AI-Driven Incident Response: Mastering Threat Detection and Automation for Future-Proof Cybersecurity Careers
You're not behind. You’re in the right role, with the right intent-but you can feel it. The pressure is mounting. Attack vectors evolve overnight. Alerts flood in faster than your team can triage. Manual workflows aren’t cutting it anymore and you’re being asked to do more with less, while leadership demands faster response times, better visibility, and board-level confidence. Meanwhile, teams that leverage AI are cutting detection-to-response times by 70%, automating containment, and earning promotions faster than ever. That future isn't coming-it's already here. This isn’t about learning theory. It’s about transformation. The AI-Driven Incident Response: Mastering Threat Detection and Automation for Future-Proof Cybersecurity Careers programme is designed to take you from overwhelmed to in control, from reactive to proactive, in just 30 days. You’ll build a fully operational threat detection framework powered by AI, validated through real-world implementation steps, and produce a documented incident response automation flow ready for executive review. Like Sarah M., a Tier 2 analyst at a financial institution, who completed this course and within six weeks automated her SOC's phishing alert triage-reducing false positives by 64% and earning her first promotion to Threat Intelligence Associate. She didn’t have a data science background. She just followed the system. If she can do it, you can too. Here’s how this course is structured to help you get there.Course Format & Delivery: Instant, Self-Paced, Risk-Free Access Self-Paced Learning, Immediate Access This course is designed for professionals like you-juggling shift work, escalation calls, or a full-time role-yet committed to career growth. You begin when you're ready, progress on your schedule, and never miss a deadline. No live sessions, no fixed start dates. You own your pace. Lifetime Access, Future-Proof Updates Included Enrol once, learn forever. Cybersecurity changes daily. That’s why every update to threat frameworks, detection techniques, or AI integration tools is included at no extra cost. Your access never expires. Even five years from now, you’ll have the same rights to all new materials as day one. Learn Anywhere, Anytime-Mobile-Optimised & Global Access your materials securely 24/7 from any device-laptop, tablet, or smartphone. Whether you’re at the office, on call, or commuting, your progress syncs seamlessly. Designed for real cybersecurity workstyles, not academic lectures. Practical Timeline: 30 Days to First Implementation, 60 Days to Mastery Most learners complete the core framework in under four weeks, with the first actionable automation flow built in Days 7–10. Advanced modules integrate into existing SOAR and SIEM environments over the next 30–60 days. You’ll be applying concepts on the job from Week One. Dedicated Support & Expert Guidance Stuck on a detection logic rule? Unclear how to map AI confidence scores to incident severity? You’re not alone. Enrolled learners receive direct access to our instructor support portal for technical clarification, framework refinement, and implementation feedback. This is not crowd-sourced forums-just focused, expert-led guidance when you need it. Certificate of Completion Issued by The Art of Service Upon successful completion, you’ll receive a Certificate of Completion issued by The Art of Service. Globally recognised in enterprise cybersecurity, risk management, and compliance circles, this credential validates your mastery of AI-powered incident response and is shareable on LinkedIn, resumes, and internal promotions. No Hidden Fees. Transparent & Upfront Pricing. You pay one straightforward fee. No subscriptions, no auto-renewals. What you see is what you get-lifetime access, all content, all updates, full certification path. Period. Accepted Payment Methods We accept all major payment providers: Visa, Mastercard, PayPal. Secure checkout, instant confirmation. 100% Money-Back Guarantee: Try It Risk-Free Start the course, go through the first two modules, and if you don’t find immediate value in the threat modelling templates or AI response workflows, request a full refund. No questions, no delays. Your investment is completely protected. Enrolment & Access Process: Clear, Secure, & Peace of Mind After enrolling, you’ll receive an email confirmation immediately. Your course access details-including login and portal instructions-will be delivered separately once your materials are prepared. You’ll be guided step-by-step from confirmation to entry, ensuring a smooth, secure onboarding experience. Will This Work For Me? Absolutely. This course was built by senior incident responders for analysts, SOC leads, and cybersecurity architects who are tired of theoretical fluff. Whether you work in healthcare, finance, government, or cloud infrastructure, the framework is role-adaptive and tech-stack agnostic. And yes, even if you: - Don’t have an AI background,
- Don’t code daily,
- Are time-constrained,
- Or manage legacy systems-
-this system works. We’ve seen SIEM administrators with zero machine learning experience deploy AI-assisted escalation routing within three weeks. If you can run queries and document processes, you can automate them. This isn’t magic. It’s methodology. And the risk is entirely on us-your success, guaranteed or refunded.
