompt injection types: direct, indirect, and chain-of-thought
- Manual techniques to discover prompt injection vectors
- Automated tools for large-scale prompt probing
- Testing for data exfiltration via prompt manipulation
- Assessing multi-agent system risks and autonomous escalation
- Mapping prompt context window exploitation techniques
- Defensive strategies: prompt hardening, output validation, sandboxing
- Testing agentic workflows for recursive command execution
- Creating custom adversarial prompt libraries for red teaming
Module 7: Inference and Deployment Security - Securing real-time inference endpoints against abuse
- Rate limiting and quota enforcement for AI APIs
- Authentication and authorisation mechanisms for model access
- Monitoring for model denial-of-service attacks
- Assessing cold start and scalability vulnerabilities
- Validating secure model serving architecture (e.g. TensorFlow Serving, TorchServe)
- Container security for AI workloads (Docker, Kubernetes)
- Infrastructure as Code (IaC) review for AI deployment pipelines
- Zero-trust principles in AI service-to-service communication
- Logging and audit trail completeness for inference decisions
Module 8: AI Security Testing Tools and Frameworks - Overview of open-source AI security testing tools (IBM Adversarial Robustness Toolbox)
- Evaluating commercial AI security scanning platforms
- Integrating AI testing into CI/CD pipelines
- Custom script development for vulnerability discovery
- Using fuzzing techniques for AI input validation testing
- Benchmarking model defenses using standardized test suites
- Automating security regression testing for model updates
- Integrating OWASP Top 10 for LLMs into assessment workflows
- Comparing model explainability tools for audit transparency
- Building custom dashboards for AI risk visibility
Module 9: Secure Development Lifecycle for AI Applications - Integrating security assessments into MLOps workflows
- Security gates for model training, validation, and deployment
- Code review practices for AI script and pipeline integrity
- Dependency checking for AI libraries (e.g. PyTorch, Hugging Face)
- Version control strategies for models, data, and code
- Peer review processes for high-risk AI features
- Establishing AI security champions within development teams
- Creating security documentation templates for AI artifacts
- Onboarding checklist for new AI-powered services
- Post-mortem analysis of AI security incidents
Module 10: Real-World Assessment Simulations - Conducting end-to-end security assessment of a customer support chatbot
- Reviewing a recommendation engine for data leakage risks
- Assessing a fraud detection model for adversarial manipulation
- Evaluating a computer vision system for spoofing vulnerabilities
- Auditing a code generation assistant for IP exposure
- Testing a voice assistant for command injection flaws
- Analysing a predictive maintenance AI for operational sabotage risks
- Reviewing a sentiment analysis tool for privacy violations
- Assessing a document classification model for metadata leakage
- Conducting threat modeling for autonomous decision-making agents
Module 11: Reporting and Communication of AI Security Findings - Structuring executive summaries for non-technical stakeholders
- Translating technical risks into business impact statements
- Creating prioritised remediation roadmaps with effort estimates
- Visualising AI risk exposure using heat maps and dashboards
- Presenting findings to audit, compliance, and board-level committees
- Drafting actionable tickets for development and data science teams
- Documenting assumptions, limitations, and scope boundaries
- Establishing feedback loops with model owners
- Creating versioned assessment reports for regulatory audits
- Archiving assessment artifacts with retention policies
Module 12: AI Security Governance and Policy Development - Designing organisational AI security policies from scratch
- Defining acceptable use criteria for generative AI tools
- Establishing model registration and inventory requirements
- Creating approval workflows for AI deployment in production
- Developing incident response playbooks for AI breaches
- Conducting third-party AI vendor security assessments
- Implementing AI risk scoring across project portfolios
- Setting thresholds for model performance and security degradation
- Integrating AI security into enterprise risk management frameworks
- Aligning internal policies with evolving regulatory expectations
Module 13: Continuous Monitoring and AI Threat Intelligence - Designing monitoring systems for AI model behaviour drift
- Detecting anomalous inference patterns in real-time
- Setting up alerts for model confidence collapse or output instability
- Integrating AI security logs with SIEM platforms
- Establishing baselines for normal AI behaviour
- Threat intelligence sharing for emerging AI attack patterns
- Automated re-assessment triggers based on model updates
- Periodic control validation for long-running AI systems
- Feedback loop integration with human oversight teams
- Benchmarking against adversarial attack databases
Module 14: Certification Preparation and Career Application - Finalising your comprehensive AI security assessment portfolio
- Reviewing certification exam objectives and structure
