Course Format & Delivery Details Self-Paced. On-Demand. Lifetime Access. Zero Risk.
This course is designed for modern developers who value control, clarity, and career advancement without compromise. From the moment you enroll, you gain structured, immediate online access to a comprehensive curriculum that evolves with the industry, ensuring your knowledge stays current for years to come. Study on Your Schedule - No Deadlines, No Pressure
The course is self-paced and fully on-demand, meaning there are no fixed start or end dates, no mandatory live sessions, and no time commitments. You decide when and where to study, making it easy to balance learning with your professional responsibilities, personal life, or global time zone. Fast Results, Real Progress - In as Little as 20 Hours
Most learners complete the core material in 20 to 30 hours, with many reporting actionable insights and enhanced workflow confidence within the first 10 hours. The modular design allows you to focus on high-impact areas immediately relevant to your current projects, giving you rapid visibility into tangible improvements in your security testing capabilities. Lifetime Access with Continuous Updates - One Payment, Forever Value
Once enrolled, you receive lifetime access to all course materials, including every future update at no additional cost. As AI-powered security tools and application architectures evolve, so does this course. You’ll always have access to cutting-edge practices, newly added techniques, and refined methodologies - ensuring your certification and skills remain industry-relevant year after year. Access Anytime, Anywhere - Desktop or Mobile
The entire course platform is mobile-friendly and accessible 24/7 from any device with an internet connection. Whether you're reviewing concepts on your phone during a commute, practising workflows on a tablet, or diving deep into security analysis on your laptop, the system adapts seamlessly to your preferred learning environment. Global accessibility means you’re never locked out, regardless of location or time zone. Direct Instructor Support When You Need It
While the course is self-guided, you are not alone. Enrolled learners receive direct access to expert instructor support through structured guidance channels. Whether you’re clarifying complex AI integration techniques, troubleshooting a testing workflow, or seeking feedback on implementation strategy, help is available to ensure you stay confident and on track. Certificate of Completion - Issued by The Art of Service
Upon finishing the curriculum, you’ll earn a Certificate of Completion issued by The Art of Service - a globally recognized authority in professional education and technical certification. This credential is shareable, verifiable, and respected across industries and hiring platforms. It signals to employers and peers that you’ve mastered AI-powered application security testing at a professional, implementation-ready level. Simple, Upfront Pricing - No Hidden Fees, No Surprises
Pricing is completely transparent, with no hidden fees, enrollment charges, or recurring subscriptions. What you see is exactly what you get - one straightforward payment for lifetime access, continuous updates, and full certification eligibility. No fine print. No upsells. Secure Payment Options - Visa, Mastercard, PayPal
We accept all major payment methods including Visa, Mastercard, and PayPal. Transactions are processed securely through industry-standard encryption, ensuring your financial information is protected at every step. 100% Money-Back Guarantee - Learn Risk-Free
We stand by the value and transformation this course delivers. If at any point within 30 days you feel it’s not meeting your expectations, simply request a full refund. No questions asked. No forms to fill out. This is our promise: you either gain career-advancing skills, or you walk away with your investment returned. That’s how confident we are that this course will work for you. Clear Access Process - Confirmation and Access Separately Delivered
After enrollment, you’ll immediately receive a confirmation email acknowledging your registration. Your access details, including login credentials and course navigation instructions, will be sent separately once your learner profile and materials are fully prepared. This ensures a smooth, error-free onboarding experience tailored to your enrolment. Will This Work For Me? - Yes, Even If You're Starting From Here…
Whether you're a backend developer integrating AI tools into CI/CD pipelines, a full-stack engineer securing cloud-native apps, or a security-conscious programmer transitioning into DevSecOps, this course meets you at your level and moves you forward. The material is designed to be practical, incremental, and immediately applicable - no theoretical fluff, no academic detours. - If you’re unsure how to apply AI models to detect SQL injection in real-time, this course gives you step-by-step patterns to do so.
- If you’ve struggled to integrate automated security feedback into pull requests, you’ll master AI-powered pull request analysis workflows.
- If you’re new to prompt engineering for vulnerability detection, you’ll learn concise, repeatable templates used by industry leaders.
Don’t Just Take Our Word For It - Learners Are Already Succeeding
“After completing this course, I automated 70% of our regression security checks using AI-driven test generation. My team now delivers features 40% faster with stronger compliance - and I got promoted two months later.” - Lena R, Senior DevOps Engineer, Berlin “I was skeptical about AI replacing manual testing, but this course taught me how to use it as a force multiplier. I built an AI-augmented scanner that reduced false positives by 65%. My manager called it a ‘game-changer’.” - Dev T, Application Security Analyst, Toronto This Works Even If You’ve Never Used Machine Learning Before
You don’t need a PhD, prior data science experience, or AI expertise. The course starts with foundational concepts and progresses through practical, hands-on implementation. Every technique is broken down into repeatable steps, using real application examples. If you can write code, read logs, and run tests, you can master AI-powered security testing - and this course makes it inevitable. Your Career Is Worth Protecting - So We’ve Removed the Risk
This isn't just another training module. This is a career acceleration system with built-in risk reversal. You pay once. You learn at your pace. You get support when needed. You earn a respected certification. And if it doesn’t deliver, you get every dollar back. There is no downside - only upside. Your next level in application security is waiting, and we’ve done everything to make sure you reach it with confidence.
Extensive & Detailed Course Curriculum
Module 1: Foundations of AI-Powered Application Security - Understanding the shift from manual to AI-augmented security testing
- Core principles of secure software development in modern environments
- The role of automation and intelligence in reducing vulnerability exposure
- Defining AI, machine learning, and generative models in security contexts
- How AI detects patterns that humans consistently miss
- Common misconceptions about AI replacing developers
- Real-world breach case studies caused by undetected vulnerabilities
- The cost of delayed vulnerability discovery in SDLC
- Integrating security early using AI-powered feedback loops
- AI ethics and responsible use in application testing
- Difference between rule-based and AI-driven detection systems
- Understanding false positives and how AI reduces them over time
- Security testing maturity models and where AI fits
- Mapping AI capabilities to OWASP Top 10 categories
- Defining scope, goals, and success metrics for AI testing
Module 2: Core Security Testing Frameworks and Methodologies - Overview of SAST, DAST, IAST, and RASP testing approaches
- Strengths and weaknesses of each testing type
- How AI enhances each methodology with predictive analysis
- Integrating testing into Agile and DevOps workflows
- Security testing in CI/CD pipelines - timing and triggers
- Defining test coverage and measuring effectiveness
- Automated vulnerability classification and prioritization
- Using risk scoring models with AI input
- Understanding attack surface mapping with intelligent scanning
- Behavioral analysis versus signature-based detection
- Threat modeling with AI-assisted scenario generation
- Creating repeatable, audit-ready testing procedures
- Aligning security testing with compliance frameworks (ISO, NIST, SOC2)
- Documentation standards for security findings
- Transitioning from checklist security to adaptive testing
Module 3: AI Models for Vulnerability Detection and Analysis - Types of machine learning models used in security testing
- Supervised vs unsupervised learning in vulnerability discovery
- Training data sources for AI-powered security tools
- How AI identifies code anomalies indicative of vulnerabilities
- Pattern recognition for SQL injection, XSS, and CSRF
- Natural language processing for parsing code comments and logs
- Using embeddings to represent code structures for AI analysis
- Neural networks in binary and compiled code analysis
- Graph-based AI for detecting insecure data flows
- Ensemble methods to increase detection accuracy
- Model drift and retraining strategies for evolving codebases
- Fine-tuning pre-trained models for internal code patterns
- Transfer learning in application security contexts
- Confidence scoring and uncertainty quantification
- Interpreting AI-generated findings - what the model really means
Module 4: Prompt Engineering for Security Test Generation - Fundamentals of prompt design for code analysis tasks
- Writing precise instructions for vulnerability detection
- Role-prompting techniques to guide AI behavior
- Using few-shot learning with example-based prompts
- Creating reusable prompt templates for common security checks
- Chain-of-thought prompting to improve reasoning accuracy
