Mastering Secure AI Development to Prevent Modern Cyber Threats
You're not behind. But the clock is ticking. Every day your organisation deploys AI models without embedded security controls, you’re one vulnerability away from a breach that could cost millions, reputational damage, and regulatory penalties. The threat landscape isn't waiting - and neither are your competitors. You know AI is transforming business, but you also know that without rigorous security by design, every innovation becomes an attack vector. You’re expected to lead, yet you’re wrestling with fragmented knowledge, unclear best practices, and the pressure to deliver fast - without compromising safety. This is where Mastering Secure AI Development to Prevent Modern Cyber Threats changes everything. In just 30 days, you’ll go from uncertainty to confidence - building production-grade AI systems with integrated security protocols that meet compliance standards, withstand penetration testing, and earn board-level trust. You’ll create a board-ready secure AI deployment proposal, complete with risk assessment framework, secure model lifecycle plan, and threat mitigation strategy. Jamal Reed, Principal ML Engineer at a Fortune 500 financial services firm, used this exact methodology to stop a model inversion attack during UAT - a flaw external auditors missed. His team now leads enterprise-wide AI governance, and he was promoted six months after completion. Here’s how this course is structured to help you get there.COURSE FORMAT & DELIVERY DETAILS Self-Paced, Immediate Online Access
This course is designed for professionals like you - busy, mission-critical, and leading high-stakes technology initiatives. That’s why it’s 100% self-paced with on-demand access, so you can progress on your schedule, during flights, after hours, or between sprint cycles - no fixed dates, no mandatory live sessions, no wasted time. Most learners complete the core content in 25 to 30 hours, with tangible results visible within the first two modules - including your first secure AI threat matrix and model hardening checklist. Lifetime Access & Continuous Updates
You’re not buying a temporary pass. You’re investing in a permanent capability. All enrolled learners receive lifetime access to the full course content, including all future updates at zero additional cost. As new AI attack vectors emerge and defensive frameworks evolve, you’ll receive updated materials aligned with NIST, MITRE ATLAS, ISO/IEC 23894, and OWASP Top 10 for LLMs. 24/7 Global Access, Mobile-Friendly Experience
Access your learning materials anytime, anywhere. Whether you're preparing for a red team exercise in Tokyo, auditing models in London, or leading a workshop in New York, the platform is fully responsive across desktop, tablet, and mobile devices. Sync your progress across devices, track completion, and pick up exactly where you left off. Comprehensive Instructor Support
While the course is self-directed, you are never alone. Enrolled learners gain access to structured instructor guidance through curated Q&A pathways, solution walkthroughs, and scenario-based feedback templates. Real-world queries are addressed via protocol-driven support, ensuring you receive clarity without delays. Issued Certificate of Completion – The Art of Service
Upon successful completion, you’ll earn a verifiable Certificate of Completion issued by The Art of Service - a globally recognised leader in professional upskilling, trusted by engineers, auditors, and CISOs in over 147 countries. This credential signals technical mastery, deep compliance insight, and strategic foresight in secure AI - a powerful differentiator on LinkedIn, internal promotions, or client engagements. Transparent Pricing, No Hidden Fees
The price you see is the price you pay - with no recurring charges, hidden subscriptions, or surprise costs. Full access includes all modules, tools, templates, and certification. Period. Secure Payment Options
We accept all major payment methods including Visa, Mastercard, and PayPal - processed through a PCI-compliant gateway to ensure your transaction is private and protected. 100% Satisfaction Guarantee – Enroll Risk-Free
We are so confident in the value of this program that we offer a complete satisfaction guarantee. If you follow the learning path and find it doesn’t meet your expectations, you’re covered by our “satisfied or refunded” promise. Your investment is protected. What Happens After Enrollment?
After registration, you’ll receive a confirmation email. Your access credentials and onboarding instructions will be sent separately as soon as the course materials are prepared and final quality checks are complete. This ensures you receive only polished, production-ready content, vetted by subject matter experts. Will This Work for Me?
Yes - even if you're not a dedicated security specialist. Even if you’ve inherited legacy models with no documentation. Even if your team lacks a unified AI governance policy. This works even if your current toolchain is hybrid, your stakeholders are risk-averse, and your compliance deadline is looming. Juliet Zhou, Senior AI Risk Analyst at a healthcare AI startup, used this course to design a HIPAA-compliant model audit trail system - from scratch - with zero prior cybersecurity certification. Her framework was later adopted across three product lines. This course works because it’s built on real-world implementation patterns, not theory. It hands you actionable artifacts, repeatable workflows, and compliance-grade documentation everyone from auditors to engineering leads will trust.
Module 1: Foundations of Secure AI Development - Understanding the evolving threat landscape for AI and machine learning systems
- Defining secure AI: from model robustness to data integrity and output reliability
- Common misconceptions about AI safety vs AI security
- Key differences between traditional cybersecurity and AI-specific threats
- The role of trustworthiness in AI – accuracy, fairness, transparency, and security
- Regulatory drivers shaping AI security: GDPR, AI Act, NIST AI RMF, and sector-specific mandates
- Mapping AI deployment risks across industries – finance, healthcare, defence, and consumer tech
- Introducing the Secure AI Lifecycle Framework (SAIL-F)
- Threat modeling basics applied to machine learning pipelines
- Common attack surfaces in AI systems: data, training, inference, APIs, and feedback loops
Module 2: Core Principles of AI Threat Prevention - Principle 1: Security by design in AI development
- Principle 2: Least privilege access for model training and inference
- Principle 3: Defence in depth across AI infrastructure layers
- Principle 4: Continuous monitoring and anomaly detection for AI outputs
- Principle 5: Proactive adversarial testing and red teaming
- Establishing threat boundaries in AI architecture
- Identifying trusted vs untrusted components
- Designing secure AI workflows from requirement gathering to deployment
- Data provenance and lineage tracking as a security control
- Secure handling of sensitive training data – PII, PHI, and trade secrets
Module 3: Threat Vectors in Modern AI Systems - Model inversion attacks – extracting training data from model outputs
- Membership inference attacks – determining if a data point was in training set
- Model stealing and replication through API queries
- Training data poisoning: subtle manipulations with catastrophic outcomes
- Label flipping attacks in supervised learning environments
- Backdoor attacks: embedding malicious triggers in model weights
- Adversarial inputs – crafting perturbations to force misclassification
- Jailbreaking in generative AI – bypassing safety filters and guardrails
- Prompt injection attacks in LLM-powered applications
- Replay attacks using captured model responses
- Supply chain risks in pre-trained models and third-party libraries
- Dependency vulnerabilities in AI frameworks like TensorFlow, PyTorch, and Hugging Face
- Exploiting insecure model serialization formats (e.g. pickle files)
- Denoising diffusion models and their unique security challenges
- Reinforcement learning vulnerabilities – reward hacking and policy manipulation