- Practicing scenario-based assessment questions
- Preparing technical documentation for peer review
- Mapping your skills to job descriptions and promotion criteria
- Presenting your Certificate of Completion effectively
- Building a personal brand as an AI security assessor
- Negotiating salary increases based on specialised expertise
- Contributing to internal AI security training programs
- Planning next steps: advanced certifications and specialisations
- Understanding the shift from traditional to AI-integrated application security
- Core principles of secure-by-design in AI-powered systems
- Differentiating between AI models, pipelines, and applications
- Key differences in threat surfaces: data, training, inference, and feedback loops
- Overview of common AI security failure modes and business impacts
- Regulatory and compliance frameworks relevant to AI security (e.g. NIST, ISO/IEC 23894)
- Mapping organisational risk appetite to AI security controls
- The role of ethics, bias, and fairness in security assessments
- Establishing the scope and boundaries of an AI security assessment
- Defining the assessment lifecycle and stakeholder expectations
Module 2: Threat Modeling for AI Systems - Adapting STRIDE and DREAD methodologies for AI applications
- Identifying unique AI threat vectors: model inversion, membership inference, prompt injection
- Constructing data flow diagrams for AI pipelines
- Threat modeling at each stage: data ingestion, preprocessing, training, serving
- Evaluating third-party model providers and API dependencies
- Mapping privilege escalation risks in AI-enabled microservices
- Identifying shadow AI: unauthorised or undocumented models in production
- Assessing supply chain risks in pretrained models and datasets
- Applying MITRE ATLAS framework to real-world AI threats
- Building custom threat libraries tailored to organisational use cases
Module 3: Data-Centric Security in AI Applications - Securing training data: integrity, provenance, and contamination risks
- Detecting and mitigating poisoned datasets
- Data leakage risks during feature engineering and dimensionality reduction
- Encryption and anonymisation standards for AI data pipelines
- Assessing Personally Identifiable Information (PII) exposure in model outputs
- Implementing differential privacy techniques in assessment design
- Evaluating synthetic data generators for security gaps
- Validating data drift and concept drift detection mechanisms
- Security testing for data augmentation workflows
- Mapping data lineage from source to inference
Module 4: Model Security and Integrity Verification - Assessing model confidentiality: can the model be extracted?
- Techniques for detecting model stealing and replication attacks
- Validating model signing and tamper-proof deployment practices
- Reverse engineering risks in public model endpoints
- Attacker advantage analysis: quantifying model exposure
- Assessment of model versioning and rollback security
- Secure model storage and access control in cloud environments
- Black-box testing for model robustness against adversarial inputs
- Gray-box techniques to evaluate internal logic exposure
- Conducting model integrity audits using cryptographic verification
Module 5: AI Vulnerability Assessment Methodologies - Systematic approach to identifying AI-specific vulnerabilities
- Creating a repeatable vulnerability classification schema
- Scoring AI vulnerabilities using modified CVSS standards
- Automated scanning vs manual assessment: when to use each
- Evaluating model robustness under distributional shift
- Testing for adversarial example susceptibility in image and text models
- Assessing model bias as a security risk factor
- Identifying overconfidence and hallucination patterns in LLMs
- Penetration testing strategies for AI-powered APIs
- Validating input sanitisation and output filtering mechanisms
Module 6: Prompt Injection and AI-Driven Attack Surface Analysis - Understanding prompt injection as a first-class security threat
- Classifying prompt injection types: direct, indirect, and chain-of-thought
- Manual techniques to discover prompt injection vectors
- Automated tools for large-scale prompt probing
- Testing for data exfiltration via prompt manipulation
- Assessing multi-agent system risks and autonomous escalation
- Mapping prompt context window exploitation techniques
- Defensive strategies: prompt hardening, output validation, sandboxing
- Testing agentic workflows for recursive command execution
- Creating custom adversarial prompt libraries for red teaming
Module 7: Inference and Deployment Security - Securing real-time inference endpoints against abuse
- Rate limiting and quota enforcement for AI APIs
- Authentication and authorisation mechanisms for model access
- Monitoring for model denial-of-service attacks
- Assessing cold start and scalability vulnerabilities
- Validating secure model serving architecture (e.g. TensorFlow Serving, TorchServe)
- Container security for AI workloads (Docker, Kubernetes)
- Infrastructure as Code (IaC) review for AI deployment pipelines
- Zero-trust principles in AI service-to-service communication
- Logging and audit trail completeness for inference decisions