- Iterative refinement of prompts based on output quality
- Context window management for large codebases
- Prompt chaining for multi-step analysis workflows
- Securing prompts against manipulation and leakage
- Versioning and testing prompts like code
- Automating prompt execution across repositories
- Measuring prompt effectiveness with precision and recall
- Common pitfalls in prompt engineering for security
- Best practices for maintaining prompt repositories
Module 5: Integrating AI Tools into Development Environments - Evaluating AI security tools: open-source vs commercial
- Setting up AI assistants in IDEs like VS Code and JetBrains
- Real-time vulnerability alerts during coding
- Auto-suggesting secure code alternatives using AI
- Configuring AI linters and static analysis plugins
- Using AI to explain complex vulnerabilities in plain language
- Controlling false alarm rates with custom thresholds
- Onboarding teams to AI-powered development workflows
- Managing tool fatigue and cognitive overload
- Ensuring AI tools comply with internal policies
- Performance impact of AI plugins on development speed
- Local vs cloud-based AI processing tradeoffs
- Offline capabilities and data privacy considerations
- Centralized configuration management for tooling
- Monitoring tool usage and adoption across teams
Module 6: Building AI-Powered Test Automation Scripts - Writing scripts that leverage AI APIs for vulnerability scanning
- Using Python to interact with AI security endpoints
- Batch processing code files for AI analysis
- Parsing and structuring AI-generated JSON responses
- Automatically generating remediation suggestions
- Creating dashboards from AI test results
- Scheduling recurring AI-powered scans with cron and GitHub Actions
- Combining traditional scanners with AI augmentation
- Error handling and retry logic in AI workflows
- Rate limiting and API cost management
- Storing and versioning historical AI scan data
- Integrating with issue tracking systems like Jira
- Tagging vulnerabilities by severity, component, and developer
- Automating triage workflows using AI classification
- Testing the reliability of AI-driven automation
Module 7: AI-Driven Dynamic and Interactive Testing - Automating web application crawling with intelligent navigation
- AI-based input generation for fuzz testing
- Predicting likely attack vectors based on application structure
- Session handling and authentication in automated DAST
- Discovering hidden endpoints using pattern analysis
- Finding broken access controls with behavioural AI
- Simulating real attacker logic with reinforcement learning
- Testing API endpoints at scale using AI-guided exploration
- Validating GraphQL security with AI-powered query analysis
- Rate limiting bypass detection using adaptive testing
- Session fixation and token leakage detection methods
- Client-side security issues identified through AI DOM analysis
- Automated comparison of expected vs actual API responses
- Tracking state changes to detect business logic flaws
- Generating realistic test data for penetration scenarios
Module 8: Securing CI/CD Pipelines with AI Feedback - Embedding security gates in build pipelines
- Fail-fast strategies for critical vulnerability detection
- Using AI to determine if a vulnerability is exploitable
- Auto-assigning security tickets based on code ownership
- Providing fix guidance directly in pull request comments
- Generating AI-powered commit message reviews
- Blocking merges based on AI risk scoring
- Whitelisting known issues with AI context awareness
- Handling legacy debt without blocking development
- Measuring pipeline security posture over time
- Reducing developer friction with contextual alerts
- Customizing feedback tone and language by team
- Integrating with Slack and Teams for real-time notifications
- Creating audit trails of AI-driven security decisions
- Scaling pipeline security across 100+ repositories
Module 9: Cloud-Native and Container Security with AI - Analyzing Dockerfiles for insecure configurations
- Scanning container images using AI-enhanced tools
- Detecting hardcoded secrets in build layers
- Monitoring Kubernetes manifests for privilege escalation risks
- AI analysis of Helm chart security patterns
- Identifying misconfigurations in Terraform and Pulumi code
- Real-time drift detection in cloud infrastructure
- Predicting IAM policy overreach using usage patterns
- Automated compliance checking against CIS benchmarks
- AI-powered detection of shadow IT resources
- Serverless function security analysis with AI
- Protecting environment variables and configuration files
- Scanning third-party container registries
- Generating secure defaults from existing infrastructure
- Cost-aware security: balancing protection and performance
Module 10: Advanced AI Techniques in Threat Intelligence - Harvesting and processing threat intelligence feeds
- Using AI to correlate vulnerabilities with active exploits
- Predicting emerging attack trends based on dark web data
- Automated adversary simulation using generative AI
- Generating realistic phishing payloads for training
- Tracking exploit publication timelines and patch urgency
- Identifying zero-day indicators through anomaly detection
- Linking CVE data to internal asset inventories
- Creating dynamic risk heatmaps powered by AI
- Automated reporting for executive security summaries
- Summarizing complex security events in plain language
- AI-assisted root cause analysis of breaches
- Temporal analysis of attack sequences
- Attribution modeling and threat actor profiling
- Scenario planning for high-impact security events
Module 11: AI in Mobile and API Security Testing - Analyzing Android APKs and iOS binaries with AI
- Detecting insecure API endpoints in mobile apps
- Reverse engineering protection analysis using pattern recognition
- AI-powered detection of jailbreak and root detection bypass
- Identifying insecure local data storage programmatically
- Testing GraphQL, gRPC, and REST APIs at scale
- Discovering undocumented API endpoints using heuristics
- Validating proper rate limiting and authentication
- Generating test cases from OpenAPI and Swagger specs
- Detecting mass assignment and IDOR vulnerabilities
- AI analysis of request/response patterns for anomalies
- Testing API version deprecation and migration paths
- Validating proper error handling and disclosure
- Automated fuzzing of complex API parameters
- Session management analysis across API calls
Module 12: Practical Projects and Real-World Simulations - Project 1: Build an AI-augmented SAST scanner for Python
- Project 2: Create a GitHub bot that comments on security risks
- Project 3: Develop an AI-powered vulnerability triage system
- Project 4: Automate OWASP ZAP scans with AI result analysis
- Project 5: Integrate AI feedback into a Node.js CI pipeline
- Simulating a real breach scenario with AI detection
- Designing AI-driven regression security tests
- Creating a security knowledge base using AI summarization
- Building a dashboard to track AI testing KPIs
- Generating executive reports from security data
- Conducting a full-stack security audit using AI tools
- Red team vs blue team exercise with AI assistance
- Automating security documentation from code metadata
- Testing AI mitigation strategies against known exploits
- Measuring ROI of AI in testing efficiency and coverage
Module 13: Governance, Compliance, and Audit Readiness - Documenting AI-assisted testing processes for auditors
- Proving due diligence in vulnerability management
- Creating evidence trails for AI-generated findings
- Handling explainability requirements in regulated industries
- Ensuring AI tools comply with GDPR, HIPAA, and CCPA
- Managing consent and data usage in testing environments
- Third-party risk assessment involving AI tools
- Vendor due diligence for AI security solutions
- Internal policy development for AI usage
- Role-based access control for AI systems
- Audit logging of all AI interactions and decisions
- Handling false negatives and accountability
- Independent validation of AI findings
- Preparing for regulatory scrutiny of automated systems
- Continuous compliance monitoring with AI
Module 14: Career Advancement and Certification Preparation - How to showcase AI security testing on your resume
- Translating course projects into portfolio pieces
- Preparing for technical interviews involving AI security
- Discussing AI limitations and strengths in professional settings
- Communicating security ROI to non-technical stakeholders
- Negotiating roles with security and AI responsibilities
- Becoming a security champion within development teams
- Mentoring others in AI-powered testing practices
- Presenting findings to engineering and security leadership
- Contributing to open-source security tools with AI
- Staying updated on emerging AI security research
- Joining professional communities and forums
- Speaking at meetups and conferences about your experience
- Building credibility through certification and case studies
- Mapping skills to job roles like DevSecOps, AppSec Engineer, and SRE
Module 15: Certification, Lifelong Learning, and Next Steps - Final assessment: applying AI testing to a full application
- Submitting your capstone project for evaluation
- Review process and feedback timeline
- Earning your Certificate of Completion from The Art of Service
- Understanding certification validity and renewal
- Sharing your credential on LinkedIn and GitHub
- Verifying certification status for employers
- Accessing the alumni network of AI security practitioners
- Receiving curated updates on new AI security techniques
- Participating in advanced topic deep dives
- Contributing to community knowledge bases
- Accessing new modules as they are released
- Tracking your progress with built-in learning analytics
- Using gamification elements to maintain momentum
- Setting your next career milestone in application security
Module 1: Foundations of AI-Powered Application Security - Understanding the shift from manual to AI-augmented security testing