Module 4: Secure Model Development Frameworks - Introducing the NIST AI Risk Management Framework (RMF)
- Mapping AI threats to NIST RMF functions: Govern, Map, Measure, Manage
- Implementing MITRE ATLAS – Adversarial Threat Landscape for AI Systems
- Translating ATLAS tactics into operational checklists
- OWASP Top 10 for Large Language Models – detailed breakdown
- Using ISO/IEC 23894:2023 for AI risk management alignment
- Building custom threat taxonomies for your organisation’s AI portfolio
- Leveraging IEEE P7009 for fail-safe operation of autonomous systems
- Applying SOC 2 Trust Service Criteria to AI systems
- Integrating AI security into DevSecOps practices
- Automating threat detection using static analysis for model code
- Creating AI-specific playbooks for incident response teams
- Establishing AI security policies for model deployment and retirement
- Developing a secure AI development charter for cross-functional teams
- Aligning with industry-specific standards: HIPAA for health AI, GLBA for finance, etc
Module 5: Secure Data Engineering for AI - Data minimisation and anonymisation best practices
- Pseudonymisation vs full anonymisation – tradeoffs and compliance requirements
- Differential privacy: theory and implementable patterns
- Federated learning as a privacy-preserving architecture
- Homomorphic encryption for secure model training
- Secure multi-party computation for collaborative AI
- Designing tamper-proof data pipelines
- Validating data integrity using cryptographic hashing
- Securing ETL processes in large-scale AI training
- Hardening data storage for model training and validation
- Access controls for training datasets – RBAC and ABAC implementation
- Logging and auditing data access activities
- Preventing data leakage during model debugging and logging
- Handling synthetic data securely – risks and safety checks
- Secure data sharing between research and production environments
Module 6: Model Hardening Techniques - Adversarial training to improve model robustness
- Input sanitisation and input validation patterns for AI systems
- Clipping and normalisation as a defence against perturbation attacks
- Defensive distillation in neural networks
- Feature squeezing to reduce model sensitivity
- Randomised smoothing for probabilistic robustness guarantees
- Gradient masking: limitations and alternatives
- Ensemble methods to reduce attack success rates
- Calibration of confidence scores to detect out-of-distribution inputs
- Security implications of model sparsity and pruning
- Quantisation-aware training with security considerations
- Secure fine-tuning of pre-trained models
- Preventing overfitting that exposes training data
- Hardening transformer architectures against attention-based attacks
- Securing embedding layers against reconstruction attempts
Module 7: Secure Deployment Architectures - Containerisation security for AI workloads – Docker, Kubernetes
- Immutable containers and signed images for model deployment
- Network segmentation for AI inference endpoints
- TLS termination and mutual authentication for AI APIs
- Rate limiting and request throttling to prevent abuse
- Secure API gateways for generative AI services
- Zero-trust architecture applied to AI systems
- Securing GPU clusters and AI accelerators
- Protecting model weights in persistent storage
- In-memory protection of model parameters during inference
- Preventing unauthorised model extraction via inference APIs
- Using confidential computing – Intel SGX, AMD SEV, AWS Nitro Enclaves
- Isolating AI services using sandboxed environments
- Securing serverless AI functions (AWS Lambda, Azure Functions)
- Edge AI security – protecting on-device models
Module 8: Monitoring, Detection & Response - Designing observability for AI systems – logs, metrics, traces
- Key performance indicators for AI security health
- Drift detection: data drift, concept drift, and adversarial drift
- Statistical methods for anomaly detection in model outputs
- Real-time monitoring of input-output distributions
- Setting up alerting for abnormal API usage patterns
- Integrating AI systems into SIEM platforms
- Creating AI-specific dashboards in Grafana and Elastic Stack
- Automated rollback triggers for compromised models
- Incident classification guide for AI-related breaches
- Post-mortem analysis for AI security failures
- Threat hunting in AI environments – indicators of compromise
- Conducting AI-focused tabletop exercises
- Creating a model emergency response team (MERT)
- Tracking model lineage during forensic investigations
Module 9: Compliance & Governance Strategies - Developing an AI governance board charter
- Creating model cards for transparency and accountability
- Implementing system cards for AI infrastructure disclosure
- Audit trails for model training, evaluation, and deployment
- Documentation requirements for AI risk assessments
- Establishing AI model inventory and registry
- Version control for datasets, code, and models
- Securing model rollouts with canary deployments
- Implementing model retirement and deprecation policies
- Third-party vendor risk assessment for AI tools
- Contractual clauses for AI security in vendor agreements
- Insurance considerations for AI liability and breach coverage
- Preparing for AI audits by regulators or external firms
- Aligning AI practices with ISO 27001 and SOC 2 controls
- Generating compliance reports for executive leadership
Module 10: Testing & Validation at Scale - Developing a secure AI test plan
- Black-box vs white-box testing for AI models
- Automated testing frameworks for adversarial robustness
- Building a test suite for prompt injection resistance
- Simulating model inversion and membership inference attacks
- Integration testing for AI-powered applications
- Performance vs security tradeoff analysis
- Penetration testing methodology for AI systems
- Hiring and managing external red teams for AI
- Automated fuzzing of AI input channels
- Testing model behaviour under adversarial conditions
- Validating safety guardrails in generative AI
- Benchmarking model resilience against known attack libraries
- Using ART (Adversarial Robustness Toolbox) in test pipelines
- Regression testing for model updates and retraining
Module 11: Secure Generative AI Implementation - Architecture of large language models and their security implications
- Securing prompt engineering pipelines
- Preventing hallucination-driven exposure of sensitive data
- Guardrail implementation for LLM outputs
- Content filtering using classifier chains and rule-based systems
- Context window management to prevent leakage
- Securing RAG (Retrieval-Augmented Generation) systems
- Validating external knowledge sources in RAG pipelines
- Embedding security into agentic AI behaviours
- Preventing unauthorised tool use by AI agents
- Controlling function calling and API access in LLMs
- Monitoring agent decision trees for anomalous chains
- Hardening fine-tuning data for domain-specific LLMs
- Secure deployment of open-source LLMs
- Managing model weights and tokenizers securely
Module 12: Secure Model Operations (MLOps Security) - Integrating security into continuous integration/continuous deployment (CI/CD)
- Automated scanning of model code for vulnerabilities
- Static application security testing (SAST) for ML scripts
- Dynamic analysis of model behaviour in staging
- Secrets management for API keys and credentials
- Secure handling of environment variables in MLOps
- Role-based access control (RBAC) in ML platforms
- Audit logging for model retraining and redeployment
- Immutable pipelines to prevent configuration drift
- Secure model registry design patterns
- Signing and verifying model releases
- Blue-green deployments for zero-downtime secure updates
- Feature store security – protecting shared features
- Monitoring model performance degradation as a security signal
- Automated revocation of compromised model endpoints
Module 13: Organisational AI Security Culture - Building a cross-functional AI security task force
- Training developers, data scientists, and product managers
- Creating secure AI coding standards and style guides