Module 8: AI Security Testing Tools and Frameworks - Overview of open-source AI security testing tools (IBM Adversarial Robustness Toolbox)
- Evaluating commercial AI security scanning platforms
- Integrating AI testing into CI/CD pipelines
- Custom script development for vulnerability discovery
- Using fuzzing techniques for AI input validation testing
- Benchmarking model defenses using standardized test suites
- Automating security regression testing for model updates
- Integrating OWASP Top 10 for LLMs into assessment workflows
- Comparing model explainability tools for audit transparency
- Building custom dashboards for AI risk visibility
Module 9: Secure Development Lifecycle for AI Applications - Integrating security assessments into MLOps workflows
- Security gates for model training, validation, and deployment
- Code review practices for AI script and pipeline integrity
- Dependency checking for AI libraries (e.g. PyTorch, Hugging Face)
- Version control strategies for models, data, and code
- Peer review processes for high-risk AI features
- Establishing AI security champions within development teams
- Creating security documentation templates for AI artifacts
- Onboarding checklist for new AI-powered services
- Post-mortem analysis of AI security incidents
Module 10: Real-World Assessment Simulations - Conducting end-to-end security assessment of a customer support chatbot
- Reviewing a recommendation engine for data leakage risks
- Assessing a fraud detection model for adversarial manipulation
- Evaluating a computer vision system for spoofing vulnerabilities
- Auditing a code generation assistant for IP exposure
- Testing a voice assistant for command injection flaws
- Analysing a predictive maintenance AI for operational sabotage risks
- Reviewing a sentiment analysis tool for privacy violations
- Assessing a document classification model for metadata leakage
- Conducting threat modeling for autonomous decision-making agents
Module 11: Reporting and Communication of AI Security Findings - Structuring executive summaries for non-technical stakeholders
- Translating technical risks into business impact statements
- Creating prioritised remediation roadmaps with effort estimates
- Visualising AI risk exposure using heat maps and dashboards
- Presenting findings to audit, compliance, and board-level committees
- Drafting actionable tickets for development and data science teams
- Documenting assumptions, limitations, and scope boundaries
- Establishing feedback loops with model owners
- Creating versioned assessment reports for regulatory audits
- Archiving assessment artifacts with retention policies
Module 12: AI Security Governance and Policy Development - Designing organisational AI security policies from scratch
- Defining acceptable use criteria for generative AI tools
- Establishing model registration and inventory requirements
- Creating approval workflows for AI deployment in production
- Developing incident response playbooks for AI breaches
- Conducting third-party AI vendor security assessments
- Implementing AI risk scoring across project portfolios
- Setting thresholds for model performance and security degradation
- Integrating AI security into enterprise risk management frameworks
- Aligning internal policies with evolving regulatory expectations
Module 13: Continuous Monitoring and AI Threat Intelligence - Designing monitoring systems for AI model behaviour drift
- Detecting anomalous inference patterns in real-time
- Setting up alerts for model confidence collapse or output instability
- Integrating AI security logs with SIEM platforms
- Establishing baselines for normal AI behaviour
- Threat intelligence sharing for emerging AI attack patterns
- Automated re-assessment triggers based on model updates
- Periodic control validation for long-running AI systems
- Feedback loop integration with human oversight teams
- Benchmarking against adversarial attack databases
Module 14: Certification Preparation and Career Application - Finalising your comprehensive AI security assessment portfolio
- Reviewing certification exam objectives and structure
- Practicing scenario-based assessment questions
- Preparing technical documentation for peer review
- Mapping your skills to job descriptions and promotion criteria
- Presenting your Certificate of Completion effectively
- Building a personal brand as an AI security assessor
- Negotiating salary increases based on specialised expertise
- Contributing to internal AI security training programs
- Planning next steps: advanced certifications and specialisations
- Securing training data: integrity, provenance, and contamination risks
- Detecting and mitigating poisoned datasets
- Data leakage risks during feature engineering and dimensionality reduction
- Encryption and anonymisation standards for AI data pipelines
- Assessing Personally Identifiable Information (PII) exposure in model outputs
- Implementing differential privacy techniques in assessment design
- Evaluating synthetic data generators for security gaps
- Validating data drift and concept drift detection mechanisms
- Security testing for data augmentation workflows
- Mapping data lineage from source to inference
Module 4: Model Security and Integrity Verification - Assessing model confidentiality: can the model be extracted?
