Mastering Secure Software Lifecycle Practices for AI-Driven Enterprises
You’re not behind. But the window to lead is closing fast. Every day, AI projects stall-held back by security debt, fragmented workflows, and unchecked technical risk. You’re not alone if you’ve felt the pressure: delivering innovation while protecting your organisation from silent, systemic vulnerabilities that could derail a product launch-or worse, trigger regulatory fallout. Yet elite teams are moving differently. They're not just coding AI. They're building it with secure-by-design architecture, embedding governance from day one, and gaining board-level trust through auditable, resilient development lifecycles. Mastering Secure Software Lifecycle Practices for AI-Driven Enterprises is your blueprint to become that leader. This isn’t about theory-it’s a battle-tested roadmap to transition from reactive risk management to proactive, scalable AI delivery. You’ll go from uncertain prototypes to a fully governed AI product rollout, complete with documentation, compliance alignment, and a security assurance framework ready for audit. One of our early adopters, Elena Rossi, Senior AI Architect at a global fintech, used this method to secure stakeholder buy-in for an intelligent fraud detection system. Her proposal-including risk matrices, secure CI/CD pipelines, and third-party model validation-was approved in under 14 days. It’s now deployed across 3 regions with zero security incidents. This course is designed for engineers, technical leads, and security architects who refuse to choose between innovation and integrity. You’ll gain clarity, control, and career momentum-all within a structured path trusted by professionals in regulated sectors. Here’s how this course is structured to help you get there.Course Format & Delivery Details Self-Paced. Immediate Online Access. Zero Time Conflicts. This course is designed for the working professional. You decide when and where you learn. There are no fixed start dates, no mandatory live sessions, and no time zone constraints-just immediate access to a comprehensive, on-demand curriculum built for maximum integration with your real-world responsibilities. Most learners complete the core framework in under 25 hours and are able to apply the first set of deliverables-such as a risk-weighted SDLC map and a secure model deployment checklist-to their current projects within days. You get lifetime access to all course materials. This includes every framework, template, and tool guide, with ongoing future updates provided at no additional cost. As AI regulations evolve and new attack vectors emerge, your access evolves with them. The platform is mobile-friendly and accessible 24/7 from any device. Whether you’re reviewing threat models on your phone during transit or finalising a secure integration plan from a remote office, your progress is preserved, tracked, and always within reach. You are not alone. Throughout the course, you’ll have direct access to expert instructor support via a monitored guidance channel. Ask specific questions about model provenance tracking, AI-specific threat modeling, or compliance alignment, and receive detailed, contextual guidance within 24 business hours. Upon completion, you will earn a Certificate of Completion issued by The Art of Service-a globally recognised credential trusted by enterprises, auditors, and technical hiring managers. This certification validates your command of secure AI development practices and signals operational discipline to stakeholders. Pricing is straightforward with no hidden fees. The total cost includes full access, all templates, expert support, and the formal certificate. There are no upsells, no premium tiers, and no recurring charges. We accept all major payment methods, including Visa, Mastercard, and PayPal, ensuring a frictionless onboarding process for individuals and teams. Your journey comes with a 30-day satisfied-or-refunded guarantee. If the course does not meet your expectations for depth, relevance, or practical value, simply request a full refund. No questions, no hurdles. We reverse the risk so you can move forward with confidence. After enrollment, you’ll receive a confirmation email. Your access credentials and entry point to the course will be sent separately once your materials are fully prepared-ensuring every resource is accurate, up to date, and optimised for your success. Will this work for you? Absolutely-even if: - You’re not a security specialist but need to lead AI projects with confidence
- Your organisation uses third-party or open-source AI models
- You’re integrating AI into legacy systems with complex compliance requirements
- You’re under pressure to move faster but can’t afford to compromise on resilience
Our curriculum is used by AI leads in financial services, healthcare, and government tech-where failure is not an option. The frameworks are calibrated for real environments, not ideal ones. Join thousands of professionals who’ve turned uncertainty into authority. With clear structure, field-tested tools, and an unshakeable foundation in secure development, you’ll be equipped to deliver AI that's not only intelligent-but trustworthy.
Module 1: Foundations of AI-Driven Security Risk - Understanding the evolving threat landscape in AI development
- Key differences between traditional SDLC and AI-augmented SDLC
- Common failure points in insecure AI model deployment
- Regulatory drivers shaping AI security: GDPR, NIS2, AI Act, and ISO/IEC 23894
- Defining secure-by-design principles for machine learning systems
- Mapping AI-specific risks across data, model, and inference layers
- The role of bias detection in pre-deployment security validation
- Architecture-level vulnerabilities in generative AI pipelines
- Integrating security into MLOps from inception
- Establishing organisational AI security governance maturity
Module 2: Secure Requirements Engineering for AI Systems - Deriving security requirements from AI use case objectives
- Defining data provenance and lineage constraints
- Threat modeling during AI solution scoping (STRIDE applied to AI)
- Specifying model robustness and adversarial resilience criteria
- Embedding explainability and auditability as functional requirements
- Handling third-party model integration risks
- Legal and contractual obligations in data sourcing
- Confidentiality and access control specifications for training data
- Requirement traceability from business need to technical implementation
- Using threat libraries to anticipate AI-specific attack surfaces
Module 3: Designing Secure AI Architectures - Principles of zero-trust in AI system design
- Secure data flow mapping for AI pipelines
- Architecting model isolation and execution sandboxing
- Secure model storage and model versioning strategies
- Designing secure APIs for model inference services
- Enforcing input sanitisation and guardrails at inference points
- Defensive design against prompt injection and jailbreaking
- Secure integration of LLMs with internal knowledge bases
- Secure orchestration layers in multi-agent AI systems
- Architecture review checklists for AI deployment readiness
Module 4: Secure Data Management and Model Training - Securing training data ingestion and preprocessing
- Data deduplication and poisoning detection techniques
- Secure handling of sensitive attributes in datasets
- Data encryption standards for training environments
- Access control policies for annotated and labelled data
- Model training in regulated environments (air-gapped, audited)
- Verifying data integrity with cryptographic hashes
- Secure bulk data transfers using approved protocols
- Training pipeline auditing and logging requirements
- Integrating differential privacy in model development
Module 5: Model Development and Code Security - Secure coding standards for AI model scripts and notebooks
- Static code analysis for Python, PyTorch, TensorFlow, and JAX
- Preventing secret leakage in AI development repositories
- Managing dependencies with software bill of materials (SBOM)
- Code signing and integrity verification for models and scripts
- Pre-commit hooks and automation for security linting
- Secure handling of notebook-based development in teams
- Version control best practices for AI artifacts (Git-LFS, DVC)
- Isolation of development, staging, and production code branches
- Peer review standards for AI model code
Module 6: Secure Build and Integration Pipelines - Designing secure CI/CD workflows for AI models
- Securing GitHub Actions, GitLab CI, and Jenkins for ML
- Environment hardening for build agents
- Secrets management using Hashicorp Vault and AWS Secrets Manager
- Container security in model build pipelines (Docker, Podman)
- Base image scanning and vulnerability patching
- Immutable pipeline execution and audit trails
- Automating security gates in model integration workflows
- Integration of SAST and SCA tools into AI CI/CD
- Secure handling of model checkpoints and intermediate weights
Module 7: Threat Modeling for AI Systems - Applying PASTA and OCTAVE to AI workloads
- Mapping adversarial AI attack trees
- Identifying high-risk data and model touchpoints
- Attacker persona development for AI-specific threats
- Using attack libraries: MITRE ATLAS and LLM-ATTACK
- Conducting AI-focused pen-testing scoping sessions
- Prioritising threats using DREAD and CARVER matrices
- Model inversion and membership inference risk assessment