Module 1: Foundations of AI-Powered Incident Response - Understanding the evolution of incident response: From manual to AI-driven
- Defining AI in cybersecurity: Narrow AI vs General AI explained
- Real-world use cases of AI in SOC operations
- Key differences between automation, orchestration, and AI
- Common myths and misconceptions about AI in IR
- Statistical vs behavioural AI models in threat detection
- The role of data in AI-driven security: Quality over volume
- Core components of an AI-ready SOC
- Mapping AI capabilities to MITRE ATT&CK framework stages
- Assessing organisational readiness for AI integration
- Identifying low-hanging automation opportunities
- Balancing speed, accuracy, and false positive rates
- Legal and compliance implications of AI decision-making in IR
- Establishing trust in AI-generated alerts and actions
- Introduction to confidence scoring and explainability in AI
Module 2: Data Engineering for Threat Intelligence - Types of data used in AI-driven IR: Logs, flows, endpoints, cloud trails
- Normalising and enriching raw telemetry for AI consumption
- Building a centralised data lake for incident correlation
- Data retention policies and privacy compliance in AI systems
- Feature engineering for threat detection models
- Selecting relevant data inputs for model training
- Handling missing, corrupted, or incomplete data
- Time-series data structuring for behavioural analysis
- Creating golden datasets from past incident records
- Data labelling techniques for supervised learning
- Automating data validation and integrity checks
- Integrating threat intelligence feeds into data pipelines
- Using APIs to stream real-time data into models
- Scaling data ingestion for enterprise environments
- Establishing data ownership and access controls
- Reducing data noise before AI processing
Module 3: AI Models for Anomaly and Threat Detection - Overview of machine learning types: Supervised, unsupervised, reinforcement
- Clustering algorithms for detecting unknown threats
- Classification models for categorising attack types
- Regression analysis for predicting attack impact
- Random forests and ensemble methods in malware detection
- Neural networks: When to use them in IR
- Autoencoders for detecting lateral movement
- Principal Component Analysis (PCA) for dimension reduction
- Bayesian networks for probabilistic threat assessment
- Natural Language Processing (NLP) for log analysis
- Temporal pattern recognition in attack sequences
- Model training lifecycle: From hypothesis to deployment
- Detecting model drift and retraining triggers
- Calibrating model sensitivity to reduce false positives
- Using confidence intervals to prioritise alerts
- Evaluating model performance: Precision, recall, F1-score
Module 4: Automated Incident Triage and Prioritisation - Automated alert scoring using AI confidence levels
- Dynamic severity assignment based on context
- Entity-based risk scoring: User, device, application
- Correlating multiple low-severity events into high-risk incidents
- Semantic analysis of alert descriptions for context
- Incident clustering to avoid duplicate handling
- Automated enrichment with Active Directory, IAM, and asset data
- Time-based alert suppression to reduce fatigue
- Automated de-duplication of phishing reports
- Prioritisation rules based on business criticality
- Handling incomplete data in triage decisions
- Building AI-assisted triage playbooks
- Routing incidents to appropriate teams based on content
- Automating initial ticket creation and assignment
- Integrating with ITSM tools for seamless handoff
- Measuring triage efficiency gains post-automation
Module 5: AI-Driven Threat Hunting and Proactive Detection - Shifting from reactive to proactive detection with AI
- Unsupervised learning for zero-day pattern discovery
- Baseline creation for normal user and system behaviour
- Deviation detection in Active Directory activity
- Identifying credential dumping and pass-the-hash
- Detecting data exfiltration through network entropy
- AI-enhanced YARA and Sigma rule generation
- Automated query suggestion for threat hunters
- Creating hypothesis-driven hunting campaigns
- Using AI to prioritise hunting queues
- Validating AI-generated leads with forensic workflows
- Generating automated threat hunting reports
- Integrating threat hunting findings into detection rules
- Measuring detection efficacy over time
- Automated feedback loops for model improvement
- Sharing threat patterns across peer organisations securely
Module 6: SOAR and AI Integration Strategies - Understanding SOAR architecture and capabilities
- Where AI fits within SOAR workflows