- Techniques for detecting model stealing and replication attacks
- Validating model signing and tamper-proof deployment practices
- Reverse engineering risks in public model endpoints
- Attacker advantage analysis: quantifying model exposure
- Assessment of model versioning and rollback security
- Secure model storage and access control in cloud environments
- Black-box testing for model robustness against adversarial inputs
- Gray-box techniques to evaluate internal logic exposure
- Conducting model integrity audits using cryptographic verification
Module 5: AI Vulnerability Assessment Methodologies - Systematic approach to identifying AI-specific vulnerabilities
- Creating a repeatable vulnerability classification schema
- Scoring AI vulnerabilities using modified CVSS standards
- Automated scanning vs manual assessment: when to use each
- Evaluating model robustness under distributional shift
- Testing for adversarial example susceptibility in image and text models
- Assessing model bias as a security risk factor
- Identifying overconfidence and hallucination patterns in LLMs
- Penetration testing strategies for AI-powered APIs
- Validating input sanitisation and output filtering mechanisms
Module 6: Prompt Injection and AI-Driven Attack Surface Analysis - Understanding prompt injection as a first-class security threat
- Classifying prompt injection types: direct, indirect, and chain-of-thought
- Manual techniques to discover prompt injection vectors
- Automated tools for large-scale prompt probing
- Testing for data exfiltration via prompt manipulation
- Assessing multi-agent system risks and autonomous escalation
- Mapping prompt context window exploitation techniques
- Defensive strategies: prompt hardening, output validation, sandboxing
- Testing agentic workflows for recursive command execution
- Creating custom adversarial prompt libraries for red teaming
Module 7: Inference and Deployment Security - Securing real-time inference endpoints against abuse
- Rate limiting and quota enforcement for AI APIs
- Authentication and authorisation mechanisms for model access
- Monitoring for model denial-of-service attacks
- Assessing cold start and scalability vulnerabilities
- Validating secure model serving architecture (e.g. TensorFlow Serving, TorchServe)
- Container security for AI workloads (Docker, Kubernetes)
- Infrastructure as Code (IaC) review for AI deployment pipelines
- Zero-trust principles in AI service-to-service communication
- Logging and audit trail completeness for inference decisions
Module 8: AI Security Testing Tools and Frameworks - Overview of open-source AI security testing tools (IBM Adversarial Robustness Toolbox)
- Evaluating commercial AI security scanning platforms
- Integrating AI testing into CI/CD pipelines
- Custom script development for vulnerability discovery
- Using fuzzing techniques for AI input validation testing
- Benchmarking model defenses using standardized test suites
- Automating security regression testing for model updates
- Integrating OWASP Top 10 for LLMs into assessment workflows
- Comparing model explainability tools for audit transparency
- Building custom dashboards for AI risk visibility
Module 9: Secure Development Lifecycle for AI Applications - Integrating security assessments into MLOps workflows
- Security gates for model training, validation, and deployment
- Code review practices for AI script and pipeline integrity
- Dependency checking for AI libraries (e.g. PyTorch, Hugging Face)
- Version control strategies for models, data, and code
- Peer review processes for high-risk AI features
- Establishing AI security champions within development teams
- Creating security documentation templates for AI artifacts
- Onboarding checklist for new AI-powered services
- Post-mortem analysis of AI security incidents
Module 10: Real-World Assessment Simulations - Conducting end-to-end security assessment of a customer support chatbot
- Reviewing a recommendation engine for data leakage risks
- Assessing a fraud detection model for adversarial manipulation
- Evaluating a computer vision system for spoofing vulnerabilities
- Auditing a code generation assistant for IP exposure
- Testing a voice assistant for command injection flaws
- Analysing a predictive maintenance AI for operational sabotage risks
- Reviewing a sentiment analysis tool for privacy violations
- Assessing a document classification model for metadata leakage
- Conducting threat modeling for autonomous decision-making agents
Module 11: Reporting and Communication of AI Security Findings - Structuring executive summaries for non-technical stakeholders
- Translating technical risks into business impact statements
- Creating prioritised remediation roadmaps with effort estimates
- Visualising AI risk exposure using heat maps and dashboards
- Presenting findings to audit, compliance, and board-level committees
- Drafting actionable tickets for development and data science teams
- Documenting assumptions, limitations, and scope boundaries
- Establishing feedback loops with model owners
- Creating versioned assessment reports for regulatory audits
- Archiving assessment artifacts with retention policies