- Defending against evasion, poisoning, and backdoor attacks
- Creating mitigating controls from threat model outputs
Module 8: Secure Testing and Validation Frameworks - Designing test suites for model security and robustness
- Adversarial testing: generating perturbed inputs for validation
- Bias and fairness testing across demographic variables
- Stress testing models under abnormal input loads
- Conformance testing against regulatory standards
- Automated security regression testing for AI models
- Conducting red team exercises for AI systems
- Model explainability verification using SHAP, LIME, and feature importance
- Validation of model drift detection thresholds
- Creating audit-ready test documentation packs
Module 9: Secure Deployment and Model Release - Secure production environment provisioning
- Immutable model container deployment strategies
- Canary and blue-green releases for AI models
- Infrastructure as Code (IaC) security: Terraform, CloudFormation
- Role-based access control for model deployment operations
- Model signing and cryptographic attestation (Sigstore, in-toto)
- Audit logging for model activation and configuration changes
- API key rotation and secure service account management
- Secure configuration of inference endpoints
- Runtime protection using eBPF and kernel-level monitoring
Module 10: Runtime Security and Monitoring - Real-time input filtering and anomaly detection
- Monitoring for prompt injection and output manipulation
- Implementing rate limiting and abuse prevention
- Log aggregation and analysis for AI interactions
- Runtime application self-protection (RASP) for AI services
- Model performance and behaviour drift detection
- Alerting on suspicious inference patterns
- Monitoring third-party model API calls and usage
- Secure session management in user-facing AI products
- Integrating AI logs with SIEM and SOAR platforms
Module 11: Incident Response for AI Systems - Developing AI-specific incident playbooks
- Classifying AI incidents: model poisoning, data leak, bias exposure
- Containment procedures for compromised models
- Forensic analysis of model training data and weights
- Preserving chain of custody for AI artifacts
- Coordinating cross-functional response teams
- Notification protocols for AI-related breaches
- Rebuilding and revalidating models post-incident
- Post-mortem analysis and improvement loops
- Regulatory reporting requirements for AI incidents
Module 12: Compliance and Audit Readiness - Mapping controls to ISO/IEC 42001 and NIST AI RMF
- Establishing AI governance documentation frameworks
- Preparing a model inventory and registry
- Documenting data processing assessments (DPIAs) for AI
- Creating model cards and system cards for transparency
- Generating artefacts for internal and external audits
- Aligning AI practices with SOC 2, ISO 27001, and ISO 31000
- Third-party vendor risk assessment for AI suppliers
- Conducting internal AI control assessments
- Maintaining compliance documentation dashboards
Module 13: Secure Model Operations (MLOps) - Implementing secure model lifecycle management
- Automated model retraining with data integrity checks
- Secure handling of model updates and rollbacks
- Model version control and provenance tracking
- Automated drift detection and alerting
- Secure access to model monitoring dashboards
- Configuration drift prevention in AI infrastructure
- Secure model retirement and data erasure procedures
- Maintaining audit logs for operational decisions
- Integrating observability with security monitoring
Module 14: Governance, Risk, and Oversight - Establishing an AI ethics and security review board
- Developing AI risk appetite statements
- Risk ranking AI projects using heat mapping
- Creating escalation paths for high-risk model findings
- Defining oversight roles: AI stewards, guardians, reviewers
- Drafting AI use policies and acceptable use guidelines
- Ensuring board-level reporting on AI risk posture
- Third-party model assurance frameworks
- Conducting periodic AI security maturity assessments
- Building organisational AI security capability roadmaps
Module 15: Integration with Enterprise Security Programs - Embedding AI security into existing cybersecurity frameworks
- Integrating AI risk into enterprise GRC platforms
- Aligning with CISO office objectives and reporting lines
- Extending security information and event management to AI
- Implementing secure identity federation for AI services
- Training security operations teams on AI threats
- Conducting AI-focused phishing and social engineering tests
- Updating business continuity plans to include AI failures
- Integrating AI security into third-party risk management
- Sharing threat intelligence on AI-specific attacks
Module 16: Secure Development of Generative AI Applications - Secure prompt engineering principles
- Handling sensitive user data in LLM interactions
- Preventing data leakage through model outputs
- Secure fine-tuning of large language models
- Validation of retrieval-augmented generation (RAG) pipelines
- Securing vector databases and embedding stores
- Privacy-preserving techniques in semantic search
- Monitoring hallucination rates and factual drift
- Controlling access to enterprise knowledge connectors
- Auditing generative outputs for compliance and brand safety
Module 17: Advanced Security Automation and Tooling - Selecting AI security tools: detection, monitoring, enforcement
- Integrating AI security scanners into DevOps workflows
- Automated threat model generation for new AI features
- Policy-as-Code for AI governance enforcement
- Dynamic analysis of AI model behaviour in sandboxed runs
- AI-assisted code review for security vulnerabilities
- Automated model card generation and documentation
- Security posture dashboards for AI portfolios
- Custom rule development for AI-specific SAST tools
- Building secure AI development sandboxes for teams
Module 18: Implementation Projects and Real-World Applications - Securing an AI-powered customer support chatbot
- Implementing a regulated AI underwriting model
- Hardening an internal RAG knowledge assistant
- Deploying a computer vision model in a manufacturing setting
- Integrating external LLM APIs securely with internal data
- Creating a secure AI model marketplace within an enterprise
- Developing a compliance-focused document summarisation tool
- Building an anomaly detection system with adversarial robustness
- Protecting AI-driven supply chain forecasting models
- Designing a secure AI co-pilot for developers
Module 19: Certification, Career Advancement, and Next Steps - Preparing your Certificate of Completion submission
- Compiling a professional portfolio of secure AI documentation
- Highlighting certification on LinkedIn and resumes
- Communicating your achievement to managers and stakeholders
- Positioning yourself for AI security leadership roles
- Advancing into roles: AI Security Lead, ML Security Engineer, AI Governance Officer
- Joining global communities of AI security practitioners
- Accessing advanced resources and reading lists
- Maintaining skills with ongoing update notifications
- Planned pathways to higher-level certifications in AI governance
- Understanding the evolving threat landscape in AI development
- Key differences between traditional SDLC and AI-augmented SDLC
- Common failure points in insecure AI model deployment
- Regulatory drivers shaping AI security: GDPR, NIS2, AI Act, and ISO/IEC 23894
- Defining secure-by-design principles for machine learning systems
- Mapping AI-specific risks across data, model, and inference layers
- The role of bias detection in pre-deployment security validation
- Architecture-level vulnerabilities in generative AI pipelines
- Integrating security into MLOps from inception
- Establishing organisational AI security governance maturity
Module 2: Secure Requirements Engineering for AI Systems - Deriving security requirements from AI use case objectives
- Defining data provenance and lineage constraints
- Threat modeling during AI solution scoping (STRIDE applied to AI)
- Specifying model robustness and adversarial resilience criteria
- Embedding explainability and auditability as functional requirements
- Handling third-party model integration risks
- Legal and contractual obligations in data sourcing
- Confidentiality and access control specifications for training data
- Requirement traceability from business need to technical implementation
- Using threat libraries to anticipate AI-specific attack surfaces
Module 3: Designing Secure AI Architectures - Principles of zero-trust in AI system design
- Secure data flow mapping for AI pipelines
- Architecting model isolation and execution sandboxing
- Secure model storage and model versioning strategies
- Designing secure APIs for model inference services
- Enforcing input sanitisation and guardrails at inference points
- Defensive design against prompt injection and jailbreaking
- Secure integration of LLMs with internal knowledge bases
- Secure orchestration layers in multi-agent AI systems
- Architecture review checklists for AI deployment readiness
Module 4: Secure Data Management and Model Training - Securing training data ingestion and preprocessing
- Data deduplication and poisoning detection techniques
- Secure handling of sensitive attributes in datasets
- Data encryption standards for training environments
- Access control policies for annotated and labelled data
- Model training in regulated environments (air-gapped, audited)