- Automating playbook decision logic with AI
- Dynamic playbook selection based on incident context
- AI-guided enrichment steps within playbooks
- Automating containment actions with risk assessment
- Using AI to validate containment success
- Automated evidence collection and chain-of-custody logging
- Integrating external threat intelligence into playbooks
- Automated notification and escalation routing
- Adaptive response based on attacker behaviour
- Time-based response throttling to prevent overreaction
- Parallel execution of non-conflicting actions
- Validating playbook outcomes against success criteria
- AI-driven playbook optimisation over time
- Testing and versioning AI-enhanced playbooks
Module 7: Real-World Threat Scenarios and Response Automation - Simulated phishing campaign detection and response
- Automated identification of malicious Office macros
- Endpoint ransomware behaviour detection
- Automated isolation of infected devices
- Cloud credential compromise detection
- Automated revocation and rotation of compromised keys
- Detecting lateral movement through SSH and RDP
- Automating user session termination on high-risk sign-ins
- Identifying data staging and compression before exfil
- Automating network segmentation upon detection
- Detecting DNS tunneling through anomaly detection
- Blocking malicious domains in real-time
- Identifying insider threat indicators through behavioural AI
- Automating HR and legal notifications on policy breaches
- Handling false positives caused by AI models
- Creating rollback procedures for automated actions
Module 8: Explainability, Auditability, and Governance of AI Systems - Why explainable AI (XAI) matters in incident response
- Generating human-readable justifications for AI actions
- Creating audit trails for AI-driven decisions
- Compliance requirements for automated security actions
- Documentation standards for AI model usage
- Establishing approval layers for high-risk automations
- Human-in-the-loop vs full automation: When to apply each
- Logging model input, output, and decision pathways
- Version control for AI models and rule sets
- Conducting third-party AI audits
- Mapping AI actions to NIST or ISO 27001 controls
- Board-level reporting on AI performance and risk
- Creating an AI oversight committee
- Handling model bias in security contexts
- Ensuring vendor transparency in third-party AI tools
- Maintaining chain of custody in AI-influenced investigations
Module 9: Performance Monitoring and Incident Metrics - Key performance indicators for AI-driven IR
- Measuring mean time to detect (MTTD) reduction
- Tracking mean time to respond (MTTR) improvements
- Calculating false positive and false negative rates
- Tracking automation success and failure rates
- Measuring analyst productivity gains
- Creating dashboards for AI system health
- Monitoring model confidence over time
- Alert volume trends before and after AI deployment
- Analyst workload reduction metrics
- Cost-benefit analysis of AI implementation
- ROI calculation for automation initiatives
- Incident closure rate improvements
- Tracking repeat incidents and recurrence patterns
- Using metrics to justify AI investment to leadership
- Continuous feedback loop between metrics and improvement
Module 10: Future-Proofing Your AI-Driven SOC - Building a culture of AI adoption in security teams
- Upskilling analysts for AI collaboration
- Creating internal AI champions and advocates
- Developing an AI integration roadmap
- Prioritising automation use cases by impact and feasibility
- Budgeting for AI tools and infrastructure
- Evaluating third-party AI vendors and solutions
- Integrating AI into incident response plans (IRPs)
- Conducting AI-informed tabletop exercises
- Testing AI components during red team engagements
- Handling AI failure scenarios and fallback procedures
- Incident escalation paths when AI is compromised
- Preparing for AI-targeted attacks
- Ethical considerations in autonomous response
- Long-term maintenance of AI systems
- Staying updated on emerging AI threats and defences
Module 11: Capstone Project: Build Your AI-Driven Incident Workflow - Selecting a high-impact use case from your environment
- Defining success criteria and KPIs
- Data sourcing and preparation for your model
- Designing detection logic and confidence thresholds
- Mapping the full incident lifecycle: Detect, Triage, Respond, Report
- Building automated enrichment steps
- Designing automated containment actions
- Creating human review checkpoints for critical actions
- Generating audit-ready documentation for each step
- Testing workflow with historical incident data