- Core principles of secure software development in modern environments
- The role of automation and intelligence in reducing vulnerability exposure
- Defining AI, machine learning, and generative models in security contexts
- How AI detects patterns that humans consistently miss
- Common misconceptions about AI replacing developers
- Real-world breach case studies caused by undetected vulnerabilities
- The cost of delayed vulnerability discovery in SDLC
- Integrating security early using AI-powered feedback loops
- AI ethics and responsible use in application testing
- Difference between rule-based and AI-driven detection systems
- Understanding false positives and how AI reduces them over time
- Security testing maturity models and where AI fits
- Mapping AI capabilities to OWASP Top 10 categories
- Defining scope, goals, and success metrics for AI testing
Module 2: Core Security Testing Frameworks and Methodologies - Overview of SAST, DAST, IAST, and RASP testing approaches
- Strengths and weaknesses of each testing type
- How AI enhances each methodology with predictive analysis
- Integrating testing into Agile and DevOps workflows
- Security testing in CI/CD pipelines - timing and triggers
- Defining test coverage and measuring effectiveness
- Automated vulnerability classification and prioritization
- Using risk scoring models with AI input
- Understanding attack surface mapping with intelligent scanning
- Behavioral analysis versus signature-based detection
- Threat modeling with AI-assisted scenario generation
- Creating repeatable, audit-ready testing procedures
- Aligning security testing with compliance frameworks (ISO, NIST, SOC2)
- Documentation standards for security findings
- Transitioning from checklist security to adaptive testing
Module 3: AI Models for Vulnerability Detection and Analysis - Types of machine learning models used in security testing
- Supervised vs unsupervised learning in vulnerability discovery
- Training data sources for AI-powered security tools
- How AI identifies code anomalies indicative of vulnerabilities
- Pattern recognition for SQL injection, XSS, and CSRF
- Natural language processing for parsing code comments and logs
- Using embeddings to represent code structures for AI analysis
- Neural networks in binary and compiled code analysis
- Graph-based AI for detecting insecure data flows
- Ensemble methods to increase detection accuracy
- Model drift and retraining strategies for evolving codebases
- Fine-tuning pre-trained models for internal code patterns
- Transfer learning in application security contexts
- Confidence scoring and uncertainty quantification
- Interpreting AI-generated findings - what the model really means
Module 4: Prompt Engineering for Security Test Generation - Fundamentals of prompt design for code analysis tasks
- Writing precise instructions for vulnerability detection
- Role-prompting techniques to guide AI behavior
- Using few-shot learning with example-based prompts
- Creating reusable prompt templates for common security checks
- Chain-of-thought prompting to improve reasoning accuracy
- Iterative refinement of prompts based on output quality
- Context window management for large codebases
- Prompt chaining for multi-step analysis workflows
- Securing prompts against manipulation and leakage
- Versioning and testing prompts like code
- Automating prompt execution across repositories
- Measuring prompt effectiveness with precision and recall
- Common pitfalls in prompt engineering for security
- Best practices for maintaining prompt repositories
Module 5: Integrating AI Tools into Development Environments - Evaluating AI security tools: open-source vs commercial
- Setting up AI assistants in IDEs like VS Code and JetBrains
- Real-time vulnerability alerts during coding
- Auto-suggesting secure code alternatives using AI
- Configuring AI linters and static analysis plugins
- Using AI to explain complex vulnerabilities in plain language
- Controlling false alarm rates with custom thresholds
- Onboarding teams to AI-powered development workflows
- Managing tool fatigue and cognitive overload
- Ensuring AI tools comply with internal policies
- Performance impact of AI plugins on development speed
- Local vs cloud-based AI processing tradeoffs
- Offline capabilities and data privacy considerations
- Centralized configuration management for tooling
- Monitoring tool usage and adoption across teams
Module 6: Building AI-Powered Test Automation Scripts - Writing scripts that leverage AI APIs for vulnerability scanning
- Using Python to interact with AI security endpoints
- Batch processing code files for AI analysis
- Parsing and structuring AI-generated JSON responses
- Automatically generating remediation suggestions
- Creating dashboards from AI test results
- Scheduling recurring AI-powered scans with cron and GitHub Actions
- Combining traditional scanners with AI augmentation
- Error handling and retry logic in AI workflows
- Rate limiting and API cost management
- Storing and versioning historical AI scan data
- Integrating with issue tracking systems like Jira
- Tagging vulnerabilities by severity, component, and developer
- Automating triage workflows using AI classification
- Testing the reliability of AI-driven automation
Module 7: AI-Driven Dynamic and Interactive Testing - Automating web application crawling with intelligent navigation
- AI-based input generation for fuzz testing
- Predicting likely attack vectors based on application structure
- Session handling and authentication in automated DAST
- Discovering hidden endpoints using pattern analysis
- Finding broken access controls with behavioural AI
- Simulating real attacker logic with reinforcement learning
- Testing API endpoints at scale using AI-guided exploration
- Validating GraphQL security with AI-powered query analysis
- Rate limiting bypass detection using adaptive testing
- Session fixation and token leakage detection methods
- Client-side security issues identified through AI DOM analysis
- Automated comparison of expected vs actual API responses
- Tracking state changes to detect business logic flaws
- Generating realistic test data for penetration scenarios
Module 8: Securing CI/CD Pipelines with AI Feedback - Embedding security gates in build pipelines
- Fail-fast strategies for critical vulnerability detection
- Using AI to determine if a vulnerability is exploitable
- Auto-assigning security tickets based on code ownership
- Providing fix guidance directly in pull request comments
- Generating AI-powered commit message reviews
- Blocking merges based on AI risk scoring
- Whitelisting known issues with AI context awareness
- Handling legacy debt without blocking development
- Measuring pipeline security posture over time
- Reducing developer friction with contextual alerts
- Customizing feedback tone and language by team
- Integrating with Slack and Teams for real-time notifications
- Creating audit trails of AI-driven security decisions
- Scaling pipeline security across 100+ repositories
Module 9: Cloud-Native and Container Security with AI - Analyzing Dockerfiles for insecure configurations
- Scanning container images using AI-enhanced tools
- Detecting hardcoded secrets in build layers
- Monitoring Kubernetes manifests for privilege escalation risks
- AI analysis of Helm chart security patterns
- Identifying misconfigurations in Terraform and Pulumi code
- Real-time drift detection in cloud infrastructure
- Predicting IAM policy overreach using usage patterns
- Automated compliance checking against CIS benchmarks
- AI-powered detection of shadow IT resources
- Serverless function security analysis with AI
- Protecting environment variables and configuration files
- Scanning third-party container registries
- Generating secure defaults from existing infrastructure
- Cost-aware security: balancing protection and performance
Module 10: Advanced AI Techniques in Threat Intelligence - Harvesting and processing threat intelligence feeds
- Using AI to correlate vulnerabilities with active exploits
- Predicting emerging attack trends based on dark web data
- Automated adversary simulation using generative AI
- Generating realistic phishing payloads for training
- Tracking exploit publication timelines and patch urgency
- Identifying zero-day indicators through anomaly detection
- Linking CVE data to internal asset inventories
- Creating dynamic risk heatmaps powered by AI
- Automated reporting for executive security summaries
- Summarizing complex security events in plain language
- AI-assisted root cause analysis of breaches
- Temporal analysis of attack sequences
- Attribution modeling and threat actor profiling
- Scenario planning for high-impact security events
Module 11: AI in Mobile and API Security Testing - Analyzing Android APKs and iOS binaries with AI
- Detecting insecure API endpoints in mobile apps
- Reverse engineering protection analysis using pattern recognition
- AI-powered detection of jailbreak and root detection bypass
- Identifying insecure local data storage programmatically
- Testing GraphQL, gRPC, and REST APIs at scale
- Discovering undocumented API endpoints using heuristics
- Validating proper rate limiting and authentication
- Generating test cases from OpenAPI and Swagger specs
- Detecting mass assignment and IDOR vulnerabilities
- AI analysis of request/response patterns for anomalies
- Testing API version deprecation and migration paths
- Validating proper error handling and disclosure
- Automated fuzzing of complex API parameters
- Session management analysis across API calls
Module 12: Practical Projects and Real-World Simulations - Project 1: Build an AI-augmented SAST scanner for Python
- Project 2: Create a GitHub bot that comments on security risks
- Project 3: Develop an AI-powered vulnerability triage system
- Project 4: Automate OWASP ZAP scans with AI result analysis
- Project 5: Integrate AI feedback into a Node.js CI pipeline
- Simulating a real breach scenario with AI detection