- Verifying data integrity with cryptographic hashes
- Secure bulk data transfers using approved protocols
- Training pipeline auditing and logging requirements
- Integrating differential privacy in model development
Module 5: Model Development and Code Security - Secure coding standards for AI model scripts and notebooks
- Static code analysis for Python, PyTorch, TensorFlow, and JAX
- Preventing secret leakage in AI development repositories
- Managing dependencies with software bill of materials (SBOM)
- Code signing and integrity verification for models and scripts
- Pre-commit hooks and automation for security linting
- Secure handling of notebook-based development in teams
- Version control best practices for AI artifacts (Git-LFS, DVC)
- Isolation of development, staging, and production code branches
- Peer review standards for AI model code
Module 6: Secure Build and Integration Pipelines - Designing secure CI/CD workflows for AI models
- Securing GitHub Actions, GitLab CI, and Jenkins for ML
- Environment hardening for build agents
- Secrets management using Hashicorp Vault and AWS Secrets Manager
- Container security in model build pipelines (Docker, Podman)
- Base image scanning and vulnerability patching
- Immutable pipeline execution and audit trails
- Automating security gates in model integration workflows
- Integration of SAST and SCA tools into AI CI/CD
- Secure handling of model checkpoints and intermediate weights
Module 7: Threat Modeling for AI Systems - Applying PASTA and OCTAVE to AI workloads
- Mapping adversarial AI attack trees
- Identifying high-risk data and model touchpoints
- Attacker persona development for AI-specific threats
- Using attack libraries: MITRE ATLAS and LLM-ATTACK
- Conducting AI-focused pen-testing scoping sessions
- Prioritising threats using DREAD and CARVER matrices
- Model inversion and membership inference risk assessment
- Defending against evasion, poisoning, and backdoor attacks
- Creating mitigating controls from threat model outputs
Module 8: Secure Testing and Validation Frameworks - Designing test suites for model security and robustness
- Adversarial testing: generating perturbed inputs for validation
- Bias and fairness testing across demographic variables
- Stress testing models under abnormal input loads
- Conformance testing against regulatory standards
- Automated security regression testing for AI models
- Conducting red team exercises for AI systems
- Model explainability verification using SHAP, LIME, and feature importance
- Validation of model drift detection thresholds
- Creating audit-ready test documentation packs
Module 9: Secure Deployment and Model Release - Secure production environment provisioning
- Immutable model container deployment strategies
- Canary and blue-green releases for AI models
- Infrastructure as Code (IaC) security: Terraform, CloudFormation
- Role-based access control for model deployment operations
- Model signing and cryptographic attestation (Sigstore, in-toto)
- Audit logging for model activation and configuration changes
- API key rotation and secure service account management
- Secure configuration of inference endpoints
- Runtime protection using eBPF and kernel-level monitoring
Module 10: Runtime Security and Monitoring - Real-time input filtering and anomaly detection
- Monitoring for prompt injection and output manipulation
- Implementing rate limiting and abuse prevention
- Log aggregation and analysis for AI interactions
- Runtime application self-protection (RASP) for AI services
- Model performance and behaviour drift detection
- Alerting on suspicious inference patterns
- Monitoring third-party model API calls and usage
- Secure session management in user-facing AI products
- Integrating AI logs with SIEM and SOAR platforms
Module 11: Incident Response for AI Systems - Developing AI-specific incident playbooks
- Classifying AI incidents: model poisoning, data leak, bias exposure
- Containment procedures for compromised models
- Forensic analysis of model training data and weights
- Preserving chain of custody for AI artifacts
- Coordinating cross-functional response teams
- Notification protocols for AI-related breaches
- Rebuilding and revalidating models post-incident
- Post-mortem analysis and improvement loops
- Regulatory reporting requirements for AI incidents
Module 12: Compliance and Audit Readiness - Mapping controls to ISO/IEC 42001 and NIST AI RMF
- Establishing AI governance documentation frameworks
- Preparing a model inventory and registry
- Documenting data processing assessments (DPIAs) for AI
- Creating model cards and system cards for transparency
- Generating artefacts for internal and external audits
- Aligning AI practices with SOC 2, ISO 27001, and ISO 31000
- Third-party vendor risk assessment for AI suppliers
- Conducting internal AI control assessments
- Maintaining compliance documentation dashboards
Module 13: Secure Model Operations (MLOps) - Implementing secure model lifecycle management
- Automated model retraining with data integrity checks
- Secure handling of model updates and rollbacks
- Model version control and provenance tracking
- Automated drift detection and alerting
- Secure access to model monitoring dashboards
- Configuration drift prevention in AI infrastructure
- Secure model retirement and data erasure procedures
- Maintaining audit logs for operational decisions
- Integrating observability with security monitoring
Module 14: Governance, Risk, and Oversight - Establishing an AI ethics and security review board
- Developing AI risk appetite statements
- Risk ranking AI projects using heat mapping
- Creating escalation paths for high-risk model findings
- Defining oversight roles: AI stewards, guardians, reviewers
- Drafting AI use policies and acceptable use guidelines
- Ensuring board-level reporting on AI risk posture
- Third-party model assurance frameworks
- Conducting periodic AI security maturity assessments
- Building organisational AI security capability roadmaps
Module 15: Integration with Enterprise Security Programs - Embedding AI security into existing cybersecurity frameworks
- Integrating AI risk into enterprise GRC platforms
- Aligning with CISO office objectives and reporting lines
- Extending security information and event management to AI
- Implementing secure identity federation for AI services
- Training security operations teams on AI threats
- Conducting AI-focused phishing and social engineering tests
- Updating business continuity plans to include AI failures
- Integrating AI security into third-party risk management
- Sharing threat intelligence on AI-specific attacks
Module 16: Secure Development of Generative AI Applications - Secure prompt engineering principles
- Handling sensitive user data in LLM interactions
- Preventing data leakage through model outputs
- Secure fine-tuning of large language models
- Validation of retrieval-augmented generation (RAG) pipelines
- Securing vector databases and embedding stores
- Privacy-preserving techniques in semantic search
- Monitoring hallucination rates and factual drift
- Controlling access to enterprise knowledge connectors
- Auditing generative outputs for compliance and brand safety
Module 17: Advanced Security Automation and Tooling - Selecting AI security tools: detection, monitoring, enforcement
- Integrating AI security scanners into DevOps workflows
- Automated threat model generation for new AI features
- Policy-as-Code for AI governance enforcement
- Dynamic analysis of AI model behaviour in sandboxed runs
- AI-assisted code review for security vulnerabilities
- Automated model card generation and documentation
- Security posture dashboards for AI portfolios
- Custom rule development for AI-specific SAST tools
- Building secure AI development sandboxes for teams
Module 18: Implementation Projects and Real-World Applications - Securing an AI-powered customer support chatbot
- Implementing a regulated AI underwriting model
- Hardening an internal RAG knowledge assistant
- Deploying a computer vision model in a manufacturing setting
- Integrating external LLM APIs securely with internal data
- Creating a secure AI model marketplace within an enterprise
- Developing a compliance-focused document summarisation tool
- Building an anomaly detection system with adversarial robustness
- Protecting AI-driven supply chain forecasting models
- Designing a secure AI co-pilot for developers
Module 19: Certification, Career Advancement, and Next Steps - Preparing your Certificate of Completion submission
- Compiling a professional portfolio of secure AI documentation
- Highlighting certification on LinkedIn and resumes
- Communicating your achievement to managers and stakeholders
- Positioning yourself for AI security leadership roles
- Advancing into roles: AI Security Lead, ML Security Engineer, AI Governance Officer
- Joining global communities of AI security practitioners
- Accessing advanced resources and reading lists
- Maintaining skills with ongoing update notifications
- Planned pathways to higher-level certifications in AI governance
- Principles of zero-trust in AI system design
- Secure data flow mapping for AI pipelines
- Architecting model isolation and execution sandboxing
- Secure model storage and model versioning strategies
- Designing secure APIs for model inference services
- Enforcing input sanitisation and guardrails at inference points
- Defensive design against prompt injection and jailbreaking