- Developing internal AI security champions network
- Running secure AI workshops and threat modeling sessions
- Communicating AI risks to non-technical stakeholders
- Creating executive briefings on AI security posture
- Designing AI incident communication protocols
- Establishing whistleblower channels for AI misuse
- Conducting AI security awareness campaigns
- Aligning incentives with secure development behaviour
- Measuring cultural maturity in AI security practices
- Onboarding checklist for new hires working with AI
- External communication strategy for AI security breaches
- Handling media and public relations during AI incidents
Module 14: Certification & Career Advancement - How to use your Certificate of Completion effectively
- Adding AI security credentials to LinkedIn and résumés
- Positioning yourself as a secure AI leader in job interviews
- Contributing to open-source secure AI projects
- Speaking at conferences on AI security topics
- Writing technical blogs to demonstrate expertise
- Negotiating higher compensation with specialised skills
- Transitioning from developer to AI security architect
- Preparing for advanced certifications in AI governance
- Joining professional networks for AI security
- Contributing to AI standards bodies and policy groups
- Staying updated on emerging AI attack patterns
- Accessing exclusive resources from The Art of Service
- Invitations to private forums for certified professionals
- Next-step learning paths in AI red teaming, policy, and audit
- Understanding the evolving threat landscape for AI and machine learning systems
- Defining secure AI: from model robustness to data integrity and output reliability
- Common misconceptions about AI safety vs AI security
- Key differences between traditional cybersecurity and AI-specific threats
- The role of trustworthiness in AI – accuracy, fairness, transparency, and security
- Regulatory drivers shaping AI security: GDPR, AI Act, NIST AI RMF, and sector-specific mandates
- Mapping AI deployment risks across industries – finance, healthcare, defence, and consumer tech
- Introducing the Secure AI Lifecycle Framework (SAIL-F)
- Threat modeling basics applied to machine learning pipelines
- Common attack surfaces in AI systems: data, training, inference, APIs, and feedback loops
Module 2: Core Principles of AI Threat Prevention - Principle 1: Security by design in AI development
- Principle 2: Least privilege access for model training and inference
- Principle 3: Defence in depth across AI infrastructure layers
- Principle 4: Continuous monitoring and anomaly detection for AI outputs
- Principle 5: Proactive adversarial testing and red teaming
- Establishing threat boundaries in AI architecture
- Identifying trusted vs untrusted components
- Designing secure AI workflows from requirement gathering to deployment
- Data provenance and lineage tracking as a security control
- Secure handling of sensitive training data – PII, PHI, and trade secrets
Module 3: Threat Vectors in Modern AI Systems - Model inversion attacks – extracting training data from model outputs
- Membership inference attacks – determining if a data point was in training set
- Model stealing and replication through API queries
- Training data poisoning: subtle manipulations with catastrophic outcomes
- Label flipping attacks in supervised learning environments
- Backdoor attacks: embedding malicious triggers in model weights
- Adversarial inputs – crafting perturbations to force misclassification
- Jailbreaking in generative AI – bypassing safety filters and guardrails
- Prompt injection attacks in LLM-powered applications
- Replay attacks using captured model responses
- Supply chain risks in pre-trained models and third-party libraries
- Dependency vulnerabilities in AI frameworks like TensorFlow, PyTorch, and Hugging Face
- Exploiting insecure model serialization formats (e.g. pickle files)
- Denoising diffusion models and their unique security challenges
- Reinforcement learning vulnerabilities – reward hacking and policy manipulation
Module 4: Secure Model Development Frameworks - Introducing the NIST AI Risk Management Framework (RMF)
- Mapping AI threats to NIST RMF functions: Govern, Map, Measure, Manage
- Implementing MITRE ATLAS – Adversarial Threat Landscape for AI Systems
- Translating ATLAS tactics into operational checklists
- OWASP Top 10 for Large Language Models – detailed breakdown
- Using ISO/IEC 23894:2023 for AI risk management alignment
- Building custom threat taxonomies for your organisation’s AI portfolio
- Leveraging IEEE P7009 for fail-safe operation of autonomous systems
- Applying SOC 2 Trust Service Criteria to AI systems
- Integrating AI security into DevSecOps practices
- Automating threat detection using static analysis for model code
- Creating AI-specific playbooks for incident response teams
- Establishing AI security policies for model deployment and retirement
- Developing a secure AI development charter for cross-functional teams
- Aligning with industry-specific standards: HIPAA for health AI, GLBA for finance, etc
Module 5: Secure Data Engineering for AI - Data minimisation and anonymisation best practices
- Pseudonymisation vs full anonymisation – tradeoffs and compliance requirements
- Differential privacy: theory and implementable patterns
- Federated learning as a privacy-preserving architecture
- Homomorphic encryption for secure model training
- Secure multi-party computation for collaborative AI
- Designing tamper-proof data pipelines
- Validating data integrity using cryptographic hashing
- Securing ETL processes in large-scale AI training
- Hardening data storage for model training and validation
- Access controls for training datasets – RBAC and ABAC implementation
- Logging and auditing data access activities
- Preventing data leakage during model debugging and logging
- Handling synthetic data securely – risks and safety checks
- Secure data sharing between research and production environments
Module 6: Model Hardening Techniques - Adversarial training to improve model robustness
- Input sanitisation and input validation patterns for AI systems
- Clipping and normalisation as a defence against perturbation attacks
- Defensive distillation in neural networks
- Feature squeezing to reduce model sensitivity
- Randomised smoothing for probabilistic robustness guarantees
- Gradient masking: limitations and alternatives
- Ensemble methods to reduce attack success rates
- Calibration of confidence scores to detect out-of-distribution inputs
- Security implications of model sparsity and pruning
- Quantisation-aware training with security considerations
- Secure fine-tuning of pre-trained models
- Preventing overfitting that exposes training data
- Hardening transformer architectures against attention-based attacks
- Securing embedding layers against reconstruction attempts
Module 7: Secure Deployment Architectures - Containerisation security for AI workloads – Docker, Kubernetes
- Immutable containers and signed images for model deployment
- Network segmentation for AI inference endpoints
- TLS termination and mutual authentication for AI APIs
- Rate limiting and request throttling to prevent abuse
- Secure API gateways for generative AI services
- Zero-trust architecture applied to AI systems
- Securing GPU clusters and AI accelerators
- Protecting model weights in persistent storage
- In-memory protection of model parameters during inference
- Preventing unauthorised model extraction via inference APIs
- Using confidential computing – Intel SGX, AMD SEV, AWS Nitro Enclaves
- Isolating AI services using sandboxed environments
- Securing serverless AI functions (AWS Lambda, Azure Functions)
- Edge AI security – protecting on-device models
Module 8: Monitoring, Detection & Response - Designing observability for AI systems – logs, metrics, traces
- Key performance indicators for AI security health
- Drift detection: data drift, concept drift, and adversarial drift
- Statistical methods for anomaly detection in model outputs
- Real-time monitoring of input-output distributions
- Setting up alerting for abnormal API usage patterns
- Integrating AI systems into SIEM platforms