Module 12: AI Security Governance and Policy Development - Designing organisational AI security policies from scratch
- Defining acceptable use criteria for generative AI tools
- Establishing model registration and inventory requirements
- Creating approval workflows for AI deployment in production
- Developing incident response playbooks for AI breaches
- Conducting third-party AI vendor security assessments
- Implementing AI risk scoring across project portfolios
- Setting thresholds for model performance and security degradation
- Integrating AI security into enterprise risk management frameworks
- Aligning internal policies with evolving regulatory expectations
Module 13: Continuous Monitoring and AI Threat Intelligence - Designing monitoring systems for AI model behaviour drift
- Detecting anomalous inference patterns in real-time
- Setting up alerts for model confidence collapse or output instability
- Integrating AI security logs with SIEM platforms
- Establishing baselines for normal AI behaviour
- Threat intelligence sharing for emerging AI attack patterns
- Automated re-assessment triggers based on model updates
- Periodic control validation for long-running AI systems
- Feedback loop integration with human oversight teams
- Benchmarking against adversarial attack databases
Module 14: Certification Preparation and Career Application - Finalising your comprehensive AI security assessment portfolio
- Reviewing certification exam objectives and structure
- Practicing scenario-based assessment questions
- Preparing technical documentation for peer review
- Mapping your skills to job descriptions and promotion criteria
- Presenting your Certificate of Completion effectively
- Building a personal brand as an AI security assessor
- Negotiating salary increases based on specialised expertise
- Contributing to internal AI security training programs
- Planning next steps: advanced certifications and specialisations
- Systematic approach to identifying AI-specific vulnerabilities
- Creating a repeatable vulnerability classification schema
- Scoring AI vulnerabilities using modified CVSS standards
- Automated scanning vs manual assessment: when to use each
- Evaluating model robustness under distributional shift
- Testing for adversarial example susceptibility in image and text models
- Assessing model bias as a security risk factor
- Identifying overconfidence and hallucination patterns in LLMs
- Penetration testing strategies for AI-powered APIs
- Validating input sanitisation and output filtering mechanisms
Module 6: Prompt Injection and AI-Driven Attack Surface Analysis - Understanding prompt injection as a first-class security threat
- Classifying prompt injection types: direct, indirect, and chain-of-thought
- Manual techniques to discover prompt injection vectors
- Automated tools for large-scale prompt probing
- Testing for data exfiltration via prompt manipulation
- Assessing multi-agent system risks and autonomous escalation
- Mapping prompt context window exploitation techniques
- Defensive strategies: prompt hardening, output validation, sandboxing
- Testing agentic workflows for recursive command execution
- Creating custom adversarial prompt libraries for red teaming
Module 7: Inference and Deployment Security - Securing real-time inference endpoints against abuse
- Rate limiting and quota enforcement for AI APIs
- Authentication and authorisation mechanisms for model access
- Monitoring for model denial-of-service attacks
- Assessing cold start and scalability vulnerabilities
- Validating secure model serving architecture (e.g. TensorFlow Serving, TorchServe)
- Container security for AI workloads (Docker, Kubernetes)
- Infrastructure as Code (IaC) review for AI deployment pipelines
- Zero-trust principles in AI service-to-service communication
- Logging and audit trail completeness for inference decisions
Module 8: AI Security Testing Tools and Frameworks - Overview of open-source AI security testing tools (IBM Adversarial Robustness Toolbox)
- Evaluating commercial AI security scanning platforms
- Integrating AI testing into CI/CD pipelines
- Custom script development for vulnerability discovery
- Using fuzzing techniques for AI input validation testing
- Benchmarking model defenses using standardized test suites
- Automating security regression testing for model updates
- Integrating OWASP Top 10 for LLMs into assessment workflows
- Comparing model explainability tools for audit transparency
- Building custom dashboards for AI risk visibility
Module 9: Secure Development Lifecycle for AI Applications - Integrating security assessments into MLOps workflows
- Security gates for model training, validation, and deployment
- Code review practices for AI script and pipeline integrity
- Dependency checking for AI libraries (e.g. PyTorch, Hugging Face)
- Version control strategies for models, data, and code
- Peer review processes for high-risk AI features
- Establishing AI security champions within development teams
- Creating security documentation templates for AI artifacts
- Onboarding checklist for new AI-powered services
- Post-mortem analysis of AI security incidents