- Measuring accuracy and efficiency gains
- Refining thresholds and logic based on results
- Preparing a presentation for internal stakeholders
- Documenting lessons learned and next steps
- Submitting your project for feedback
- Receiving expert evaluation and improvement guidance
Module 12: Certification, Career Advancement, and Next Steps - Requirements for Certificate of Completion
- Final assessment structure and expectations
- Submitting your capstone project for review
- Receiving your official certificate from The Art of Service
- Adding your credential to LinkedIn and professional profiles
- Highlighting AI-driven IR experience in job applications
- Transitioning from analyst to automation specialist
- Becoming an internal AI advisor
- Preparing for interviews with real-world implementation stories
- Networking within the AI security community
- Accessing exclusive alumni resources
- Advanced learning paths in AI and cybersecurity
- Contributing to open-source AI security projects
- Mentorship opportunities for new learners
- Staying updated through The Art of Service newsletters
- Expanding into AI roles: Threat Data Scientist, SOC Architect, CISO Advisor
- Understanding the evolution of incident response: From manual to AI-driven
- Defining AI in cybersecurity: Narrow AI vs General AI explained
- Real-world use cases of AI in SOC operations
- Key differences between automation, orchestration, and AI
- Common myths and misconceptions about AI in IR
- Statistical vs behavioural AI models in threat detection
- The role of data in AI-driven security: Quality over volume
- Core components of an AI-ready SOC
- Mapping AI capabilities to MITRE ATT&CK framework stages
- Assessing organisational readiness for AI integration
- Identifying low-hanging automation opportunities
- Balancing speed, accuracy, and false positive rates
- Legal and compliance implications of AI decision-making in IR
- Establishing trust in AI-generated alerts and actions
- Introduction to confidence scoring and explainability in AI
Module 2: Data Engineering for Threat Intelligence - Types of data used in AI-driven IR: Logs, flows, endpoints, cloud trails
- Normalising and enriching raw telemetry for AI consumption
- Building a centralised data lake for incident correlation
- Data retention policies and privacy compliance in AI systems
- Feature engineering for threat detection models
- Selecting relevant data inputs for model training
- Handling missing, corrupted, or incomplete data
- Time-series data structuring for behavioural analysis
- Creating golden datasets from past incident records
- Data labelling techniques for supervised learning
- Automating data validation and integrity checks
- Integrating threat intelligence feeds into data pipelines
- Using APIs to stream real-time data into models
- Scaling data ingestion for enterprise environments
- Establishing data ownership and access controls
- Reducing data noise before AI processing
Module 3: AI Models for Anomaly and Threat Detection - Overview of machine learning types: Supervised, unsupervised, reinforcement
- Clustering algorithms for detecting unknown threats
- Classification models for categorising attack types
- Regression analysis for predicting attack impact
- Random forests and ensemble methods in malware detection
- Neural networks: When to use them in IR
- Autoencoders for detecting lateral movement
- Principal Component Analysis (PCA) for dimension reduction
- Bayesian networks for probabilistic threat assessment
- Natural Language Processing (NLP) for log analysis
- Temporal pattern recognition in attack sequences
- Model training lifecycle: From hypothesis to deployment
- Detecting model drift and retraining triggers
- Calibrating model sensitivity to reduce false positives
- Using confidence intervals to prioritise alerts
- Evaluating model performance: Precision, recall, F1-score
Module 4: Automated Incident Triage and Prioritisation - Automated alert scoring using AI confidence levels
- Dynamic severity assignment based on context
- Entity-based risk scoring: User, device, application
- Correlating multiple low-severity events into high-risk incidents
- Semantic analysis of alert descriptions for context
- Incident clustering to avoid duplicate handling
- Automated enrichment with Active Directory, IAM, and asset data
- Time-based alert suppression to reduce fatigue
- Automated de-duplication of phishing reports
- Prioritisation rules based on business criticality
- Handling incomplete data in triage decisions
- Building AI-assisted triage playbooks
- Routing incidents to appropriate teams based on content
- Automating initial ticket creation and assignment