- Creating AI-specific dashboards in Grafana and Elastic Stack
- Automated rollback triggers for compromised models
- Incident classification guide for AI-related breaches
- Post-mortem analysis for AI security failures
- Threat hunting in AI environments – indicators of compromise
- Conducting AI-focused tabletop exercises
- Creating a model emergency response team (MERT)
- Tracking model lineage during forensic investigations
Module 9: Compliance & Governance Strategies - Developing an AI governance board charter
- Creating model cards for transparency and accountability
- Implementing system cards for AI infrastructure disclosure
- Audit trails for model training, evaluation, and deployment
- Documentation requirements for AI risk assessments
- Establishing AI model inventory and registry
- Version control for datasets, code, and models
- Securing model rollouts with canary deployments
- Implementing model retirement and deprecation policies
- Third-party vendor risk assessment for AI tools
- Contractual clauses for AI security in vendor agreements
- Insurance considerations for AI liability and breach coverage
- Preparing for AI audits by regulators or external firms
- Aligning AI practices with ISO 27001 and SOC 2 controls
- Generating compliance reports for executive leadership
Module 10: Testing & Validation at Scale - Developing a secure AI test plan
- Black-box vs white-box testing for AI models
- Automated testing frameworks for adversarial robustness
- Building a test suite for prompt injection resistance
- Simulating model inversion and membership inference attacks
- Integration testing for AI-powered applications
- Performance vs security tradeoff analysis
- Penetration testing methodology for AI systems
- Hiring and managing external red teams for AI
- Automated fuzzing of AI input channels
- Testing model behaviour under adversarial conditions
- Validating safety guardrails in generative AI
- Benchmarking model resilience against known attack libraries
- Using ART (Adversarial Robustness Toolbox) in test pipelines
- Regression testing for model updates and retraining
Module 11: Secure Generative AI Implementation - Architecture of large language models and their security implications
- Securing prompt engineering pipelines
- Preventing hallucination-driven exposure of sensitive data
- Guardrail implementation for LLM outputs
- Content filtering using classifier chains and rule-based systems
- Context window management to prevent leakage
- Securing RAG (Retrieval-Augmented Generation) systems
- Validating external knowledge sources in RAG pipelines
- Embedding security into agentic AI behaviours
- Preventing unauthorised tool use by AI agents
- Controlling function calling and API access in LLMs
- Monitoring agent decision trees for anomalous chains
- Hardening fine-tuning data for domain-specific LLMs
- Secure deployment of open-source LLMs
- Managing model weights and tokenizers securely
Module 12: Secure Model Operations (MLOps Security) - Integrating security into continuous integration/continuous deployment (CI/CD)
- Automated scanning of model code for vulnerabilities
- Static application security testing (SAST) for ML scripts
- Dynamic analysis of model behaviour in staging
- Secrets management for API keys and credentials
- Secure handling of environment variables in MLOps
- Role-based access control (RBAC) in ML platforms
- Audit logging for model retraining and redeployment
- Immutable pipelines to prevent configuration drift
- Secure model registry design patterns
- Signing and verifying model releases
- Blue-green deployments for zero-downtime secure updates
- Feature store security – protecting shared features
- Monitoring model performance degradation as a security signal
- Automated revocation of compromised model endpoints
Module 13: Organisational AI Security Culture - Building a cross-functional AI security task force
- Training developers, data scientists, and product managers
- Creating secure AI coding standards and style guides
- Developing internal AI security champions network
- Running secure AI workshops and threat modeling sessions
- Communicating AI risks to non-technical stakeholders
- Creating executive briefings on AI security posture
- Designing AI incident communication protocols
- Establishing whistleblower channels for AI misuse
- Conducting AI security awareness campaigns
- Aligning incentives with secure development behaviour
- Measuring cultural maturity in AI security practices
- Onboarding checklist for new hires working with AI
- External communication strategy for AI security breaches
- Handling media and public relations during AI incidents
Module 14: Certification & Career Advancement - How to use your Certificate of Completion effectively
- Adding AI security credentials to LinkedIn and résumés
- Positioning yourself as a secure AI leader in job interviews
- Contributing to open-source secure AI projects
- Speaking at conferences on AI security topics
- Writing technical blogs to demonstrate expertise
- Negotiating higher compensation with specialised skills
- Transitioning from developer to AI security architect
- Preparing for advanced certifications in AI governance
- Joining professional networks for AI security
- Contributing to AI standards bodies and policy groups
- Staying updated on emerging AI attack patterns
- Accessing exclusive resources from The Art of Service
- Invitations to private forums for certified professionals
- Next-step learning paths in AI red teaming, policy, and audit
- Model inversion attacks – extracting training data from model outputs
- Membership inference attacks – determining if a data point was in training set
- Model stealing and replication through API queries
- Training data poisoning: subtle manipulations with catastrophic outcomes
- Label flipping attacks in supervised learning environments
- Backdoor attacks: embedding malicious triggers in model weights
- Adversarial inputs – crafting perturbations to force misclassification
- Jailbreaking in generative AI – bypassing safety filters and guardrails
- Prompt injection attacks in LLM-powered applications
- Replay attacks using captured model responses
- Supply chain risks in pre-trained models and third-party libraries
- Dependency vulnerabilities in AI frameworks like TensorFlow, PyTorch, and Hugging Face
- Exploiting insecure model serialization formats (e.g. pickle files)
- Denoising diffusion models and their unique security challenges
- Reinforcement learning vulnerabilities – reward hacking and policy manipulation
Module 4: Secure Model Development Frameworks - Introducing the NIST AI Risk Management Framework (RMF)
- Mapping AI threats to NIST RMF functions: Govern, Map, Measure, Manage
- Implementing MITRE ATLAS – Adversarial Threat Landscape for AI Systems
- Translating ATLAS tactics into operational checklists
- OWASP Top 10 for Large Language Models – detailed breakdown
- Using ISO/IEC 23894:2023 for AI risk management alignment
- Building custom threat taxonomies for your organisation’s AI portfolio
- Leveraging IEEE P7009 for fail-safe operation of autonomous systems
- Applying SOC 2 Trust Service Criteria to AI systems
- Integrating AI security into DevSecOps practices
- Automating threat detection using static analysis for model code
- Creating AI-specific playbooks for incident response teams
- Establishing AI security policies for model deployment and retirement
- Developing a secure AI development charter for cross-functional teams
- Aligning with industry-specific standards: HIPAA for health AI, GLBA for finance, etc
Module 5: Secure Data Engineering for AI - Data minimisation and anonymisation best practices
- Pseudonymisation vs full anonymisation – tradeoffs and compliance requirements
- Differential privacy: theory and implementable patterns
- Federated learning as a privacy-preserving architecture
- Homomorphic encryption for secure model training
- Secure multi-party computation for collaborative AI
- Designing tamper-proof data pipelines
- Validating data integrity using cryptographic hashing
- Securing ETL processes in large-scale AI training
- Hardening data storage for model training and validation