- Designing AI-driven regression security tests
- Creating a security knowledge base using AI summarization
- Building a dashboard to track AI testing KPIs
- Generating executive reports from security data
- Conducting a full-stack security audit using AI tools
- Red team vs blue team exercise with AI assistance
- Automating security documentation from code metadata
- Testing AI mitigation strategies against known exploits
- Measuring ROI of AI in testing efficiency and coverage
Module 13: Governance, Compliance, and Audit Readiness - Documenting AI-assisted testing processes for auditors
- Proving due diligence in vulnerability management
- Creating evidence trails for AI-generated findings
- Handling explainability requirements in regulated industries
- Ensuring AI tools comply with GDPR, HIPAA, and CCPA
- Managing consent and data usage in testing environments
- Third-party risk assessment involving AI tools
- Vendor due diligence for AI security solutions
- Internal policy development for AI usage
- Role-based access control for AI systems
- Audit logging of all AI interactions and decisions
- Handling false negatives and accountability
- Independent validation of AI findings
- Preparing for regulatory scrutiny of automated systems
- Continuous compliance monitoring with AI
Module 14: Career Advancement and Certification Preparation - How to showcase AI security testing on your resume
- Translating course projects into portfolio pieces
- Preparing for technical interviews involving AI security
- Discussing AI limitations and strengths in professional settings
- Communicating security ROI to non-technical stakeholders
- Negotiating roles with security and AI responsibilities
- Becoming a security champion within development teams
- Mentoring others in AI-powered testing practices
- Presenting findings to engineering and security leadership
- Contributing to open-source security tools with AI
- Staying updated on emerging AI security research
- Joining professional communities and forums
- Speaking at meetups and conferences about your experience
- Building credibility through certification and case studies
- Mapping skills to job roles like DevSecOps, AppSec Engineer, and SRE
Module 15: Certification, Lifelong Learning, and Next Steps - Final assessment: applying AI testing to a full application
- Submitting your capstone project for evaluation
- Review process and feedback timeline
- Earning your Certificate of Completion from The Art of Service
- Understanding certification validity and renewal
- Sharing your credential on LinkedIn and GitHub
- Verifying certification status for employers
- Accessing the alumni network of AI security practitioners
- Receiving curated updates on new AI security techniques
- Participating in advanced topic deep dives
- Contributing to community knowledge bases
- Accessing new modules as they are released
- Tracking your progress with built-in learning analytics
- Using gamification elements to maintain momentum
- Setting your next career milestone in application security
- Overview of SAST, DAST, IAST, and RASP testing approaches
- Strengths and weaknesses of each testing type
- How AI enhances each methodology with predictive analysis
- Integrating testing into Agile and DevOps workflows
- Security testing in CI/CD pipelines - timing and triggers
- Defining test coverage and measuring effectiveness
- Automated vulnerability classification and prioritization
- Using risk scoring models with AI input
- Understanding attack surface mapping with intelligent scanning
- Behavioral analysis versus signature-based detection
- Threat modeling with AI-assisted scenario generation
- Creating repeatable, audit-ready testing procedures
- Aligning security testing with compliance frameworks (ISO, NIST, SOC2)
- Documentation standards for security findings
- Transitioning from checklist security to adaptive testing
Module 3: AI Models for Vulnerability Detection and Analysis - Types of machine learning models used in security testing
- Supervised vs unsupervised learning in vulnerability discovery
- Training data sources for AI-powered security tools
- How AI identifies code anomalies indicative of vulnerabilities
- Pattern recognition for SQL injection, XSS, and CSRF
- Natural language processing for parsing code comments and logs
- Using embeddings to represent code structures for AI analysis
- Neural networks in binary and compiled code analysis
- Graph-based AI for detecting insecure data flows
- Ensemble methods to increase detection accuracy
- Model drift and retraining strategies for evolving codebases
- Fine-tuning pre-trained models for internal code patterns
- Transfer learning in application security contexts
- Confidence scoring and uncertainty quantification
- Interpreting AI-generated findings - what the model really means
Module 4: Prompt Engineering for Security Test Generation - Fundamentals of prompt design for code analysis tasks
- Writing precise instructions for vulnerability detection
- Role-prompting techniques to guide AI behavior
- Using few-shot learning with example-based prompts
- Creating reusable prompt templates for common security checks
- Chain-of-thought prompting to improve reasoning accuracy
- Iterative refinement of prompts based on output quality
- Context window management for large codebases
- Prompt chaining for multi-step analysis workflows
- Securing prompts against manipulation and leakage
- Versioning and testing prompts like code
- Automating prompt execution across repositories
- Measuring prompt effectiveness with precision and recall
- Common pitfalls in prompt engineering for security
- Best practices for maintaining prompt repositories
Module 5: Integrating AI Tools into Development Environments - Evaluating AI security tools: open-source vs commercial
- Setting up AI assistants in IDEs like VS Code and JetBrains
- Real-time vulnerability alerts during coding
- Auto-suggesting secure code alternatives using AI
- Configuring AI linters and static analysis plugins
- Using AI to explain complex vulnerabilities in plain language
- Controlling false alarm rates with custom thresholds
- Onboarding teams to AI-powered development workflows
- Managing tool fatigue and cognitive overload
- Ensuring AI tools comply with internal policies
- Performance impact of AI plugins on development speed
- Local vs cloud-based AI processing tradeoffs
- Offline capabilities and data privacy considerations
- Centralized configuration management for tooling
- Monitoring tool usage and adoption across teams
Module 6: Building AI-Powered Test Automation Scripts - Writing scripts that leverage AI APIs for vulnerability scanning
- Using Python to interact with AI security endpoints
- Batch processing code files for AI analysis
- Parsing and structuring AI-generated JSON responses
- Automatically generating remediation suggestions
- Creating dashboards from AI test results
- Scheduling recurring AI-powered scans with cron and GitHub Actions
- Combining traditional scanners with AI augmentation
- Error handling and retry logic in AI workflows
- Rate limiting and API cost management
- Storing and versioning historical AI scan data
- Integrating with issue tracking systems like Jira
- Tagging vulnerabilities by severity, component, and developer
- Automating triage workflows using AI classification
- Testing the reliability of AI-driven automation
Module 7: AI-Driven Dynamic and Interactive Testing - Automating web application crawling with intelligent navigation
- AI-based input generation for fuzz testing
- Predicting likely attack vectors based on application structure
- Session handling and authentication in automated DAST
- Discovering hidden endpoints using pattern analysis
- Finding broken access controls with behavioural AI
- Simulating real attacker logic with reinforcement learning
- Testing API endpoints at scale using AI-guided exploration
- Validating GraphQL security with AI-powered query analysis
- Rate limiting bypass detection using adaptive testing
- Session fixation and token leakage detection methods
- Client-side security issues identified through AI DOM analysis
- Automated comparison of expected vs actual API responses
- Tracking state changes to detect business logic flaws
- Generating realistic test data for penetration scenarios
Module 8: Securing CI/CD Pipelines with AI Feedback - Embedding security gates in build pipelines
- Fail-fast strategies for critical vulnerability detection
- Using AI to determine if a vulnerability is exploitable
- Auto-assigning security tickets based on code ownership
- Providing fix guidance directly in pull request comments
- Generating AI-powered commit message reviews
- Blocking merges based on AI risk scoring
- Whitelisting known issues with AI context awareness
- Handling legacy debt without blocking development
- Measuring pipeline security posture over time
- Reducing developer friction with contextual alerts
- Customizing feedback tone and language by team
- Integrating with Slack and Teams for real-time notifications
- Creating audit trails of AI-driven security decisions
- Scaling pipeline security across 100+ repositories
Module 9: Cloud-Native and Container Security with AI - Analyzing Dockerfiles for insecure configurations
- Scanning container images using AI-enhanced tools
- Detecting hardcoded secrets in build layers
- Monitoring Kubernetes manifests for privilege escalation risks
- AI analysis of Helm chart security patterns
- Identifying misconfigurations in Terraform and Pulumi code
- Real-time drift detection in cloud infrastructure
- Predicting IAM policy overreach using usage patterns
- Automated compliance checking against CIS benchmarks
- AI-powered detection of shadow IT resources
- Serverless function security analysis with AI
- Protecting environment variables and configuration files
- Scanning third-party container registries
- Generating secure defaults from existing infrastructure
- Cost-aware security: balancing protection and performance