- Secure integration of LLMs with internal knowledge bases
- Secure orchestration layers in multi-agent AI systems
- Architecture review checklists for AI deployment readiness
Module 4: Secure Data Management and Model Training - Securing training data ingestion and preprocessing
- Data deduplication and poisoning detection techniques
- Secure handling of sensitive attributes in datasets
- Data encryption standards for training environments
- Access control policies for annotated and labelled data
- Model training in regulated environments (air-gapped, audited)
- Verifying data integrity with cryptographic hashes
- Secure bulk data transfers using approved protocols
- Training pipeline auditing and logging requirements
- Integrating differential privacy in model development
Module 5: Model Development and Code Security - Secure coding standards for AI model scripts and notebooks
- Static code analysis for Python, PyTorch, TensorFlow, and JAX
- Preventing secret leakage in AI development repositories
- Managing dependencies with software bill of materials (SBOM)
- Code signing and integrity verification for models and scripts
- Pre-commit hooks and automation for security linting
- Secure handling of notebook-based development in teams
- Version control best practices for AI artifacts (Git-LFS, DVC)
- Isolation of development, staging, and production code branches
- Peer review standards for AI model code
Module 6: Secure Build and Integration Pipelines - Designing secure CI/CD workflows for AI models
- Securing GitHub Actions, GitLab CI, and Jenkins for ML
- Environment hardening for build agents
- Secrets management using Hashicorp Vault and AWS Secrets Manager
- Container security in model build pipelines (Docker, Podman)
- Base image scanning and vulnerability patching
- Immutable pipeline execution and audit trails
- Automating security gates in model integration workflows
- Integration of SAST and SCA tools into AI CI/CD
- Secure handling of model checkpoints and intermediate weights
Module 7: Threat Modeling for AI Systems - Applying PASTA and OCTAVE to AI workloads
- Mapping adversarial AI attack trees
- Identifying high-risk data and model touchpoints
- Attacker persona development for AI-specific threats
- Using attack libraries: MITRE ATLAS and LLM-ATTACK
- Conducting AI-focused pen-testing scoping sessions
- Prioritising threats using DREAD and CARVER matrices
- Model inversion and membership inference risk assessment
- Defending against evasion, poisoning, and backdoor attacks
- Creating mitigating controls from threat model outputs
Module 8: Secure Testing and Validation Frameworks - Designing test suites for model security and robustness
- Adversarial testing: generating perturbed inputs for validation
- Bias and fairness testing across demographic variables
- Stress testing models under abnormal input loads
- Conformance testing against regulatory standards
- Automated security regression testing for AI models
- Conducting red team exercises for AI systems
- Model explainability verification using SHAP, LIME, and feature importance
- Validation of model drift detection thresholds
- Creating audit-ready test documentation packs
Module 9: Secure Deployment and Model Release - Secure production environment provisioning
- Immutable model container deployment strategies
- Canary and blue-green releases for AI models
- Infrastructure as Code (IaC) security: Terraform, CloudFormation
- Role-based access control for model deployment operations
- Model signing and cryptographic attestation (Sigstore, in-toto)
- Audit logging for model activation and configuration changes
- API key rotation and secure service account management
- Secure configuration of inference endpoints
- Runtime protection using eBPF and kernel-level monitoring
Module 10: Runtime Security and Monitoring - Real-time input filtering and anomaly detection
- Monitoring for prompt injection and output manipulation
- Implementing rate limiting and abuse prevention
- Log aggregation and analysis for AI interactions
- Runtime application self-protection (RASP) for AI services
- Model performance and behaviour drift detection
- Alerting on suspicious inference patterns
- Monitoring third-party model API calls and usage
- Secure session management in user-facing AI products
- Integrating AI logs with SIEM and SOAR platforms
Module 11: Incident Response for AI Systems - Developing AI-specific incident playbooks
- Classifying AI incidents: model poisoning, data leak, bias exposure
- Containment procedures for compromised models
- Forensic analysis of model training data and weights
- Preserving chain of custody for AI artifacts
- Coordinating cross-functional response teams
- Notification protocols for AI-related breaches
- Rebuilding and revalidating models post-incident
- Post-mortem analysis and improvement loops
- Regulatory reporting requirements for AI incidents
Module 12: Compliance and Audit Readiness - Mapping controls to ISO/IEC 42001 and NIST AI RMF
- Establishing AI governance documentation frameworks
- Preparing a model inventory and registry
- Documenting data processing assessments (DPIAs) for AI
- Creating model cards and system cards for transparency
- Generating artefacts for internal and external audits
- Aligning AI practices with SOC 2, ISO 27001, and ISO 31000
- Third-party vendor risk assessment for AI suppliers
- Conducting internal AI control assessments
- Maintaining compliance documentation dashboards
Module 13: Secure Model Operations (MLOps) - Implementing secure model lifecycle management
- Automated model retraining with data integrity checks
- Secure handling of model updates and rollbacks
- Model version control and provenance tracking
- Automated drift detection and alerting
- Secure access to model monitoring dashboards
- Configuration drift prevention in AI infrastructure
- Secure model retirement and data erasure procedures
- Maintaining audit logs for operational decisions
- Integrating observability with security monitoring
Module 14: Governance, Risk, and Oversight - Establishing an AI ethics and security review board
- Developing AI risk appetite statements
- Risk ranking AI projects using heat mapping
- Creating escalation paths for high-risk model findings
- Defining oversight roles: AI stewards, guardians, reviewers
- Drafting AI use policies and acceptable use guidelines
- Ensuring board-level reporting on AI risk posture
- Third-party model assurance frameworks
- Conducting periodic AI security maturity assessments
- Building organisational AI security capability roadmaps
Module 15: Integration with Enterprise Security Programs - Embedding AI security into existing cybersecurity frameworks
- Integrating AI risk into enterprise GRC platforms
- Aligning with CISO office objectives and reporting lines
- Extending security information and event management to AI
- Implementing secure identity federation for AI services
- Training security operations teams on AI threats
- Conducting AI-focused phishing and social engineering tests
- Updating business continuity plans to include AI failures
- Integrating AI security into third-party risk management
- Sharing threat intelligence on AI-specific attacks
Module 16: Secure Development of Generative AI Applications - Secure prompt engineering principles
- Handling sensitive user data in LLM interactions
- Preventing data leakage through model outputs
- Secure fine-tuning of large language models
- Validation of retrieval-augmented generation (RAG) pipelines
- Securing vector databases and embedding stores
- Privacy-preserving techniques in semantic search
- Monitoring hallucination rates and factual drift
- Controlling access to enterprise knowledge connectors
- Auditing generative outputs for compliance and brand safety
Module 17: Advanced Security Automation and Tooling - Selecting AI security tools: detection, monitoring, enforcement
- Integrating AI security scanners into DevOps workflows
- Automated threat model generation for new AI features
- Policy-as-Code for AI governance enforcement
- Dynamic analysis of AI model behaviour in sandboxed runs
- AI-assisted code review for security vulnerabilities
- Automated model card generation and documentation
- Security posture dashboards for AI portfolios
- Custom rule development for AI-specific SAST tools
- Building secure AI development sandboxes for teams
Module 18: Implementation Projects and Real-World Applications - Securing an AI-powered customer support chatbot
- Implementing a regulated AI underwriting model
- Hardening an internal RAG knowledge assistant
- Deploying a computer vision model in a manufacturing setting
- Integrating external LLM APIs securely with internal data
- Creating a secure AI model marketplace within an enterprise
- Developing a compliance-focused document summarisation tool
- Building an anomaly detection system with adversarial robustness
- Protecting AI-driven supply chain forecasting models
- Designing a secure AI co-pilot for developers
Module 19: Certification, Career Advancement, and Next Steps - Preparing your Certificate of Completion submission
- Compiling a professional portfolio of secure AI documentation
- Highlighting certification on LinkedIn and resumes
- Communicating your achievement to managers and stakeholders
- Positioning yourself for AI security leadership roles
- Advancing into roles: AI Security Lead, ML Security Engineer, AI Governance Officer
- Joining global communities of AI security practitioners