- Access controls for training datasets – RBAC and ABAC implementation
- Logging and auditing data access activities
- Preventing data leakage during model debugging and logging
- Handling synthetic data securely – risks and safety checks
- Secure data sharing between research and production environments
Module 6: Model Hardening Techniques - Adversarial training to improve model robustness
- Input sanitisation and input validation patterns for AI systems
- Clipping and normalisation as a defence against perturbation attacks
- Defensive distillation in neural networks
- Feature squeezing to reduce model sensitivity
- Randomised smoothing for probabilistic robustness guarantees
- Gradient masking: limitations and alternatives
- Ensemble methods to reduce attack success rates
- Calibration of confidence scores to detect out-of-distribution inputs
- Security implications of model sparsity and pruning
- Quantisation-aware training with security considerations
- Secure fine-tuning of pre-trained models
- Preventing overfitting that exposes training data
- Hardening transformer architectures against attention-based attacks
- Securing embedding layers against reconstruction attempts
Module 7: Secure Deployment Architectures - Containerisation security for AI workloads – Docker, Kubernetes
- Immutable containers and signed images for model deployment
- Network segmentation for AI inference endpoints
- TLS termination and mutual authentication for AI APIs
- Rate limiting and request throttling to prevent abuse
- Secure API gateways for generative AI services
- Zero-trust architecture applied to AI systems
- Securing GPU clusters and AI accelerators
- Protecting model weights in persistent storage
- In-memory protection of model parameters during inference
- Preventing unauthorised model extraction via inference APIs
- Using confidential computing – Intel SGX, AMD SEV, AWS Nitro Enclaves
- Isolating AI services using sandboxed environments
- Securing serverless AI functions (AWS Lambda, Azure Functions)
- Edge AI security – protecting on-device models
Module 8: Monitoring, Detection & Response - Designing observability for AI systems – logs, metrics, traces
- Key performance indicators for AI security health
- Drift detection: data drift, concept drift, and adversarial drift
- Statistical methods for anomaly detection in model outputs
- Real-time monitoring of input-output distributions
- Setting up alerting for abnormal API usage patterns
- Integrating AI systems into SIEM platforms
- Creating AI-specific dashboards in Grafana and Elastic Stack
- Automated rollback triggers for compromised models
- Incident classification guide for AI-related breaches
- Post-mortem analysis for AI security failures
- Threat hunting in AI environments – indicators of compromise
- Conducting AI-focused tabletop exercises
- Creating a model emergency response team (MERT)
- Tracking model lineage during forensic investigations
Module 9: Compliance & Governance Strategies - Developing an AI governance board charter
- Creating model cards for transparency and accountability
- Implementing system cards for AI infrastructure disclosure
- Audit trails for model training, evaluation, and deployment
- Documentation requirements for AI risk assessments
- Establishing AI model inventory and registry
- Version control for datasets, code, and models
- Securing model rollouts with canary deployments
- Implementing model retirement and deprecation policies
- Third-party vendor risk assessment for AI tools
- Contractual clauses for AI security in vendor agreements
- Insurance considerations for AI liability and breach coverage
- Preparing for AI audits by regulators or external firms
- Aligning AI practices with ISO 27001 and SOC 2 controls
- Generating compliance reports for executive leadership
Module 10: Testing & Validation at Scale - Developing a secure AI test plan
- Black-box vs white-box testing for AI models
- Automated testing frameworks for adversarial robustness
- Building a test suite for prompt injection resistance
- Simulating model inversion and membership inference attacks
- Integration testing for AI-powered applications
- Performance vs security tradeoff analysis
- Penetration testing methodology for AI systems
- Hiring and managing external red teams for AI
- Automated fuzzing of AI input channels
- Testing model behaviour under adversarial conditions
- Validating safety guardrails in generative AI
- Benchmarking model resilience against known attack libraries
- Using ART (Adversarial Robustness Toolbox) in test pipelines
- Regression testing for model updates and retraining
Module 11: Secure Generative AI Implementation - Architecture of large language models and their security implications
- Securing prompt engineering pipelines
- Preventing hallucination-driven exposure of sensitive data
- Guardrail implementation for LLM outputs
- Content filtering using classifier chains and rule-based systems
- Context window management to prevent leakage
- Securing RAG (Retrieval-Augmented Generation) systems
- Validating external knowledge sources in RAG pipelines
- Embedding security into agentic AI behaviours
- Preventing unauthorised tool use by AI agents
- Controlling function calling and API access in LLMs
- Monitoring agent decision trees for anomalous chains
- Hardening fine-tuning data for domain-specific LLMs
- Secure deployment of open-source LLMs
- Managing model weights and tokenizers securely
Module 12: Secure Model Operations (MLOps Security) - Integrating security into continuous integration/continuous deployment (CI/CD)
- Automated scanning of model code for vulnerabilities
- Static application security testing (SAST) for ML scripts
- Dynamic analysis of model behaviour in staging
- Secrets management for API keys and credentials
- Secure handling of environment variables in MLOps
- Role-based access control (RBAC) in ML platforms
- Audit logging for model retraining and redeployment
- Immutable pipelines to prevent configuration drift
- Secure model registry design patterns
- Signing and verifying model releases
- Blue-green deployments for zero-downtime secure updates
- Feature store security – protecting shared features
- Monitoring model performance degradation as a security signal
- Automated revocation of compromised model endpoints
Module 13: Organisational AI Security Culture - Building a cross-functional AI security task force
- Training developers, data scientists, and product managers
- Creating secure AI coding standards and style guides
- Developing internal AI security champions network
- Running secure AI workshops and threat modeling sessions
- Communicating AI risks to non-technical stakeholders
- Creating executive briefings on AI security posture
- Designing AI incident communication protocols
- Establishing whistleblower channels for AI misuse
- Conducting AI security awareness campaigns
- Aligning incentives with secure development behaviour
- Measuring cultural maturity in AI security practices
- Onboarding checklist for new hires working with AI
- External communication strategy for AI security breaches
- Handling media and public relations during AI incidents
Module 14: Certification & Career Advancement - How to use your Certificate of Completion effectively
- Adding AI security credentials to LinkedIn and résumés
- Positioning yourself as a secure AI leader in job interviews
- Contributing to open-source secure AI projects
- Speaking at conferences on AI security topics
- Writing technical blogs to demonstrate expertise
- Negotiating higher compensation with specialised skills
- Transitioning from developer to AI security architect
- Preparing for advanced certifications in AI governance
- Joining professional networks for AI security
- Contributing to AI standards bodies and policy groups
- Staying updated on emerging AI attack patterns
- Accessing exclusive resources from The Art of Service
- Invitations to private forums for certified professionals
- Next-step learning paths in AI red teaming, policy, and audit
- Data minimisation and anonymisation best practices
- Pseudonymisation vs full anonymisation – tradeoffs and compliance requirements