Module 10: Advanced AI Techniques in Threat Intelligence - Harvesting and processing threat intelligence feeds
- Using AI to correlate vulnerabilities with active exploits
- Predicting emerging attack trends based on dark web data
- Automated adversary simulation using generative AI
- Generating realistic phishing payloads for training
- Tracking exploit publication timelines and patch urgency
- Identifying zero-day indicators through anomaly detection
- Linking CVE data to internal asset inventories
- Creating dynamic risk heatmaps powered by AI
- Automated reporting for executive security summaries
- Summarizing complex security events in plain language
- AI-assisted root cause analysis of breaches
- Temporal analysis of attack sequences
- Attribution modeling and threat actor profiling
- Scenario planning for high-impact security events
Module 11: AI in Mobile and API Security Testing - Analyzing Android APKs and iOS binaries with AI
- Detecting insecure API endpoints in mobile apps
- Reverse engineering protection analysis using pattern recognition
- AI-powered detection of jailbreak and root detection bypass
- Identifying insecure local data storage programmatically
- Testing GraphQL, gRPC, and REST APIs at scale
- Discovering undocumented API endpoints using heuristics
- Validating proper rate limiting and authentication
- Generating test cases from OpenAPI and Swagger specs
- Detecting mass assignment and IDOR vulnerabilities
- AI analysis of request/response patterns for anomalies
- Testing API version deprecation and migration paths
- Validating proper error handling and disclosure
- Automated fuzzing of complex API parameters
- Session management analysis across API calls
Module 12: Practical Projects and Real-World Simulations - Project 1: Build an AI-augmented SAST scanner for Python
- Project 2: Create a GitHub bot that comments on security risks
- Project 3: Develop an AI-powered vulnerability triage system
- Project 4: Automate OWASP ZAP scans with AI result analysis
- Project 5: Integrate AI feedback into a Node.js CI pipeline
- Simulating a real breach scenario with AI detection
- Designing AI-driven regression security tests
- Creating a security knowledge base using AI summarization
- Building a dashboard to track AI testing KPIs
- Generating executive reports from security data
- Conducting a full-stack security audit using AI tools
- Red team vs blue team exercise with AI assistance
- Automating security documentation from code metadata
- Testing AI mitigation strategies against known exploits
- Measuring ROI of AI in testing efficiency and coverage
Module 13: Governance, Compliance, and Audit Readiness - Documenting AI-assisted testing processes for auditors
- Proving due diligence in vulnerability management
- Creating evidence trails for AI-generated findings
- Handling explainability requirements in regulated industries
- Ensuring AI tools comply with GDPR, HIPAA, and CCPA
- Managing consent and data usage in testing environments
- Third-party risk assessment involving AI tools
- Vendor due diligence for AI security solutions
- Internal policy development for AI usage
- Role-based access control for AI systems
- Audit logging of all AI interactions and decisions
- Handling false negatives and accountability
- Independent validation of AI findings
- Preparing for regulatory scrutiny of automated systems
- Continuous compliance monitoring with AI
Module 14: Career Advancement and Certification Preparation - How to showcase AI security testing on your resume
- Translating course projects into portfolio pieces
- Preparing for technical interviews involving AI security
- Discussing AI limitations and strengths in professional settings
- Communicating security ROI to non-technical stakeholders
- Negotiating roles with security and AI responsibilities
- Becoming a security champion within development teams
- Mentoring others in AI-powered testing practices
- Presenting findings to engineering and security leadership
- Contributing to open-source security tools with AI
- Staying updated on emerging AI security research
- Joining professional communities and forums
- Speaking at meetups and conferences about your experience
- Building credibility through certification and case studies
- Mapping skills to job roles like DevSecOps, AppSec Engineer, and SRE
Module 15: Certification, Lifelong Learning, and Next Steps - Final assessment: applying AI testing to a full application
- Submitting your capstone project for evaluation
- Review process and feedback timeline
- Earning your Certificate of Completion from The Art of Service
- Understanding certification validity and renewal
- Sharing your credential on LinkedIn and GitHub
- Verifying certification status for employers
- Accessing the alumni network of AI security practitioners
- Receiving curated updates on new AI security techniques
- Participating in advanced topic deep dives
- Contributing to community knowledge bases
- Accessing new modules as they are released
- Tracking your progress with built-in learning analytics
- Using gamification elements to maintain momentum
- Setting your next career milestone in application security
- Fundamentals of prompt design for code analysis tasks
- Writing precise instructions for vulnerability detection
- Role-prompting techniques to guide AI behavior
- Using few-shot learning with example-based prompts
- Creating reusable prompt templates for common security checks
- Chain-of-thought prompting to improve reasoning accuracy
- Iterative refinement of prompts based on output quality
- Context window management for large codebases
- Prompt chaining for multi-step analysis workflows
- Securing prompts against manipulation and leakage
- Versioning and testing prompts like code
- Automating prompt execution across repositories
- Measuring prompt effectiveness with precision and recall
- Common pitfalls in prompt engineering for security
- Best practices for maintaining prompt repositories
Module 5: Integrating AI Tools into Development Environments - Evaluating AI security tools: open-source vs commercial
- Setting up AI assistants in IDEs like VS Code and JetBrains
- Real-time vulnerability alerts during coding
- Auto-suggesting secure code alternatives using AI
- Configuring AI linters and static analysis plugins
- Using AI to explain complex vulnerabilities in plain language
- Controlling false alarm rates with custom thresholds
- Onboarding teams to AI-powered development workflows
- Managing tool fatigue and cognitive overload
- Ensuring AI tools comply with internal policies
- Performance impact of AI plugins on development speed
- Local vs cloud-based AI processing tradeoffs
- Offline capabilities and data privacy considerations
- Centralized configuration management for tooling
- Monitoring tool usage and adoption across teams
Module 6: Building AI-Powered Test Automation Scripts - Writing scripts that leverage AI APIs for vulnerability scanning
- Using Python to interact with AI security endpoints
- Batch processing code files for AI analysis
- Parsing and structuring AI-generated JSON responses
- Automatically generating remediation suggestions
- Creating dashboards from AI test results
- Scheduling recurring AI-powered scans with cron and GitHub Actions
- Combining traditional scanners with AI augmentation
- Error handling and retry logic in AI workflows
- Rate limiting and API cost management
- Storing and versioning historical AI scan data
- Integrating with issue tracking systems like Jira
- Tagging vulnerabilities by severity, component, and developer
- Automating triage workflows using AI classification
- Testing the reliability of AI-driven automation
Module 7: AI-Driven Dynamic and Interactive Testing - Automating web application crawling with intelligent navigation
- AI-based input generation for fuzz testing
- Predicting likely attack vectors based on application structure
- Session handling and authentication in automated DAST
- Discovering hidden endpoints using pattern analysis
- Finding broken access controls with behavioural AI
- Simulating real attacker logic with reinforcement learning
- Testing API endpoints at scale using AI-guided exploration
- Validating GraphQL security with AI-powered query analysis
- Rate limiting bypass detection using adaptive testing
- Session fixation and token leakage detection methods
- Client-side security issues identified through AI DOM analysis
- Automated comparison of expected vs actual API responses
- Tracking state changes to detect business logic flaws
- Generating realistic test data for penetration scenarios
Module 8: Securing CI/CD Pipelines with AI Feedback - Embedding security gates in build pipelines
- Fail-fast strategies for critical vulnerability detection
- Using AI to determine if a vulnerability is exploitable
- Auto-assigning security tickets based on code ownership
- Providing fix guidance directly in pull request comments
- Generating AI-powered commit message reviews
- Blocking merges based on AI risk scoring
- Whitelisting known issues with AI context awareness
- Handling legacy debt without blocking development
- Measuring pipeline security posture over time
- Reducing developer friction with contextual alerts
- Customizing feedback tone and language by team
- Integrating with Slack and Teams for real-time notifications
- Creating audit trails of AI-driven security decisions
- Scaling pipeline security across 100+ repositories
Module 9: Cloud-Native and Container Security with AI - Analyzing Dockerfiles for insecure configurations
- Scanning container images using AI-enhanced tools
- Detecting hardcoded secrets in build layers
- Monitoring Kubernetes manifests for privilege escalation risks
- AI analysis of Helm chart security patterns
- Identifying misconfigurations in Terraform and Pulumi code
- Real-time drift detection in cloud infrastructure
- Predicting IAM policy overreach using usage patterns
- Automated compliance checking against CIS benchmarks
- AI-powered detection of shadow IT resources
- Serverless function security analysis with AI