Module 10: Real-World Assessment Simulations - Conducting end-to-end security assessment of a customer support chatbot
- Reviewing a recommendation engine for data leakage risks
- Assessing a fraud detection model for adversarial manipulation
- Evaluating a computer vision system for spoofing vulnerabilities
- Auditing a code generation assistant for IP exposure
- Testing a voice assistant for command injection flaws
- Analysing a predictive maintenance AI for operational sabotage risks
- Reviewing a sentiment analysis tool for privacy violations
- Assessing a document classification model for metadata leakage
- Conducting threat modeling for autonomous decision-making agents
Module 11: Reporting and Communication of AI Security Findings - Structuring executive summaries for non-technical stakeholders
- Translating technical risks into business impact statements
- Creating prioritised remediation roadmaps with effort estimates
- Visualising AI risk exposure using heat maps and dashboards
- Presenting findings to audit, compliance, and board-level committees
- Drafting actionable tickets for development and data science teams
- Documenting assumptions, limitations, and scope boundaries
- Establishing feedback loops with model owners
- Creating versioned assessment reports for regulatory audits
- Archiving assessment artifacts with retention policies
Module 12: AI Security Governance and Policy Development - Designing organisational AI security policies from scratch
- Defining acceptable use criteria for generative AI tools
- Establishing model registration and inventory requirements
- Creating approval workflows for AI deployment in production
- Developing incident response playbooks for AI breaches
- Conducting third-party AI vendor security assessments
- Implementing AI risk scoring across project portfolios
- Setting thresholds for model performance and security degradation
- Integrating AI security into enterprise risk management frameworks
- Aligning internal policies with evolving regulatory expectations
Module 13: Continuous Monitoring and AI Threat Intelligence - Designing monitoring systems for AI model behaviour drift
- Detecting anomalous inference patterns in real-time
- Setting up alerts for model confidence collapse or output instability
- Integrating AI security logs with SIEM platforms
- Establishing baselines for normal AI behaviour
- Threat intelligence sharing for emerging AI attack patterns
- Automated re-assessment triggers based on model updates
- Periodic control validation for long-running AI systems
- Feedback loop integration with human oversight teams
- Benchmarking against adversarial attack databases
Module 14: Certification Preparation and Career Application - Finalising your comprehensive AI security assessment portfolio
- Reviewing certification exam objectives and structure
- Practicing scenario-based assessment questions
- Preparing technical documentation for peer review
- Mapping your skills to job descriptions and promotion criteria
- Presenting your Certificate of Completion effectively
- Building a personal brand as an AI security assessor
- Negotiating salary increases based on specialised expertise
- Contributing to internal AI security training programs
- Planning next steps: advanced certifications and specialisations
- Securing real-time inference endpoints against abuse
- Rate limiting and quota enforcement for AI APIs
- Authentication and authorisation mechanisms for model access
- Monitoring for model denial-of-service attacks
- Assessing cold start and scalability vulnerabilities
- Validating secure model serving architecture (e.g. TensorFlow Serving, TorchServe)
- Container security for AI workloads (Docker, Kubernetes)
- Infrastructure as Code (IaC) review for AI deployment pipelines
- Zero-trust principles in AI service-to-service communication
- Logging and audit trail completeness for inference decisions
Module 8: AI Security Testing Tools and Frameworks - Overview of open-source AI security testing tools (IBM Adversarial Robustness Toolbox)
- Evaluating commercial AI security scanning platforms
- Integrating AI testing into CI/CD pipelines
- Custom script development for vulnerability discovery
- Using fuzzing techniques for AI input validation testing
- Benchmarking model defenses using standardized test suites
- Automating security regression testing for model updates
- Integrating OWASP Top 10 for LLMs into assessment workflows
- Comparing model explainability tools for audit transparency
- Building custom dashboards for AI risk visibility
Module 9: Secure Development Lifecycle for AI Applications - Integrating security assessments into MLOps workflows
- Security gates for model training, validation, and deployment
- Code review practices for AI script and pipeline integrity
- Dependency checking for AI libraries (e.g. PyTorch, Hugging Face)
- Version control strategies for models, data, and code
- Peer review processes for high-risk AI features
- Establishing AI security champions within development teams
- Creating security documentation templates for AI artifacts
- Onboarding checklist for new AI-powered services