- Accessing advanced resources and reading lists
- Maintaining skills with ongoing update notifications
- Planned pathways to higher-level certifications in AI governance
- Secure coding standards for AI model scripts and notebooks
- Static code analysis for Python, PyTorch, TensorFlow, and JAX
- Preventing secret leakage in AI development repositories
- Managing dependencies with software bill of materials (SBOM)
- Code signing and integrity verification for models and scripts
- Pre-commit hooks and automation for security linting
- Secure handling of notebook-based development in teams
- Version control best practices for AI artifacts (Git-LFS, DVC)
- Isolation of development, staging, and production code branches
- Peer review standards for AI model code
Module 6: Secure Build and Integration Pipelines - Designing secure CI/CD workflows for AI models
- Securing GitHub Actions, GitLab CI, and Jenkins for ML
- Environment hardening for build agents
- Secrets management using Hashicorp Vault and AWS Secrets Manager
- Container security in model build pipelines (Docker, Podman)
- Base image scanning and vulnerability patching
- Immutable pipeline execution and audit trails
- Automating security gates in model integration workflows
- Integration of SAST and SCA tools into AI CI/CD
- Secure handling of model checkpoints and intermediate weights
Module 7: Threat Modeling for AI Systems - Applying PASTA and OCTAVE to AI workloads
- Mapping adversarial AI attack trees
- Identifying high-risk data and model touchpoints
- Attacker persona development for AI-specific threats
- Using attack libraries: MITRE ATLAS and LLM-ATTACK
- Conducting AI-focused pen-testing scoping sessions
- Prioritising threats using DREAD and CARVER matrices
- Model inversion and membership inference risk assessment
- Defending against evasion, poisoning, and backdoor attacks
- Creating mitigating controls from threat model outputs
Module 8: Secure Testing and Validation Frameworks - Designing test suites for model security and robustness
- Adversarial testing: generating perturbed inputs for validation
- Bias and fairness testing across demographic variables
- Stress testing models under abnormal input loads
- Conformance testing against regulatory standards
- Automated security regression testing for AI models
- Conducting red team exercises for AI systems
- Model explainability verification using SHAP, LIME, and feature importance
- Validation of model drift detection thresholds
- Creating audit-ready test documentation packs
Module 9: Secure Deployment and Model Release - Secure production environment provisioning
- Immutable model container deployment strategies
- Canary and blue-green releases for AI models
- Infrastructure as Code (IaC) security: Terraform, CloudFormation
- Role-based access control for model deployment operations
- Model signing and cryptographic attestation (Sigstore, in-toto)
- Audit logging for model activation and configuration changes
- API key rotation and secure service account management
- Secure configuration of inference endpoints
- Runtime protection using eBPF and kernel-level monitoring
Module 10: Runtime Security and Monitoring - Real-time input filtering and anomaly detection
- Monitoring for prompt injection and output manipulation
- Implementing rate limiting and abuse prevention
- Log aggregation and analysis for AI interactions
- Runtime application self-protection (RASP) for AI services
- Model performance and behaviour drift detection
- Alerting on suspicious inference patterns
- Monitoring third-party model API calls and usage
- Secure session management in user-facing AI products
- Integrating AI logs with SIEM and SOAR platforms
Module 11: Incident Response for AI Systems - Developing AI-specific incident playbooks
- Classifying AI incidents: model poisoning, data leak, bias exposure
- Containment procedures for compromised models
- Forensic analysis of model training data and weights
- Preserving chain of custody for AI artifacts
- Coordinating cross-functional response teams
- Notification protocols for AI-related breaches
- Rebuilding and revalidating models post-incident
- Post-mortem analysis and improvement loops
- Regulatory reporting requirements for AI incidents
Module 12: Compliance and Audit Readiness - Mapping controls to ISO/IEC 42001 and NIST AI RMF
- Establishing AI governance documentation frameworks
- Preparing a model inventory and registry
- Documenting data processing assessments (DPIAs) for AI
- Creating model cards and system cards for transparency
- Generating artefacts for internal and external audits
- Aligning AI practices with SOC 2, ISO 27001, and ISO 31000
- Third-party vendor risk assessment for AI suppliers
- Conducting internal AI control assessments
- Maintaining compliance documentation dashboards
Module 13: Secure Model Operations (MLOps) - Implementing secure model lifecycle management
- Automated model retraining with data integrity checks
- Secure handling of model updates and rollbacks
- Model version control and provenance tracking
- Automated drift detection and alerting
- Secure access to model monitoring dashboards
- Configuration drift prevention in AI infrastructure
- Secure model retirement and data erasure procedures
- Maintaining audit logs for operational decisions
- Integrating observability with security monitoring
Module 14: Governance, Risk, and Oversight - Establishing an AI ethics and security review board
- Developing AI risk appetite statements
- Risk ranking AI projects using heat mapping
- Creating escalation paths for high-risk model findings
- Defining oversight roles: AI stewards, guardians, reviewers
- Drafting AI use policies and acceptable use guidelines
- Ensuring board-level reporting on AI risk posture
- Third-party model assurance frameworks
- Conducting periodic AI security maturity assessments
- Building organisational AI security capability roadmaps
Module 15: Integration with Enterprise Security Programs - Embedding AI security into existing cybersecurity frameworks
- Integrating AI risk into enterprise GRC platforms
- Aligning with CISO office objectives and reporting lines
- Extending security information and event management to AI
- Implementing secure identity federation for AI services
- Training security operations teams on AI threats
- Conducting AI-focused phishing and social engineering tests
- Updating business continuity plans to include AI failures
- Integrating AI security into third-party risk management
- Sharing threat intelligence on AI-specific attacks
Module 16: Secure Development of Generative AI Applications - Secure prompt engineering principles
- Handling sensitive user data in LLM interactions
- Preventing data leakage through model outputs
- Secure fine-tuning of large language models
- Validation of retrieval-augmented generation (RAG) pipelines
- Securing vector databases and embedding stores
- Privacy-preserving techniques in semantic search
- Monitoring hallucination rates and factual drift
- Controlling access to enterprise knowledge connectors
- Auditing generative outputs for compliance and brand safety
Module 17: Advanced Security Automation and Tooling - Selecting AI security tools: detection, monitoring, enforcement
- Integrating AI security scanners into DevOps workflows
- Automated threat model generation for new AI features
- Policy-as-Code for AI governance enforcement
- Dynamic analysis of AI model behaviour in sandboxed runs
- AI-assisted code review for security vulnerabilities
- Automated model card generation and documentation
- Security posture dashboards for AI portfolios
- Custom rule development for AI-specific SAST tools
- Building secure AI development sandboxes for teams
Module 18: Implementation Projects and Real-World Applications - Securing an AI-powered customer support chatbot
- Implementing a regulated AI underwriting model
- Hardening an internal RAG knowledge assistant
- Deploying a computer vision model in a manufacturing setting
- Integrating external LLM APIs securely with internal data
- Creating a secure AI model marketplace within an enterprise
- Developing a compliance-focused document summarisation tool
- Building an anomaly detection system with adversarial robustness
- Protecting AI-driven supply chain forecasting models
- Designing a secure AI co-pilot for developers
Module 19: Certification, Career Advancement, and Next Steps - Preparing your Certificate of Completion submission
- Compiling a professional portfolio of secure AI documentation
- Highlighting certification on LinkedIn and resumes
- Communicating your achievement to managers and stakeholders
- Positioning yourself for AI security leadership roles
- Advancing into roles: AI Security Lead, ML Security Engineer, AI Governance Officer
- Joining global communities of AI security practitioners
- Accessing advanced resources and reading lists
- Maintaining skills with ongoing update notifications
- Planned pathways to higher-level certifications in AI governance
- Applying PASTA and OCTAVE to AI workloads
- Mapping adversarial AI attack trees
- Identifying high-risk data and model touchpoints
- Attacker persona development for AI-specific threats
- Using attack libraries: MITRE ATLAS and LLM-ATTACK
- Conducting AI-focused pen-testing scoping sessions
- Prioritising threats using DREAD and CARVER matrices
- Model inversion and membership inference risk assessment
- Defending against evasion, poisoning, and backdoor attacks