- Protecting environment variables and configuration files
- Scanning third-party container registries
- Generating secure defaults from existing infrastructure
- Cost-aware security: balancing protection and performance
Module 10: Advanced AI Techniques in Threat Intelligence - Harvesting and processing threat intelligence feeds
- Using AI to correlate vulnerabilities with active exploits
- Predicting emerging attack trends based on dark web data
- Automated adversary simulation using generative AI
- Generating realistic phishing payloads for training
- Tracking exploit publication timelines and patch urgency
- Identifying zero-day indicators through anomaly detection
- Linking CVE data to internal asset inventories
- Creating dynamic risk heatmaps powered by AI
- Automated reporting for executive security summaries
- Summarizing complex security events in plain language
- AI-assisted root cause analysis of breaches
- Temporal analysis of attack sequences
- Attribution modeling and threat actor profiling
- Scenario planning for high-impact security events
Module 11: AI in Mobile and API Security Testing - Analyzing Android APKs and iOS binaries with AI
- Detecting insecure API endpoints in mobile apps
- Reverse engineering protection analysis using pattern recognition
- AI-powered detection of jailbreak and root detection bypass
- Identifying insecure local data storage programmatically
- Testing GraphQL, gRPC, and REST APIs at scale
- Discovering undocumented API endpoints using heuristics
- Validating proper rate limiting and authentication
- Generating test cases from OpenAPI and Swagger specs
- Detecting mass assignment and IDOR vulnerabilities
- AI analysis of request/response patterns for anomalies
- Testing API version deprecation and migration paths
- Validating proper error handling and disclosure
- Automated fuzzing of complex API parameters
- Session management analysis across API calls
Module 12: Practical Projects and Real-World Simulations - Project 1: Build an AI-augmented SAST scanner for Python
- Project 2: Create a GitHub bot that comments on security risks
- Project 3: Develop an AI-powered vulnerability triage system
- Project 4: Automate OWASP ZAP scans with AI result analysis
- Project 5: Integrate AI feedback into a Node.js CI pipeline
- Simulating a real breach scenario with AI detection
- Designing AI-driven regression security tests
- Creating a security knowledge base using AI summarization
- Building a dashboard to track AI testing KPIs
- Generating executive reports from security data
- Conducting a full-stack security audit using AI tools
- Red team vs blue team exercise with AI assistance
- Automating security documentation from code metadata
- Testing AI mitigation strategies against known exploits
- Measuring ROI of AI in testing efficiency and coverage
Module 13: Governance, Compliance, and Audit Readiness - Documenting AI-assisted testing processes for auditors
- Proving due diligence in vulnerability management
- Creating evidence trails for AI-generated findings
- Handling explainability requirements in regulated industries
- Ensuring AI tools comply with GDPR, HIPAA, and CCPA
- Managing consent and data usage in testing environments
- Third-party risk assessment involving AI tools
- Vendor due diligence for AI security solutions
- Internal policy development for AI usage
- Role-based access control for AI systems
- Audit logging of all AI interactions and decisions
- Handling false negatives and accountability
- Independent validation of AI findings
- Preparing for regulatory scrutiny of automated systems
- Continuous compliance monitoring with AI
Module 14: Career Advancement and Certification Preparation - How to showcase AI security testing on your resume
- Translating course projects into portfolio pieces
- Preparing for technical interviews involving AI security
- Discussing AI limitations and strengths in professional settings
- Communicating security ROI to non-technical stakeholders
- Negotiating roles with security and AI responsibilities
- Becoming a security champion within development teams
- Mentoring others in AI-powered testing practices
- Presenting findings to engineering and security leadership
- Contributing to open-source security tools with AI
- Staying updated on emerging AI security research
- Joining professional communities and forums
- Speaking at meetups and conferences about your experience
- Building credibility through certification and case studies
- Mapping skills to job roles like DevSecOps, AppSec Engineer, and SRE
Module 15: Certification, Lifelong Learning, and Next Steps - Final assessment: applying AI testing to a full application
- Submitting your capstone project for evaluation
- Review process and feedback timeline
- Earning your Certificate of Completion from The Art of Service
- Understanding certification validity and renewal
- Sharing your credential on LinkedIn and GitHub
- Verifying certification status for employers
- Accessing the alumni network of AI security practitioners
- Receiving curated updates on new AI security techniques
- Participating in advanced topic deep dives
- Contributing to community knowledge bases
- Accessing new modules as they are released
- Tracking your progress with built-in learning analytics
- Using gamification elements to maintain momentum
- Setting your next career milestone in application security
- Writing scripts that leverage AI APIs for vulnerability scanning
- Using Python to interact with AI security endpoints
- Batch processing code files for AI analysis
- Parsing and structuring AI-generated JSON responses
- Automatically generating remediation suggestions
- Creating dashboards from AI test results
- Scheduling recurring AI-powered scans with cron and GitHub Actions
- Combining traditional scanners with AI augmentation
- Error handling and retry logic in AI workflows
- Rate limiting and API cost management
- Storing and versioning historical AI scan data
- Integrating with issue tracking systems like Jira
- Tagging vulnerabilities by severity, component, and developer
- Automating triage workflows using AI classification
- Testing the reliability of AI-driven automation
Module 7: AI-Driven Dynamic and Interactive Testing - Automating web application crawling with intelligent navigation
- AI-based input generation for fuzz testing
- Predicting likely attack vectors based on application structure
- Session handling and authentication in automated DAST
- Discovering hidden endpoints using pattern analysis
- Finding broken access controls with behavioural AI
- Simulating real attacker logic with reinforcement learning
- Testing API endpoints at scale using AI-guided exploration
- Validating GraphQL security with AI-powered query analysis
- Rate limiting bypass detection using adaptive testing
- Session fixation and token leakage detection methods
- Client-side security issues identified through AI DOM analysis
- Automated comparison of expected vs actual API responses
- Tracking state changes to detect business logic flaws
- Generating realistic test data for penetration scenarios
Module 8: Securing CI/CD Pipelines with AI Feedback - Embedding security gates in build pipelines
- Fail-fast strategies for critical vulnerability detection
- Using AI to determine if a vulnerability is exploitable
- Auto-assigning security tickets based on code ownership
- Providing fix guidance directly in pull request comments
- Generating AI-powered commit message reviews
- Blocking merges based on AI risk scoring
- Whitelisting known issues with AI context awareness
- Handling legacy debt without blocking development
- Measuring pipeline security posture over time
- Reducing developer friction with contextual alerts
- Customizing feedback tone and language by team
- Integrating with Slack and Teams for real-time notifications
- Creating audit trails of AI-driven security decisions
- Scaling pipeline security across 100+ repositories
Module 9: Cloud-Native and Container Security with AI - Analyzing Dockerfiles for insecure configurations
- Scanning container images using AI-enhanced tools
- Detecting hardcoded secrets in build layers
- Monitoring Kubernetes manifests for privilege escalation risks
- AI analysis of Helm chart security patterns
- Identifying misconfigurations in Terraform and Pulumi code
- Real-time drift detection in cloud infrastructure
- Predicting IAM policy overreach using usage patterns
- Automated compliance checking against CIS benchmarks
- AI-powered detection of shadow IT resources
- Serverless function security analysis with AI
- Protecting environment variables and configuration files
- Scanning third-party container registries
- Generating secure defaults from existing infrastructure
- Cost-aware security: balancing protection and performance
Module 10: Advanced AI Techniques in Threat Intelligence - Harvesting and processing threat intelligence feeds
- Using AI to correlate vulnerabilities with active exploits
- Predicting emerging attack trends based on dark web data
- Automated adversary simulation using generative AI
- Generating realistic phishing payloads for training
- Tracking exploit publication timelines and patch urgency
- Identifying zero-day indicators through anomaly detection
- Linking CVE data to internal asset inventories
- Creating dynamic risk heatmaps powered by AI
- Automated reporting for executive security summaries
- Summarizing complex security events in plain language
- AI-assisted root cause analysis of breaches
- Temporal analysis of attack sequences
- Attribution modeling and threat actor profiling
- Scenario planning for high-impact security events
Module 11: AI in Mobile and API Security Testing - Analyzing Android APKs and iOS binaries with AI
- Detecting insecure API endpoints in mobile apps
- Reverse engineering protection analysis using pattern recognition
- AI-powered detection of jailbreak and root detection bypass
- Identifying insecure local data storage programmatically
- Testing GraphQL, gRPC, and REST APIs at scale
- Discovering undocumented API endpoints using heuristics