- Differential privacy: theory and implementable patterns
- Federated learning as a privacy-preserving architecture
- Homomorphic encryption for secure model training
- Secure multi-party computation for collaborative AI
- Designing tamper-proof data pipelines
- Validating data integrity using cryptographic hashing
- Securing ETL processes in large-scale AI training
- Hardening data storage for model training and validation
- Access controls for training datasets – RBAC and ABAC implementation
- Logging and auditing data access activities
- Preventing data leakage during model debugging and logging
- Handling synthetic data securely – risks and safety checks
- Secure data sharing between research and production environments
Module 6: Model Hardening Techniques - Adversarial training to improve model robustness
- Input sanitisation and input validation patterns for AI systems
- Clipping and normalisation as a defence against perturbation attacks
- Defensive distillation in neural networks
- Feature squeezing to reduce model sensitivity
- Randomised smoothing for probabilistic robustness guarantees
- Gradient masking: limitations and alternatives
- Ensemble methods to reduce attack success rates
- Calibration of confidence scores to detect out-of-distribution inputs
- Security implications of model sparsity and pruning
- Quantisation-aware training with security considerations
- Secure fine-tuning of pre-trained models
- Preventing overfitting that exposes training data
- Hardening transformer architectures against attention-based attacks
- Securing embedding layers against reconstruction attempts
Module 7: Secure Deployment Architectures - Containerisation security for AI workloads – Docker, Kubernetes
- Immutable containers and signed images for model deployment
- Network segmentation for AI inference endpoints
- TLS termination and mutual authentication for AI APIs
- Rate limiting and request throttling to prevent abuse
- Secure API gateways for generative AI services
- Zero-trust architecture applied to AI systems
- Securing GPU clusters and AI accelerators
- Protecting model weights in persistent storage
- In-memory protection of model parameters during inference
- Preventing unauthorised model extraction via inference APIs
- Using confidential computing – Intel SGX, AMD SEV, AWS Nitro Enclaves
- Isolating AI services using sandboxed environments
- Securing serverless AI functions (AWS Lambda, Azure Functions)
- Edge AI security – protecting on-device models
Module 8: Monitoring, Detection & Response - Designing observability for AI systems – logs, metrics, traces
- Key performance indicators for AI security health
- Drift detection: data drift, concept drift, and adversarial drift
- Statistical methods for anomaly detection in model outputs
- Real-time monitoring of input-output distributions
- Setting up alerting for abnormal API usage patterns
- Integrating AI systems into SIEM platforms
- Creating AI-specific dashboards in Grafana and Elastic Stack
- Automated rollback triggers for compromised models
- Incident classification guide for AI-related breaches
- Post-mortem analysis for AI security failures
- Threat hunting in AI environments – indicators of compromise
- Conducting AI-focused tabletop exercises
- Creating a model emergency response team (MERT)
- Tracking model lineage during forensic investigations
Module 9: Compliance & Governance Strategies - Developing an AI governance board charter
- Creating model cards for transparency and accountability
- Implementing system cards for AI infrastructure disclosure
- Audit trails for model training, evaluation, and deployment
- Documentation requirements for AI risk assessments
- Establishing AI model inventory and registry
- Version control for datasets, code, and models
- Securing model rollouts with canary deployments
- Implementing model retirement and deprecation policies
- Third-party vendor risk assessment for AI tools
- Contractual clauses for AI security in vendor agreements
- Insurance considerations for AI liability and breach coverage
- Preparing for AI audits by regulators or external firms
- Aligning AI practices with ISO 27001 and SOC 2 controls
- Generating compliance reports for executive leadership
Module 10: Testing & Validation at Scale - Developing a secure AI test plan
- Black-box vs white-box testing for AI models
- Automated testing frameworks for adversarial robustness
- Building a test suite for prompt injection resistance
- Simulating model inversion and membership inference attacks
- Integration testing for AI-powered applications
- Performance vs security tradeoff analysis
- Penetration testing methodology for AI systems
- Hiring and managing external red teams for AI
- Automated fuzzing of AI input channels
- Testing model behaviour under adversarial conditions
- Validating safety guardrails in generative AI
- Benchmarking model resilience against known attack libraries
- Using ART (Adversarial Robustness Toolbox) in test pipelines
- Regression testing for model updates and retraining
Module 11: Secure Generative AI Implementation - Architecture of large language models and their security implications
- Securing prompt engineering pipelines
- Preventing hallucination-driven exposure of sensitive data
- Guardrail implementation for LLM outputs
- Content filtering using classifier chains and rule-based systems
- Context window management to prevent leakage
- Securing RAG (Retrieval-Augmented Generation) systems
- Validating external knowledge sources in RAG pipelines
- Embedding security into agentic AI behaviours
- Preventing unauthorised tool use by AI agents
- Controlling function calling and API access in LLMs
- Monitoring agent decision trees for anomalous chains
- Hardening fine-tuning data for domain-specific LLMs
- Secure deployment of open-source LLMs
- Managing model weights and tokenizers securely
Module 12: Secure Model Operations (MLOps Security) - Integrating security into continuous integration/continuous deployment (CI/CD)
- Automated scanning of model code for vulnerabilities
- Static application security testing (SAST) for ML scripts
- Dynamic analysis of model behaviour in staging
- Secrets management for API keys and credentials
- Secure handling of environment variables in MLOps
- Role-based access control (RBAC) in ML platforms
- Audit logging for model retraining and redeployment
- Immutable pipelines to prevent configuration drift
- Secure model registry design patterns
- Signing and verifying model releases
- Blue-green deployments for zero-downtime secure updates
- Feature store security – protecting shared features
- Monitoring model performance degradation as a security signal
- Automated revocation of compromised model endpoints
Module 13: Organisational AI Security Culture - Building a cross-functional AI security task force
- Training developers, data scientists, and product managers
- Creating secure AI coding standards and style guides
- Developing internal AI security champions network
- Running secure AI workshops and threat modeling sessions
- Communicating AI risks to non-technical stakeholders
- Creating executive briefings on AI security posture
- Designing AI incident communication protocols
- Establishing whistleblower channels for AI misuse
- Conducting AI security awareness campaigns
- Aligning incentives with secure development behaviour
- Measuring cultural maturity in AI security practices
- Onboarding checklist for new hires working with AI
- External communication strategy for AI security breaches
- Handling media and public relations during AI incidents
Module 14: Certification & Career Advancement - How to use your Certificate of Completion effectively
- Adding AI security credentials to LinkedIn and résumés
- Positioning yourself as a secure AI leader in job interviews
- Contributing to open-source secure AI projects
- Speaking at conferences on AI security topics
- Writing technical blogs to demonstrate expertise
- Negotiating higher compensation with specialised skills
- Transitioning from developer to AI security architect
- Preparing for advanced certifications in AI governance
- Joining professional networks for AI security