- Creating mitigating controls from threat model outputs
Module 8: Secure Testing and Validation Frameworks - Designing test suites for model security and robustness
- Adversarial testing: generating perturbed inputs for validation
- Bias and fairness testing across demographic variables
- Stress testing models under abnormal input loads
- Conformance testing against regulatory standards
- Automated security regression testing for AI models
- Conducting red team exercises for AI systems
- Model explainability verification using SHAP, LIME, and feature importance
- Validation of model drift detection thresholds
- Creating audit-ready test documentation packs
Module 9: Secure Deployment and Model Release - Secure production environment provisioning
- Immutable model container deployment strategies
- Canary and blue-green releases for AI models
- Infrastructure as Code (IaC) security: Terraform, CloudFormation
- Role-based access control for model deployment operations
- Model signing and cryptographic attestation (Sigstore, in-toto)
- Audit logging for model activation and configuration changes
- API key rotation and secure service account management
- Secure configuration of inference endpoints
- Runtime protection using eBPF and kernel-level monitoring
Module 10: Runtime Security and Monitoring - Real-time input filtering and anomaly detection
- Monitoring for prompt injection and output manipulation
- Implementing rate limiting and abuse prevention
- Log aggregation and analysis for AI interactions
- Runtime application self-protection (RASP) for AI services
- Model performance and behaviour drift detection
- Alerting on suspicious inference patterns
- Monitoring third-party model API calls and usage
- Secure session management in user-facing AI products
- Integrating AI logs with SIEM and SOAR platforms
Module 11: Incident Response for AI Systems - Developing AI-specific incident playbooks
- Classifying AI incidents: model poisoning, data leak, bias exposure
- Containment procedures for compromised models
- Forensic analysis of model training data and weights
- Preserving chain of custody for AI artifacts
- Coordinating cross-functional response teams
- Notification protocols for AI-related breaches
- Rebuilding and revalidating models post-incident
- Post-mortem analysis and improvement loops
- Regulatory reporting requirements for AI incidents
Module 12: Compliance and Audit Readiness - Mapping controls to ISO/IEC 42001 and NIST AI RMF
- Establishing AI governance documentation frameworks
- Preparing a model inventory and registry
- Documenting data processing assessments (DPIAs) for AI
- Creating model cards and system cards for transparency
- Generating artefacts for internal and external audits
- Aligning AI practices with SOC 2, ISO 27001, and ISO 31000
- Third-party vendor risk assessment for AI suppliers
- Conducting internal AI control assessments
- Maintaining compliance documentation dashboards
Module 13: Secure Model Operations (MLOps) - Implementing secure model lifecycle management
- Automated model retraining with data integrity checks
- Secure handling of model updates and rollbacks
- Model version control and provenance tracking
- Automated drift detection and alerting
- Secure access to model monitoring dashboards
- Configuration drift prevention in AI infrastructure
- Secure model retirement and data erasure procedures
- Maintaining audit logs for operational decisions
- Integrating observability with security monitoring
Module 14: Governance, Risk, and Oversight - Establishing an AI ethics and security review board
- Developing AI risk appetite statements
- Risk ranking AI projects using heat mapping
- Creating escalation paths for high-risk model findings
- Defining oversight roles: AI stewards, guardians, reviewers
- Drafting AI use policies and acceptable use guidelines
- Ensuring board-level reporting on AI risk posture
- Third-party model assurance frameworks
- Conducting periodic AI security maturity assessments
- Building organisational AI security capability roadmaps
Module 15: Integration with Enterprise Security Programs - Embedding AI security into existing cybersecurity frameworks
- Integrating AI risk into enterprise GRC platforms
- Aligning with CISO office objectives and reporting lines
- Extending security information and event management to AI
- Implementing secure identity federation for AI services
- Training security operations teams on AI threats
- Conducting AI-focused phishing and social engineering tests
- Updating business continuity plans to include AI failures
- Integrating AI security into third-party risk management
- Sharing threat intelligence on AI-specific attacks
Module 16: Secure Development of Generative AI Applications - Secure prompt engineering principles
- Handling sensitive user data in LLM interactions
- Preventing data leakage through model outputs
- Secure fine-tuning of large language models
- Validation of retrieval-augmented generation (RAG) pipelines
- Securing vector databases and embedding stores
- Privacy-preserving techniques in semantic search
- Monitoring hallucination rates and factual drift
- Controlling access to enterprise knowledge connectors
- Auditing generative outputs for compliance and brand safety
Module 17: Advanced Security Automation and Tooling - Selecting AI security tools: detection, monitoring, enforcement
- Integrating AI security scanners into DevOps workflows
- Automated threat model generation for new AI features
- Policy-as-Code for AI governance enforcement
- Dynamic analysis of AI model behaviour in sandboxed runs
- AI-assisted code review for security vulnerabilities
- Automated model card generation and documentation
- Security posture dashboards for AI portfolios
- Custom rule development for AI-specific SAST tools
- Building secure AI development sandboxes for teams
Module 18: Implementation Projects and Real-World Applications - Securing an AI-powered customer support chatbot
- Implementing a regulated AI underwriting model
- Hardening an internal RAG knowledge assistant
- Deploying a computer vision model in a manufacturing setting
- Integrating external LLM APIs securely with internal data
- Creating a secure AI model marketplace within an enterprise
- Developing a compliance-focused document summarisation tool
- Building an anomaly detection system with adversarial robustness
- Protecting AI-driven supply chain forecasting models
- Designing a secure AI co-pilot for developers
Module 19: Certification, Career Advancement, and Next Steps - Preparing your Certificate of Completion submission
- Compiling a professional portfolio of secure AI documentation
- Highlighting certification on LinkedIn and resumes
- Communicating your achievement to managers and stakeholders
- Positioning yourself for AI security leadership roles
- Advancing into roles: AI Security Lead, ML Security Engineer, AI Governance Officer
- Joining global communities of AI security practitioners
- Accessing advanced resources and reading lists
- Maintaining skills with ongoing update notifications
- Planned pathways to higher-level certifications in AI governance
- Secure production environment provisioning
- Immutable model container deployment strategies
- Canary and blue-green releases for AI models
- Infrastructure as Code (IaC) security: Terraform, CloudFormation
- Role-based access control for model deployment operations
- Model signing and cryptographic attestation (Sigstore, in-toto)
- Audit logging for model activation and configuration changes
- API key rotation and secure service account management
- Secure configuration of inference endpoints
- Runtime protection using eBPF and kernel-level monitoring
Module 10: Runtime Security and Monitoring - Real-time input filtering and anomaly detection
- Monitoring for prompt injection and output manipulation
- Implementing rate limiting and abuse prevention
- Log aggregation and analysis for AI interactions
- Runtime application self-protection (RASP) for AI services
- Model performance and behaviour drift detection
- Alerting on suspicious inference patterns
- Monitoring third-party model API calls and usage
- Secure session management in user-facing AI products
- Integrating AI logs with SIEM and SOAR platforms
Module 11: Incident Response for AI Systems - Developing AI-specific incident playbooks
- Classifying AI incidents: model poisoning, data leak, bias exposure
- Containment procedures for compromised models
- Forensic analysis of model training data and weights
- Preserving chain of custody for AI artifacts
- Coordinating cross-functional response teams
- Notification protocols for AI-related breaches
- Rebuilding and revalidating models post-incident
- Post-mortem analysis and improvement loops
- Regulatory reporting requirements for AI incidents
Module 12: Compliance and Audit Readiness - Mapping controls to ISO/IEC 42001 and NIST AI RMF
- Establishing AI governance documentation frameworks
- Preparing a model inventory and registry
- Documenting data processing assessments (DPIAs) for AI
- Creating model cards and system cards for transparency
- Generating artefacts for internal and external audits
- Aligning AI practices with SOC 2, ISO 27001, and ISO 31000
- Third-party vendor risk assessment for AI suppliers
- Conducting internal AI control assessments
- Maintaining compliance documentation dashboards
Module 13: Secure Model Operations (MLOps) - Implementing secure model lifecycle management