- Validating proper rate limiting and authentication
- Generating test cases from OpenAPI and Swagger specs
- Detecting mass assignment and IDOR vulnerabilities
- AI analysis of request/response patterns for anomalies
- Testing API version deprecation and migration paths
- Validating proper error handling and disclosure
- Automated fuzzing of complex API parameters
- Session management analysis across API calls
Module 12: Practical Projects and Real-World Simulations - Project 1: Build an AI-augmented SAST scanner for Python
- Project 2: Create a GitHub bot that comments on security risks
- Project 3: Develop an AI-powered vulnerability triage system
- Project 4: Automate OWASP ZAP scans with AI result analysis
- Project 5: Integrate AI feedback into a Node.js CI pipeline
- Simulating a real breach scenario with AI detection
- Designing AI-driven regression security tests
- Creating a security knowledge base using AI summarization
- Building a dashboard to track AI testing KPIs
- Generating executive reports from security data
- Conducting a full-stack security audit using AI tools
- Red team vs blue team exercise with AI assistance
- Automating security documentation from code metadata
- Testing AI mitigation strategies against known exploits
- Measuring ROI of AI in testing efficiency and coverage
Module 13: Governance, Compliance, and Audit Readiness - Documenting AI-assisted testing processes for auditors
- Proving due diligence in vulnerability management
- Creating evidence trails for AI-generated findings
- Handling explainability requirements in regulated industries
- Ensuring AI tools comply with GDPR, HIPAA, and CCPA
- Managing consent and data usage in testing environments
- Third-party risk assessment involving AI tools
- Vendor due diligence for AI security solutions
- Internal policy development for AI usage
- Role-based access control for AI systems
- Audit logging of all AI interactions and decisions
- Handling false negatives and accountability
- Independent validation of AI findings
- Preparing for regulatory scrutiny of automated systems
- Continuous compliance monitoring with AI
Module 14: Career Advancement and Certification Preparation - How to showcase AI security testing on your resume
- Translating course projects into portfolio pieces
- Preparing for technical interviews involving AI security
- Discussing AI limitations and strengths in professional settings
- Communicating security ROI to non-technical stakeholders
- Negotiating roles with security and AI responsibilities
- Becoming a security champion within development teams
- Mentoring others in AI-powered testing practices
- Presenting findings to engineering and security leadership
- Contributing to open-source security tools with AI
- Staying updated on emerging AI security research
- Joining professional communities and forums
- Speaking at meetups and conferences about your experience
- Building credibility through certification and case studies
- Mapping skills to job roles like DevSecOps, AppSec Engineer, and SRE
Module 15: Certification, Lifelong Learning, and Next Steps - Final assessment: applying AI testing to a full application
- Submitting your capstone project for evaluation
- Review process and feedback timeline
- Earning your Certificate of Completion from The Art of Service
- Understanding certification validity and renewal
- Sharing your credential on LinkedIn and GitHub
- Verifying certification status for employers
- Accessing the alumni network of AI security practitioners
- Receiving curated updates on new AI security techniques
- Participating in advanced topic deep dives
- Contributing to community knowledge bases
- Accessing new modules as they are released
- Tracking your progress with built-in learning analytics
- Using gamification elements to maintain momentum
- Setting your next career milestone in application security
- Embedding security gates in build pipelines
- Fail-fast strategies for critical vulnerability detection
- Using AI to determine if a vulnerability is exploitable
- Auto-assigning security tickets based on code ownership
- Providing fix guidance directly in pull request comments
- Generating AI-powered commit message reviews
- Blocking merges based on AI risk scoring
- Whitelisting known issues with AI context awareness
- Handling legacy debt without blocking development
- Measuring pipeline security posture over time
- Reducing developer friction with contextual alerts
- Customizing feedback tone and language by team
- Integrating with Slack and Teams for real-time notifications
- Creating audit trails of AI-driven security decisions
- Scaling pipeline security across 100+ repositories
Module 9: Cloud-Native and Container Security with AI - Analyzing Dockerfiles for insecure configurations
- Scanning container images using AI-enhanced tools
- Detecting hardcoded secrets in build layers
- Monitoring Kubernetes manifests for privilege escalation risks
- AI analysis of Helm chart security patterns
- Identifying misconfigurations in Terraform and Pulumi code
- Real-time drift detection in cloud infrastructure
- Predicting IAM policy overreach using usage patterns
- Automated compliance checking against CIS benchmarks
- AI-powered detection of shadow IT resources
- Serverless function security analysis with AI
- Protecting environment variables and configuration files
- Scanning third-party container registries
- Generating secure defaults from existing infrastructure
- Cost-aware security: balancing protection and performance
Module 10: Advanced AI Techniques in Threat Intelligence - Harvesting and processing threat intelligence feeds
- Using AI to correlate vulnerabilities with active exploits
- Predicting emerging attack trends based on dark web data
- Automated adversary simulation using generative AI
- Generating realistic phishing payloads for training
- Tracking exploit publication timelines and patch urgency
- Identifying zero-day indicators through anomaly detection
- Linking CVE data to internal asset inventories
- Creating dynamic risk heatmaps powered by AI
- Automated reporting for executive security summaries
- Summarizing complex security events in plain language
- AI-assisted root cause analysis of breaches
- Temporal analysis of attack sequences
- Attribution modeling and threat actor profiling
- Scenario planning for high-impact security events
Module 11: AI in Mobile and API Security Testing - Analyzing Android APKs and iOS binaries with AI
- Detecting insecure API endpoints in mobile apps
- Reverse engineering protection analysis using pattern recognition
- AI-powered detection of jailbreak and root detection bypass
- Identifying insecure local data storage programmatically
- Testing GraphQL, gRPC, and REST APIs at scale
- Discovering undocumented API endpoints using heuristics
- Validating proper rate limiting and authentication
- Generating test cases from OpenAPI and Swagger specs
- Detecting mass assignment and IDOR vulnerabilities
- AI analysis of request/response patterns for anomalies
- Testing API version deprecation and migration paths
- Validating proper error handling and disclosure
- Automated fuzzing of complex API parameters
- Session management analysis across API calls
Module 12: Practical Projects and Real-World Simulations - Project 1: Build an AI-augmented SAST scanner for Python
- Project 2: Create a GitHub bot that comments on security risks
- Project 3: Develop an AI-powered vulnerability triage system
- Project 4: Automate OWASP ZAP scans with AI result analysis
- Project 5: Integrate AI feedback into a Node.js CI pipeline
- Simulating a real breach scenario with AI detection
- Designing AI-driven regression security tests
- Creating a security knowledge base using AI summarization
- Building a dashboard to track AI testing KPIs
- Generating executive reports from security data
- Conducting a full-stack security audit using AI tools
- Red team vs blue team exercise with AI assistance
- Automating security documentation from code metadata
- Testing AI mitigation strategies against known exploits
- Measuring ROI of AI in testing efficiency and coverage
Module 13: Governance, Compliance, and Audit Readiness - Documenting AI-assisted testing processes for auditors
- Proving due diligence in vulnerability management
- Creating evidence trails for AI-generated findings
- Handling explainability requirements in regulated industries
- Ensuring AI tools comply with GDPR, HIPAA, and CCPA
- Managing consent and data usage in testing environments
- Third-party risk assessment involving AI tools
- Vendor due diligence for AI security solutions
- Internal policy development for AI usage
- Role-based access control for AI systems
- Audit logging of all AI interactions and decisions
- Handling false negatives and accountability
- Independent validation of AI findings
- Preparing for regulatory scrutiny of automated systems
- Continuous compliance monitoring with AI
Module 14: Career Advancement and Certification Preparation - How to showcase AI security testing on your resume
- Translating course projects into portfolio pieces
- Preparing for technical interviews involving AI security
- Discussing AI limitations and strengths in professional settings
- Communicating security ROI to non-technical stakeholders
- Negotiating roles with security and AI responsibilities
- Becoming a security champion within development teams
- Mentoring others in AI-powered testing practices
- Presenting findings to engineering and security leadership
- Contributing to open-source security tools with AI
- Staying updated on emerging AI security research
- Joining professional communities and forums
- Speaking at meetups and conferences about your experience
- Building credibility through certification and case studies
- Mapping skills to job roles like DevSecOps, AppSec Engineer, and SRE
Module 15: Certification, Lifelong Learning, and Next Steps - Final assessment: applying AI testing to a full application
- Submitting your capstone project for evaluation