- Post-mortem analysis of AI security incidents
Module 10: Real-World Assessment Simulations - Conducting end-to-end security assessment of a customer support chatbot
- Reviewing a recommendation engine for data leakage risks
- Assessing a fraud detection model for adversarial manipulation
- Evaluating a computer vision system for spoofing vulnerabilities
- Auditing a code generation assistant for IP exposure
- Testing a voice assistant for command injection flaws
- Analysing a predictive maintenance AI for operational sabotage risks
- Reviewing a sentiment analysis tool for privacy violations
- Assessing a document classification model for metadata leakage
- Conducting threat modeling for autonomous decision-making agents
Module 11: Reporting and Communication of AI Security Findings - Structuring executive summaries for non-technical stakeholders
- Translating technical risks into business impact statements
- Creating prioritised remediation roadmaps with effort estimates
- Visualising AI risk exposure using heat maps and dashboards
- Presenting findings to audit, compliance, and board-level committees
- Drafting actionable tickets for development and data science teams
- Documenting assumptions, limitations, and scope boundaries
- Establishing feedback loops with model owners
- Creating versioned assessment reports for regulatory audits
- Archiving assessment artifacts with retention policies
Module 12: AI Security Governance and Policy Development - Designing organisational AI security policies from scratch
- Defining acceptable use criteria for generative AI tools
- Establishing model registration and inventory requirements
- Creating approval workflows for AI deployment in production
- Developing incident response playbooks for AI breaches
- Conducting third-party AI vendor security assessments
- Implementing AI risk scoring across project portfolios
- Setting thresholds for model performance and security degradation
- Integrating AI security into enterprise risk management frameworks
- Aligning internal policies with evolving regulatory expectations
Module 13: Continuous Monitoring and AI Threat Intelligence - Designing monitoring systems for AI model behaviour drift
- Detecting anomalous inference patterns in real-time
- Setting up alerts for model confidence collapse or output instability
- Integrating AI security logs with SIEM platforms
- Establishing baselines for normal AI behaviour
- Threat intelligence sharing for emerging AI attack patterns
- Automated re-assessment triggers based on model updates
- Periodic control validation for long-running AI systems
- Feedback loop integration with human oversight teams
- Benchmarking against adversarial attack databases
Module 14: Certification Preparation and Career Application - Finalising your comprehensive AI security assessment portfolio
- Reviewing certification exam objectives and structure
- Practicing scenario-based assessment questions
- Preparing technical documentation for peer review
- Mapping your skills to job descriptions and promotion criteria
- Presenting your Certificate of Completion effectively
- Building a personal brand as an AI security assessor
- Negotiating salary increases based on specialised expertise
- Contributing to internal AI security training programs
- Planning next steps: advanced certifications and specialisations
- Integrating security assessments into MLOps workflows
- Security gates for model training, validation, and deployment
- Code review practices for AI script and pipeline integrity
- Dependency checking for AI libraries (e.g. PyTorch, Hugging Face)
- Version control strategies for models, data, and code
- Peer review processes for high-risk AI features
- Establishing AI security champions within development teams
- Creating security documentation templates for AI artifacts
- Onboarding checklist for new AI-powered services
- Post-mortem analysis of AI security incidents
Module 10: Real-World Assessment Simulations - Conducting end-to-end security assessment of a customer support chatbot
- Reviewing a recommendation engine for data leakage risks
- Assessing a fraud detection model for adversarial manipulation
- Evaluating a computer vision system for spoofing vulnerabilities
- Auditing a code generation assistant for IP exposure
- Testing a voice assistant for command injection flaws
- Analysing a predictive maintenance AI for operational sabotage risks
- Reviewing a sentiment analysis tool for privacy violations
- Assessing a document classification model for metadata leakage
- Conducting threat modeling for autonomous decision-making agents
Module 11: Reporting and Communication of AI Security Findings - Structuring executive summaries for non-technical stakeholders
- Translating technical risks into business impact statements
- Creating prioritised remediation roadmaps with effort estimates
- Visualising AI risk exposure using heat maps and dashboards
- Presenting findings to audit, compliance, and board-level committees
- Drafting actionable tickets for development and data science teams
- Documenting assumptions, limitations, and scope boundaries
- Establishing feedback loops with model owners