- Automated model retraining with data integrity checks
- Secure handling of model updates and rollbacks
- Model version control and provenance tracking
- Automated drift detection and alerting
- Secure access to model monitoring dashboards
- Configuration drift prevention in AI infrastructure
- Secure model retirement and data erasure procedures
- Maintaining audit logs for operational decisions
- Integrating observability with security monitoring
Module 14: Governance, Risk, and Oversight - Establishing an AI ethics and security review board
- Developing AI risk appetite statements
- Risk ranking AI projects using heat mapping
- Creating escalation paths for high-risk model findings
- Defining oversight roles: AI stewards, guardians, reviewers
- Drafting AI use policies and acceptable use guidelines
- Ensuring board-level reporting on AI risk posture
- Third-party model assurance frameworks
- Conducting periodic AI security maturity assessments
- Building organisational AI security capability roadmaps
Module 15: Integration with Enterprise Security Programs - Embedding AI security into existing cybersecurity frameworks
- Integrating AI risk into enterprise GRC platforms
- Aligning with CISO office objectives and reporting lines
- Extending security information and event management to AI
- Implementing secure identity federation for AI services
- Training security operations teams on AI threats
- Conducting AI-focused phishing and social engineering tests
- Updating business continuity plans to include AI failures
- Integrating AI security into third-party risk management
- Sharing threat intelligence on AI-specific attacks
Module 16: Secure Development of Generative AI Applications - Secure prompt engineering principles
- Handling sensitive user data in LLM interactions
- Preventing data leakage through model outputs
- Secure fine-tuning of large language models
- Validation of retrieval-augmented generation (RAG) pipelines
- Securing vector databases and embedding stores
- Privacy-preserving techniques in semantic search
- Monitoring hallucination rates and factual drift
- Controlling access to enterprise knowledge connectors
- Auditing generative outputs for compliance and brand safety
Module 17: Advanced Security Automation and Tooling - Selecting AI security tools: detection, monitoring, enforcement
- Integrating AI security scanners into DevOps workflows
- Automated threat model generation for new AI features
- Policy-as-Code for AI governance enforcement
- Dynamic analysis of AI model behaviour in sandboxed runs
- AI-assisted code review for security vulnerabilities
- Automated model card generation and documentation
- Security posture dashboards for AI portfolios
- Custom rule development for AI-specific SAST tools
- Building secure AI development sandboxes for teams
Module 18: Implementation Projects and Real-World Applications - Securing an AI-powered customer support chatbot
- Implementing a regulated AI underwriting model
- Hardening an internal RAG knowledge assistant
- Deploying a computer vision model in a manufacturing setting
- Integrating external LLM APIs securely with internal data
- Creating a secure AI model marketplace within an enterprise
- Developing a compliance-focused document summarisation tool
- Building an anomaly detection system with adversarial robustness
- Protecting AI-driven supply chain forecasting models
- Designing a secure AI co-pilot for developers
Module 19: Certification, Career Advancement, and Next Steps - Preparing your Certificate of Completion submission
- Compiling a professional portfolio of secure AI documentation
- Highlighting certification on LinkedIn and resumes
- Communicating your achievement to managers and stakeholders
- Positioning yourself for AI security leadership roles
- Advancing into roles: AI Security Lead, ML Security Engineer, AI Governance Officer
- Joining global communities of AI security practitioners
- Accessing advanced resources and reading lists
- Maintaining skills with ongoing update notifications
- Planned pathways to higher-level certifications in AI governance
- Developing AI-specific incident playbooks
- Classifying AI incidents: model poisoning, data leak, bias exposure
- Containment procedures for compromised models
- Forensic analysis of model training data and weights
- Preserving chain of custody for AI artifacts
- Coordinating cross-functional response teams
- Notification protocols for AI-related breaches
- Rebuilding and revalidating models post-incident
- Post-mortem analysis and improvement loops
- Regulatory reporting requirements for AI incidents
Module 12: Compliance and Audit Readiness - Mapping controls to ISO/IEC 42001 and NIST AI RMF
- Establishing AI governance documentation frameworks
- Preparing a model inventory and registry
- Documenting data processing assessments (DPIAs) for AI
- Creating model cards and system cards for transparency
- Generating artefacts for internal and external audits
- Aligning AI practices with SOC 2, ISO 27001, and ISO 31000
- Third-party vendor risk assessment for AI suppliers
- Conducting internal AI control assessments
- Maintaining compliance documentation dashboards
Module 13: Secure Model Operations (MLOps) - Implementing secure model lifecycle management
- Automated model retraining with data integrity checks
- Secure handling of model updates and rollbacks
- Model version control and provenance tracking
- Automated drift detection and alerting
- Secure access to model monitoring dashboards
- Configuration drift prevention in AI infrastructure
- Secure model retirement and data erasure procedures
- Maintaining audit logs for operational decisions
- Integrating observability with security monitoring
Module 14: Governance, Risk, and Oversight - Establishing an AI ethics and security review board
- Developing AI risk appetite statements
- Risk ranking AI projects using heat mapping
- Creating escalation paths for high-risk model findings
- Defining oversight roles: AI stewards, guardians, reviewers
- Drafting AI use policies and acceptable use guidelines
- Ensuring board-level reporting on AI risk posture
- Third-party model assurance frameworks
- Conducting periodic AI security maturity assessments
- Building organisational AI security capability roadmaps
Module 15: Integration with Enterprise Security Programs - Embedding AI security into existing cybersecurity frameworks
- Integrating AI risk into enterprise GRC platforms
- Aligning with CISO office objectives and reporting lines
- Extending security information and event management to AI
- Implementing secure identity federation for AI services
- Training security operations teams on AI threats
- Conducting AI-focused phishing and social engineering tests
- Updating business continuity plans to include AI failures
- Integrating AI security into third-party risk management
- Sharing threat intelligence on AI-specific attacks
Module 16: Secure Development of Generative AI Applications - Secure prompt engineering principles
- Handling sensitive user data in LLM interactions
- Preventing data leakage through model outputs
- Secure fine-tuning of large language models
- Validation of retrieval-augmented generation (RAG) pipelines
- Securing vector databases and embedding stores
- Privacy-preserving techniques in semantic search
- Monitoring hallucination rates and factual drift
- Controlling access to enterprise knowledge connectors
- Auditing generative outputs for compliance and brand safety
Module 17: Advanced Security Automation and Tooling - Selecting AI security tools: detection, monitoring, enforcement
- Integrating AI security scanners into DevOps workflows
- Automated threat model generation for new AI features
- Policy-as-Code for AI governance enforcement
- Dynamic analysis of AI model behaviour in sandboxed runs
- AI-assisted code review for security vulnerabilities
- Automated model card generation and documentation
- Security posture dashboards for AI portfolios
- Custom rule development for AI-specific SAST tools
- Building secure AI development sandboxes for teams
Module 18: Implementation Projects and Real-World Applications - Securing an AI-powered customer support chatbot
- Implementing a regulated AI underwriting model
- Hardening an internal RAG knowledge assistant
- Deploying a computer vision model in a manufacturing setting
- Integrating external LLM APIs securely with internal data
- Creating a secure AI model marketplace within an enterprise
- Developing a compliance-focused document summarisation tool
- Building an anomaly detection system with adversarial robustness
- Protecting AI-driven supply chain forecasting models
- Designing a secure AI co-pilot for developers
Module 19: Certification, Career Advancement, and Next Steps - Preparing your Certificate of Completion submission
- Compiling a professional portfolio of secure AI documentation
- Highlighting certification on LinkedIn and resumes
- Communicating your achievement to managers and stakeholders
- Positioning yourself for AI security leadership roles
- Advancing into roles: AI Security Lead, ML Security Engineer, AI Governance Officer
- Joining global communities of AI security practitioners
- Accessing advanced resources and reading lists
- Maintaining skills with ongoing update notifications
- Planned pathways to higher-level certifications in AI governance
- Implementing secure model lifecycle management
- Automated model retraining with data integrity checks