- Review process and feedback timeline
- Earning your Certificate of Completion from The Art of Service
- Understanding certification validity and renewal
- Sharing your credential on LinkedIn and GitHub
- Verifying certification status for employers
- Accessing the alumni network of AI security practitioners
- Receiving curated updates on new AI security techniques
- Participating in advanced topic deep dives
- Contributing to community knowledge bases
- Accessing new modules as they are released
- Tracking your progress with built-in learning analytics
- Using gamification elements to maintain momentum
- Setting your next career milestone in application security
- Harvesting and processing threat intelligence feeds
- Using AI to correlate vulnerabilities with active exploits
- Predicting emerging attack trends based on dark web data
- Automated adversary simulation using generative AI
- Generating realistic phishing payloads for training
- Tracking exploit publication timelines and patch urgency
- Identifying zero-day indicators through anomaly detection
- Linking CVE data to internal asset inventories
- Creating dynamic risk heatmaps powered by AI
- Automated reporting for executive security summaries
- Summarizing complex security events in plain language
- AI-assisted root cause analysis of breaches
- Temporal analysis of attack sequences
- Attribution modeling and threat actor profiling
- Scenario planning for high-impact security events
Module 11: AI in Mobile and API Security Testing - Analyzing Android APKs and iOS binaries with AI
- Detecting insecure API endpoints in mobile apps
- Reverse engineering protection analysis using pattern recognition
- AI-powered detection of jailbreak and root detection bypass
- Identifying insecure local data storage programmatically
- Testing GraphQL, gRPC, and REST APIs at scale
- Discovering undocumented API endpoints using heuristics
- Validating proper rate limiting and authentication
- Generating test cases from OpenAPI and Swagger specs
- Detecting mass assignment and IDOR vulnerabilities
- AI analysis of request/response patterns for anomalies
- Testing API version deprecation and migration paths
- Validating proper error handling and disclosure
- Automated fuzzing of complex API parameters
- Session management analysis across API calls
Module 12: Practical Projects and Real-World Simulations - Project 1: Build an AI-augmented SAST scanner for Python
- Project 2: Create a GitHub bot that comments on security risks
- Project 3: Develop an AI-powered vulnerability triage system
- Project 4: Automate OWASP ZAP scans with AI result analysis
- Project 5: Integrate AI feedback into a Node.js CI pipeline
- Simulating a real breach scenario with AI detection
- Designing AI-driven regression security tests
- Creating a security knowledge base using AI summarization
- Building a dashboard to track AI testing KPIs
- Generating executive reports from security data
- Conducting a full-stack security audit using AI tools
- Red team vs blue team exercise with AI assistance
- Automating security documentation from code metadata
- Testing AI mitigation strategies against known exploits
- Measuring ROI of AI in testing efficiency and coverage
Module 13: Governance, Compliance, and Audit Readiness - Documenting AI-assisted testing processes for auditors
- Proving due diligence in vulnerability management
- Creating evidence trails for AI-generated findings
- Handling explainability requirements in regulated industries
- Ensuring AI tools comply with GDPR, HIPAA, and CCPA
- Managing consent and data usage in testing environments
- Third-party risk assessment involving AI tools
- Vendor due diligence for AI security solutions
- Internal policy development for AI usage
- Role-based access control for AI systems
- Audit logging of all AI interactions and decisions
- Handling false negatives and accountability
- Independent validation of AI findings
- Preparing for regulatory scrutiny of automated systems
- Continuous compliance monitoring with AI
Module 14: Career Advancement and Certification Preparation - How to showcase AI security testing on your resume
- Translating course projects into portfolio pieces
- Preparing for technical interviews involving AI security
- Discussing AI limitations and strengths in professional settings
- Communicating security ROI to non-technical stakeholders
- Negotiating roles with security and AI responsibilities
- Becoming a security champion within development teams
- Mentoring others in AI-powered testing practices
- Presenting findings to engineering and security leadership
- Contributing to open-source security tools with AI
- Staying updated on emerging AI security research
- Joining professional communities and forums
- Speaking at meetups and conferences about your experience
- Building credibility through certification and case studies
- Mapping skills to job roles like DevSecOps, AppSec Engineer, and SRE
Module 15: Certification, Lifelong Learning, and Next Steps - Final assessment: applying AI testing to a full application
- Submitting your capstone project for evaluation
- Review process and feedback timeline
- Earning your Certificate of Completion from The Art of Service
- Understanding certification validity and renewal
- Sharing your credential on LinkedIn and GitHub
- Verifying certification status for employers
- Accessing the alumni network of AI security practitioners
- Receiving curated updates on new AI security techniques
- Participating in advanced topic deep dives
- Contributing to community knowledge bases
- Accessing new modules as they are released
- Tracking your progress with built-in learning analytics
- Using gamification elements to maintain momentum
- Setting your next career milestone in application security
- Project 1: Build an AI-augmented SAST scanner for Python
- Project 2: Create a GitHub bot that comments on security risks
- Project 3: Develop an AI-powered vulnerability triage system
- Project 4: Automate OWASP ZAP scans with AI result analysis
- Project 5: Integrate AI feedback into a Node.js CI pipeline
- Simulating a real breach scenario with AI detection
- Designing AI-driven regression security tests
- Creating a security knowledge base using AI summarization
- Building a dashboard to track AI testing KPIs
- Generating executive reports from security data
- Conducting a full-stack security audit using AI tools
- Red team vs blue team exercise with AI assistance
- Automating security documentation from code metadata
- Testing AI mitigation strategies against known exploits
- Measuring ROI of AI in testing efficiency and coverage
Module 13: Governance, Compliance, and Audit Readiness - Documenting AI-assisted testing processes for auditors
- Proving due diligence in vulnerability management
- Creating evidence trails for AI-generated findings
- Handling explainability requirements in regulated industries
- Ensuring AI tools comply with GDPR, HIPAA, and CCPA
- Managing consent and data usage in testing environments
- Third-party risk assessment involving AI tools
- Vendor due diligence for AI security solutions
- Internal policy development for AI usage
- Role-based access control for AI systems
- Audit logging of all AI interactions and decisions
- Handling false negatives and accountability
- Independent validation of AI findings
- Preparing for regulatory scrutiny of automated systems
- Continuous compliance monitoring with AI
Module 14: Career Advancement and Certification Preparation - How to showcase AI security testing on your resume
- Translating course projects into portfolio pieces
- Preparing for technical interviews involving AI security
- Discussing AI limitations and strengths in professional settings
- Communicating security ROI to non-technical stakeholders
- Negotiating roles with security and AI responsibilities
- Becoming a security champion within development teams
- Mentoring others in AI-powered testing practices
- Presenting findings to engineering and security leadership
- Contributing to open-source security tools with AI
- Staying updated on emerging AI security research
- Joining professional communities and forums
- Speaking at meetups and conferences about your experience
- Building credibility through certification and case studies
- Mapping skills to job roles like DevSecOps, AppSec Engineer, and SRE
Module 15: Certification, Lifelong Learning, and Next Steps - Final assessment: applying AI testing to a full application
- Submitting your capstone project for evaluation
- Review process and feedback timeline
- Earning your Certificate of Completion from The Art of Service
- Understanding certification validity and renewal
- Sharing your credential on LinkedIn and GitHub
- Verifying certification status for employers
- Accessing the alumni network of AI security practitioners
- Receiving curated updates on new AI security techniques
- Participating in advanced topic deep dives
- Contributing to community knowledge bases
- Accessing new modules as they are released
- Tracking your progress with built-in learning analytics
- Using gamification elements to maintain momentum
- Setting your next career milestone in application security
- How to showcase AI security testing on your resume
- Translating course projects into portfolio pieces
- Preparing for technical interviews involving AI security
- Discussing AI limitations and strengths in professional settings
- Communicating security ROI to non-technical stakeholders
- Negotiating roles with security and AI responsibilities
- Becoming a security champion within development teams
- Mentoring others in AI-powered testing practices
- Presenting findings to engineering and security leadership
- Contributing to open-source security tools with AI
- Staying updated on emerging AI security research
- Joining professional communities and forums
- Speaking at meetups and conferences about your experience
- Building credibility through certification and case studies
- Mapping skills to job roles like DevSecOps, AppSec Engineer, and SRE