- Creating versioned assessment reports for regulatory audits
- Archiving assessment artifacts with retention policies
Module 12: AI Security Governance and Policy Development - Designing organisational AI security policies from scratch
- Defining acceptable use criteria for generative AI tools
- Establishing model registration and inventory requirements
- Creating approval workflows for AI deployment in production
- Developing incident response playbooks for AI breaches
- Conducting third-party AI vendor security assessments
- Implementing AI risk scoring across project portfolios
- Setting thresholds for model performance and security degradation
- Integrating AI security into enterprise risk management frameworks
- Aligning internal policies with evolving regulatory expectations
Module 13: Continuous Monitoring and AI Threat Intelligence - Designing monitoring systems for AI model behaviour drift
- Detecting anomalous inference patterns in real-time
- Setting up alerts for model confidence collapse or output instability
- Integrating AI security logs with SIEM platforms
- Establishing baselines for normal AI behaviour
- Threat intelligence sharing for emerging AI attack patterns
- Automated re-assessment triggers based on model updates
- Periodic control validation for long-running AI systems
- Feedback loop integration with human oversight teams
- Benchmarking against adversarial attack databases
Module 14: Certification Preparation and Career Application - Finalising your comprehensive AI security assessment portfolio
- Reviewing certification exam objectives and structure
- Practicing scenario-based assessment questions
- Preparing technical documentation for peer review
- Mapping your skills to job descriptions and promotion criteria
- Presenting your Certificate of Completion effectively
- Building a personal brand as an AI security assessor
- Negotiating salary increases based on specialised expertise
- Contributing to internal AI security training programs
- Planning next steps: advanced certifications and specialisations
- Structuring executive summaries for non-technical stakeholders
- Translating technical risks into business impact statements
- Creating prioritised remediation roadmaps with effort estimates
- Visualising AI risk exposure using heat maps and dashboards
- Presenting findings to audit, compliance, and board-level committees
- Drafting actionable tickets for development and data science teams
- Documenting assumptions, limitations, and scope boundaries
- Establishing feedback loops with model owners
- Creating versioned assessment reports for regulatory audits
- Archiving assessment artifacts with retention policies
Module 12: AI Security Governance and Policy Development - Designing organisational AI security policies from scratch
- Defining acceptable use criteria for generative AI tools
- Establishing model registration and inventory requirements
- Creating approval workflows for AI deployment in production
- Developing incident response playbooks for AI breaches
- Conducting third-party AI vendor security assessments
- Implementing AI risk scoring across project portfolios
- Setting thresholds for model performance and security degradation
- Integrating AI security into enterprise risk management frameworks
- Aligning internal policies with evolving regulatory expectations
Module 13: Continuous Monitoring and AI Threat Intelligence - Designing monitoring systems for AI model behaviour drift
- Detecting anomalous inference patterns in real-time
- Setting up alerts for model confidence collapse or output instability
- Integrating AI security logs with SIEM platforms
- Establishing baselines for normal AI behaviour
- Threat intelligence sharing for emerging AI attack patterns
- Automated re-assessment triggers based on model updates
- Periodic control validation for long-running AI systems
- Feedback loop integration with human oversight teams
- Benchmarking against adversarial attack databases
Module 14: Certification Preparation and Career Application - Finalising your comprehensive AI security assessment portfolio
- Reviewing certification exam objectives and structure
- Practicing scenario-based assessment questions
- Preparing technical documentation for peer review
- Mapping your skills to job descriptions and promotion criteria
- Presenting your Certificate of Completion effectively
- Building a personal brand as an AI security assessor
- Negotiating salary increases based on specialised expertise
- Contributing to internal AI security training programs
- Planning next steps: advanced certifications and specialisations
- Designing monitoring systems for AI model behaviour drift
- Detecting anomalous inference patterns in real-time
- Setting up alerts for model confidence collapse or output instability
- Integrating AI security logs with SIEM platforms
- Establishing baselines for normal AI behaviour
- Threat intelligence sharing for emerging AI attack patterns
- Automated re-assessment triggers based on model updates
- Periodic control validation for long-running AI systems
- Feedback loop integration with human oversight teams
- Benchmarking against adversarial attack databases