- Contributing to AI standards bodies and policy groups
- Staying updated on emerging AI attack patterns
- Accessing exclusive resources from The Art of Service
- Invitations to private forums for certified professionals
- Next-step learning paths in AI red teaming, policy, and audit
- Containerisation security for AI workloads – Docker, Kubernetes
- Immutable containers and signed images for model deployment
- Network segmentation for AI inference endpoints
- TLS termination and mutual authentication for AI APIs
- Rate limiting and request throttling to prevent abuse
- Secure API gateways for generative AI services
- Zero-trust architecture applied to AI systems
- Securing GPU clusters and AI accelerators
- Protecting model weights in persistent storage
- In-memory protection of model parameters during inference
- Preventing unauthorised model extraction via inference APIs
- Using confidential computing – Intel SGX, AMD SEV, AWS Nitro Enclaves
- Isolating AI services using sandboxed environments
- Securing serverless AI functions (AWS Lambda, Azure Functions)
- Edge AI security – protecting on-device models
Module 8: Monitoring, Detection & Response - Designing observability for AI systems – logs, metrics, traces
- Key performance indicators for AI security health
- Drift detection: data drift, concept drift, and adversarial drift
- Statistical methods for anomaly detection in model outputs
- Real-time monitoring of input-output distributions
- Setting up alerting for abnormal API usage patterns
- Integrating AI systems into SIEM platforms
- Creating AI-specific dashboards in Grafana and Elastic Stack
- Automated rollback triggers for compromised models
- Incident classification guide for AI-related breaches
- Post-mortem analysis for AI security failures
- Threat hunting in AI environments – indicators of compromise
- Conducting AI-focused tabletop exercises
- Creating a model emergency response team (MERT)
- Tracking model lineage during forensic investigations
Module 9: Compliance & Governance Strategies - Developing an AI governance board charter
- Creating model cards for transparency and accountability
- Implementing system cards for AI infrastructure disclosure
- Audit trails for model training, evaluation, and deployment
- Documentation requirements for AI risk assessments
- Establishing AI model inventory and registry
- Version control for datasets, code, and models
- Securing model rollouts with canary deployments
- Implementing model retirement and deprecation policies
- Third-party vendor risk assessment for AI tools
- Contractual clauses for AI security in vendor agreements
- Insurance considerations for AI liability and breach coverage
- Preparing for AI audits by regulators or external firms
- Aligning AI practices with ISO 27001 and SOC 2 controls
- Generating compliance reports for executive leadership
Module 10: Testing & Validation at Scale - Developing a secure AI test plan
- Black-box vs white-box testing for AI models
- Automated testing frameworks for adversarial robustness
- Building a test suite for prompt injection resistance
- Simulating model inversion and membership inference attacks
- Integration testing for AI-powered applications
- Performance vs security tradeoff analysis
- Penetration testing methodology for AI systems
- Hiring and managing external red teams for AI
- Automated fuzzing of AI input channels
- Testing model behaviour under adversarial conditions
- Validating safety guardrails in generative AI
- Benchmarking model resilience against known attack libraries
- Using ART (Adversarial Robustness Toolbox) in test pipelines
- Regression testing for model updates and retraining
Module 11: Secure Generative AI Implementation - Architecture of large language models and their security implications
- Securing prompt engineering pipelines
- Preventing hallucination-driven exposure of sensitive data
- Guardrail implementation for LLM outputs
- Content filtering using classifier chains and rule-based systems
- Context window management to prevent leakage
- Securing RAG (Retrieval-Augmented Generation) systems
- Validating external knowledge sources in RAG pipelines
- Embedding security into agentic AI behaviours
- Preventing unauthorised tool use by AI agents
- Controlling function calling and API access in LLMs
- Monitoring agent decision trees for anomalous chains
- Hardening fine-tuning data for domain-specific LLMs
- Secure deployment of open-source LLMs
- Managing model weights and tokenizers securely
Module 12: Secure Model Operations (MLOps Security) - Integrating security into continuous integration/continuous deployment (CI/CD)
- Automated scanning of model code for vulnerabilities
- Static application security testing (SAST) for ML scripts
- Dynamic analysis of model behaviour in staging
- Secrets management for API keys and credentials
- Secure handling of environment variables in MLOps
- Role-based access control (RBAC) in ML platforms
- Audit logging for model retraining and redeployment
- Immutable pipelines to prevent configuration drift
- Secure model registry design patterns
- Signing and verifying model releases
- Blue-green deployments for zero-downtime secure updates
- Feature store security – protecting shared features
- Monitoring model performance degradation as a security signal
- Automated revocation of compromised model endpoints
Module 13: Organisational AI Security Culture - Building a cross-functional AI security task force
- Training developers, data scientists, and product managers
- Creating secure AI coding standards and style guides
- Developing internal AI security champions network
- Running secure AI workshops and threat modeling sessions
- Communicating AI risks to non-technical stakeholders
- Creating executive briefings on AI security posture
- Designing AI incident communication protocols
- Establishing whistleblower channels for AI misuse
- Conducting AI security awareness campaigns
- Aligning incentives with secure development behaviour
- Measuring cultural maturity in AI security practices
- Onboarding checklist for new hires working with AI
- External communication strategy for AI security breaches
- Handling media and public relations during AI incidents
Module 14: Certification & Career Advancement - How to use your Certificate of Completion effectively
- Adding AI security credentials to LinkedIn and résumés
- Positioning yourself as a secure AI leader in job interviews
- Contributing to open-source secure AI projects
- Speaking at conferences on AI security topics
- Writing technical blogs to demonstrate expertise
- Negotiating higher compensation with specialised skills
- Transitioning from developer to AI security architect
- Preparing for advanced certifications in AI governance
- Joining professional networks for AI security
- Contributing to AI standards bodies and policy groups
- Staying updated on emerging AI attack patterns
- Accessing exclusive resources from The Art of Service
- Invitations to private forums for certified professionals
- Next-step learning paths in AI red teaming, policy, and audit
- Developing an AI governance board charter
- Creating model cards for transparency and accountability
- Implementing system cards for AI infrastructure disclosure
- Audit trails for model training, evaluation, and deployment
- Documentation requirements for AI risk assessments
- Establishing AI model inventory and registry
- Version control for datasets, code, and models
- Securing model rollouts with canary deployments
- Implementing model retirement and deprecation policies
- Third-party vendor risk assessment for AI tools
- Contractual clauses for AI security in vendor agreements
- Insurance considerations for AI liability and breach coverage
- Preparing for AI audits by regulators or external firms
- Aligning AI practices with ISO 27001 and SOC 2 controls
- Generating compliance reports for executive leadership
Module 10: Testing & Validation at Scale - Developing a secure AI test plan
- Black-box vs white-box testing for AI models
- Automated testing frameworks for adversarial robustness
- Building a test suite for prompt injection resistance