- Secure handling of model updates and rollbacks
- Model version control and provenance tracking
- Automated drift detection and alerting
- Secure access to model monitoring dashboards
- Configuration drift prevention in AI infrastructure
- Secure model retirement and data erasure procedures
- Maintaining audit logs for operational decisions
- Integrating observability with security monitoring
Module 14: Governance, Risk, and Oversight - Establishing an AI ethics and security review board
- Developing AI risk appetite statements
- Risk ranking AI projects using heat mapping
- Creating escalation paths for high-risk model findings
- Defining oversight roles: AI stewards, guardians, reviewers
- Drafting AI use policies and acceptable use guidelines
- Ensuring board-level reporting on AI risk posture
- Third-party model assurance frameworks
- Conducting periodic AI security maturity assessments
- Building organisational AI security capability roadmaps
Module 15: Integration with Enterprise Security Programs - Embedding AI security into existing cybersecurity frameworks
- Integrating AI risk into enterprise GRC platforms
- Aligning with CISO office objectives and reporting lines
- Extending security information and event management to AI
- Implementing secure identity federation for AI services
- Training security operations teams on AI threats
- Conducting AI-focused phishing and social engineering tests
- Updating business continuity plans to include AI failures
- Integrating AI security into third-party risk management
- Sharing threat intelligence on AI-specific attacks
Module 16: Secure Development of Generative AI Applications - Secure prompt engineering principles
- Handling sensitive user data in LLM interactions
- Preventing data leakage through model outputs
- Secure fine-tuning of large language models
- Validation of retrieval-augmented generation (RAG) pipelines
- Securing vector databases and embedding stores
- Privacy-preserving techniques in semantic search
- Monitoring hallucination rates and factual drift
- Controlling access to enterprise knowledge connectors
- Auditing generative outputs for compliance and brand safety
Module 17: Advanced Security Automation and Tooling - Selecting AI security tools: detection, monitoring, enforcement
- Integrating AI security scanners into DevOps workflows
- Automated threat model generation for new AI features
- Policy-as-Code for AI governance enforcement
- Dynamic analysis of AI model behaviour in sandboxed runs
- AI-assisted code review for security vulnerabilities
- Automated model card generation and documentation
- Security posture dashboards for AI portfolios
- Custom rule development for AI-specific SAST tools
- Building secure AI development sandboxes for teams
Module 18: Implementation Projects and Real-World Applications - Securing an AI-powered customer support chatbot
- Implementing a regulated AI underwriting model
- Hardening an internal RAG knowledge assistant
- Deploying a computer vision model in a manufacturing setting
- Integrating external LLM APIs securely with internal data
- Creating a secure AI model marketplace within an enterprise
- Developing a compliance-focused document summarisation tool
- Building an anomaly detection system with adversarial robustness
- Protecting AI-driven supply chain forecasting models
- Designing a secure AI co-pilot for developers
Module 19: Certification, Career Advancement, and Next Steps - Preparing your Certificate of Completion submission
- Compiling a professional portfolio of secure AI documentation
- Highlighting certification on LinkedIn and resumes
- Communicating your achievement to managers and stakeholders
- Positioning yourself for AI security leadership roles
- Advancing into roles: AI Security Lead, ML Security Engineer, AI Governance Officer
- Joining global communities of AI security practitioners
- Accessing advanced resources and reading lists
- Maintaining skills with ongoing update notifications
- Planned pathways to higher-level certifications in AI governance
- Embedding AI security into existing cybersecurity frameworks
- Integrating AI risk into enterprise GRC platforms
- Aligning with CISO office objectives and reporting lines
- Extending security information and event management to AI
- Implementing secure identity federation for AI services
- Training security operations teams on AI threats
- Conducting AI-focused phishing and social engineering tests
- Updating business continuity plans to include AI failures
- Integrating AI security into third-party risk management
- Sharing threat intelligence on AI-specific attacks
Module 16: Secure Development of Generative AI Applications - Secure prompt engineering principles
- Handling sensitive user data in LLM interactions
- Preventing data leakage through model outputs
- Secure fine-tuning of large language models
- Validation of retrieval-augmented generation (RAG) pipelines
- Securing vector databases and embedding stores
- Privacy-preserving techniques in semantic search
- Monitoring hallucination rates and factual drift
- Controlling access to enterprise knowledge connectors
- Auditing generative outputs for compliance and brand safety
Module 17: Advanced Security Automation and Tooling - Selecting AI security tools: detection, monitoring, enforcement
- Integrating AI security scanners into DevOps workflows
- Automated threat model generation for new AI features
- Policy-as-Code for AI governance enforcement
- Dynamic analysis of AI model behaviour in sandboxed runs
- AI-assisted code review for security vulnerabilities
- Automated model card generation and documentation
- Security posture dashboards for AI portfolios
- Custom rule development for AI-specific SAST tools
- Building secure AI development sandboxes for teams
Module 18: Implementation Projects and Real-World Applications - Securing an AI-powered customer support chatbot
- Implementing a regulated AI underwriting model
- Hardening an internal RAG knowledge assistant
- Deploying a computer vision model in a manufacturing setting
- Integrating external LLM APIs securely with internal data
- Creating a secure AI model marketplace within an enterprise
- Developing a compliance-focused document summarisation tool
- Building an anomaly detection system with adversarial robustness
- Protecting AI-driven supply chain forecasting models
- Designing a secure AI co-pilot for developers
Module 19: Certification, Career Advancement, and Next Steps - Preparing your Certificate of Completion submission
- Compiling a professional portfolio of secure AI documentation
- Highlighting certification on LinkedIn and resumes
- Communicating your achievement to managers and stakeholders
- Positioning yourself for AI security leadership roles
- Advancing into roles: AI Security Lead, ML Security Engineer, AI Governance Officer
- Joining global communities of AI security practitioners
- Accessing advanced resources and reading lists
- Maintaining skills with ongoing update notifications
- Planned pathways to higher-level certifications in AI governance
- Selecting AI security tools: detection, monitoring, enforcement
- Integrating AI security scanners into DevOps workflows
- Automated threat model generation for new AI features
- Policy-as-Code for AI governance enforcement
- Dynamic analysis of AI model behaviour in sandboxed runs
- AI-assisted code review for security vulnerabilities
- Automated model card generation and documentation
- Security posture dashboards for AI portfolios
- Custom rule development for AI-specific SAST tools
- Building secure AI development sandboxes for teams
Module 18: Implementation Projects and Real-World Applications - Securing an AI-powered customer support chatbot
- Implementing a regulated AI underwriting model
- Hardening an internal RAG knowledge assistant
- Deploying a computer vision model in a manufacturing setting
- Integrating external LLM APIs securely with internal data
- Creating a secure AI model marketplace within an enterprise
- Developing a compliance-focused document summarisation tool
- Building an anomaly detection system with adversarial robustness
- Protecting AI-driven supply chain forecasting models
- Designing a secure AI co-pilot for developers
Module 19: Certification, Career Advancement, and Next Steps - Preparing your Certificate of Completion submission
- Compiling a professional portfolio of secure AI documentation
- Highlighting certification on LinkedIn and resumes
- Communicating your achievement to managers and stakeholders
- Positioning yourself for AI security leadership roles
- Advancing into roles: AI Security Lead, ML Security Engineer, AI Governance Officer
- Joining global communities of AI security practitioners
- Accessing advanced resources and reading lists
- Maintaining skills with ongoing update notifications
- Planned pathways to higher-level certifications in AI governance
- Preparing your Certificate of Completion submission
- Compiling a professional portfolio of secure AI documentation
- Highlighting certification on LinkedIn and resumes
- Communicating your achievement to managers and stakeholders
- Positioning yourself for AI security leadership roles
- Advancing into roles: AI Security Lead, ML Security Engineer, AI Governance Officer
- Joining global communities of AI security practitioners
- Accessing advanced resources and reading lists
- Maintaining skills with ongoing update notifications
- Planned pathways to higher-level certifications in AI governance