- Simulating model inversion and membership inference attacks
- Integration testing for AI-powered applications
- Performance vs security tradeoff analysis
- Penetration testing methodology for AI systems
- Hiring and managing external red teams for AI
- Automated fuzzing of AI input channels
- Testing model behaviour under adversarial conditions
- Validating safety guardrails in generative AI
- Benchmarking model resilience against known attack libraries
- Using ART (Adversarial Robustness Toolbox) in test pipelines
- Regression testing for model updates and retraining
Module 11: Secure Generative AI Implementation - Architecture of large language models and their security implications
- Securing prompt engineering pipelines
- Preventing hallucination-driven exposure of sensitive data
- Guardrail implementation for LLM outputs
- Content filtering using classifier chains and rule-based systems
- Context window management to prevent leakage
- Securing RAG (Retrieval-Augmented Generation) systems
- Validating external knowledge sources in RAG pipelines
- Embedding security into agentic AI behaviours
- Preventing unauthorised tool use by AI agents
- Controlling function calling and API access in LLMs
- Monitoring agent decision trees for anomalous chains
- Hardening fine-tuning data for domain-specific LLMs
- Secure deployment of open-source LLMs
- Managing model weights and tokenizers securely
Module 12: Secure Model Operations (MLOps Security) - Integrating security into continuous integration/continuous deployment (CI/CD)
- Automated scanning of model code for vulnerabilities
- Static application security testing (SAST) for ML scripts
- Dynamic analysis of model behaviour in staging
- Secrets management for API keys and credentials
- Secure handling of environment variables in MLOps
- Role-based access control (RBAC) in ML platforms
- Audit logging for model retraining and redeployment
- Immutable pipelines to prevent configuration drift
- Secure model registry design patterns
- Signing and verifying model releases
- Blue-green deployments for zero-downtime secure updates
- Feature store security – protecting shared features
- Monitoring model performance degradation as a security signal
- Automated revocation of compromised model endpoints
Module 13: Organisational AI Security Culture - Building a cross-functional AI security task force
- Training developers, data scientists, and product managers
- Creating secure AI coding standards and style guides
- Developing internal AI security champions network
- Running secure AI workshops and threat modeling sessions
- Communicating AI risks to non-technical stakeholders
- Creating executive briefings on AI security posture
- Designing AI incident communication protocols
- Establishing whistleblower channels for AI misuse
- Conducting AI security awareness campaigns
- Aligning incentives with secure development behaviour
- Measuring cultural maturity in AI security practices
- Onboarding checklist for new hires working with AI
- External communication strategy for AI security breaches
- Handling media and public relations during AI incidents
Module 14: Certification & Career Advancement - How to use your Certificate of Completion effectively
- Adding AI security credentials to LinkedIn and résumés
- Positioning yourself as a secure AI leader in job interviews
- Contributing to open-source secure AI projects
- Speaking at conferences on AI security topics
- Writing technical blogs to demonstrate expertise
- Negotiating higher compensation with specialised skills
- Transitioning from developer to AI security architect
- Preparing for advanced certifications in AI governance
- Joining professional networks for AI security
- Contributing to AI standards bodies and policy groups
- Staying updated on emerging AI attack patterns
- Accessing exclusive resources from The Art of Service
- Invitations to private forums for certified professionals
- Next-step learning paths in AI red teaming, policy, and audit
- Architecture of large language models and their security implications
- Securing prompt engineering pipelines
- Preventing hallucination-driven exposure of sensitive data
- Guardrail implementation for LLM outputs
- Content filtering using classifier chains and rule-based systems
- Context window management to prevent leakage
- Securing RAG (Retrieval-Augmented Generation) systems
- Validating external knowledge sources in RAG pipelines
- Embedding security into agentic AI behaviours
- Preventing unauthorised tool use by AI agents
- Controlling function calling and API access in LLMs
- Monitoring agent decision trees for anomalous chains
- Hardening fine-tuning data for domain-specific LLMs
- Secure deployment of open-source LLMs
- Managing model weights and tokenizers securely
Module 12: Secure Model Operations (MLOps Security) - Integrating security into continuous integration/continuous deployment (CI/CD)
- Automated scanning of model code for vulnerabilities
- Static application security testing (SAST) for ML scripts
- Dynamic analysis of model behaviour in staging
- Secrets management for API keys and credentials
- Secure handling of environment variables in MLOps
- Role-based access control (RBAC) in ML platforms
- Audit logging for model retraining and redeployment
- Immutable pipelines to prevent configuration drift
- Secure model registry design patterns
- Signing and verifying model releases
- Blue-green deployments for zero-downtime secure updates
- Feature store security – protecting shared features
- Monitoring model performance degradation as a security signal
- Automated revocation of compromised model endpoints
Module 13: Organisational AI Security Culture - Building a cross-functional AI security task force
- Training developers, data scientists, and product managers
- Creating secure AI coding standards and style guides
- Developing internal AI security champions network
- Running secure AI workshops and threat modeling sessions
- Communicating AI risks to non-technical stakeholders
- Creating executive briefings on AI security posture
- Designing AI incident communication protocols
- Establishing whistleblower channels for AI misuse
- Conducting AI security awareness campaigns
- Aligning incentives with secure development behaviour
- Measuring cultural maturity in AI security practices
- Onboarding checklist for new hires working with AI
- External communication strategy for AI security breaches
- Handling media and public relations during AI incidents
Module 14: Certification & Career Advancement - How to use your Certificate of Completion effectively
- Adding AI security credentials to LinkedIn and résumés
- Positioning yourself as a secure AI leader in job interviews
- Contributing to open-source secure AI projects
- Speaking at conferences on AI security topics
- Writing technical blogs to demonstrate expertise
- Negotiating higher compensation with specialised skills
- Transitioning from developer to AI security architect
- Preparing for advanced certifications in AI governance
- Joining professional networks for AI security
- Contributing to AI standards bodies and policy groups
- Staying updated on emerging AI attack patterns
- Accessing exclusive resources from The Art of Service
- Invitations to private forums for certified professionals
- Next-step learning paths in AI red teaming, policy, and audit
- Building a cross-functional AI security task force
- Training developers, data scientists, and product managers
- Creating secure AI coding standards and style guides
- Developing internal AI security champions network
- Running secure AI workshops and threat modeling sessions
- Communicating AI risks to non-technical stakeholders
- Creating executive briefings on AI security posture
- Designing AI incident communication protocols
- Establishing whistleblower channels for AI misuse
- Conducting AI security awareness campaigns
- Aligning incentives with secure development behaviour
- Measuring cultural maturity in AI security practices
- Onboarding checklist for new hires working with AI
- External communication strategy for AI security breaches
- Handling media and public relations during AI incidents