- Integrating with ITSM tools for seamless handoff
- Measuring triage efficiency gains post-automation
Module 5: AI-Driven Threat Hunting and Proactive Detection - Shifting from reactive to proactive detection with AI
- Unsupervised learning for zero-day pattern discovery
- Baseline creation for normal user and system behaviour
- Deviation detection in Active Directory activity
- Identifying credential dumping and pass-the-hash
- Detecting data exfiltration through network entropy
- AI-enhanced YARA and Sigma rule generation
- Automated query suggestion for threat hunters
- Creating hypothesis-driven hunting campaigns
- Using AI to prioritise hunting queues
- Validating AI-generated leads with forensic workflows
- Generating automated threat hunting reports
- Integrating threat hunting findings into detection rules
- Measuring detection efficacy over time
- Automated feedback loops for model improvement
- Sharing threat patterns across peer organisations securely
Module 6: SOAR and AI Integration Strategies - Understanding SOAR architecture and capabilities
- Where AI fits within SOAR workflows
- Automating playbook decision logic with AI
- Dynamic playbook selection based on incident context
- AI-guided enrichment steps within playbooks
- Automating containment actions with risk assessment
- Using AI to validate containment success
- Automated evidence collection and chain-of-custody logging
- Integrating external threat intelligence into playbooks
- Automated notification and escalation routing
- Adaptive response based on attacker behaviour
- Time-based response throttling to prevent overreaction
- Parallel execution of non-conflicting actions
- Validating playbook outcomes against success criteria
- AI-driven playbook optimisation over time
- Testing and versioning AI-enhanced playbooks
Module 7: Real-World Threat Scenarios and Response Automation - Simulated phishing campaign detection and response
- Automated identification of malicious Office macros
- Endpoint ransomware behaviour detection
- Automated isolation of infected devices
- Cloud credential compromise detection
- Automated revocation and rotation of compromised keys
- Detecting lateral movement through SSH and RDP
- Automating user session termination on high-risk sign-ins
- Identifying data staging and compression before exfil
- Automating network segmentation upon detection
- Detecting DNS tunneling through anomaly detection
- Blocking malicious domains in real-time
- Identifying insider threat indicators through behavioural AI
- Automating HR and legal notifications on policy breaches
- Handling false positives caused by AI models
- Creating rollback procedures for automated actions
Module 8: Explainability, Auditability, and Governance of AI Systems - Why explainable AI (XAI) matters in incident response
- Generating human-readable justifications for AI actions
- Creating audit trails for AI-driven decisions
- Compliance requirements for automated security actions
- Documentation standards for AI model usage
- Establishing approval layers for high-risk automations
- Human-in-the-loop vs full automation: When to apply each
- Logging model input, output, and decision pathways
- Version control for AI models and rule sets
- Conducting third-party AI audits
- Mapping AI actions to NIST or ISO 27001 controls
- Board-level reporting on AI performance and risk
- Creating an AI oversight committee
- Handling model bias in security contexts
- Ensuring vendor transparency in third-party AI tools
- Maintaining chain of custody in AI-influenced investigations
Module 9: Performance Monitoring and Incident Metrics - Key performance indicators for AI-driven IR
- Measuring mean time to detect (MTTD) reduction
- Tracking mean time to respond (MTTR) improvements
- Calculating false positive and false negative rates
- Tracking automation success and failure rates
- Measuring analyst productivity gains
- Creating dashboards for AI system health
- Monitoring model confidence over time
- Alert volume trends before and after AI deployment
- Analyst workload reduction metrics
- Cost-benefit analysis of AI implementation
- ROI calculation for automation initiatives
- Incident closure rate improvements
- Tracking repeat incidents and recurrence patterns
- Using metrics to justify AI investment to leadership
- Continuous feedback loop between metrics and improvement
Module 10: Future-Proofing Your AI-Driven SOC - Building a culture of AI adoption in security teams
- Upskilling analysts for AI collaboration
- Creating internal AI champions and advocates
- Developing an AI integration roadmap
- Prioritising automation use cases by impact and feasibility
- Budgeting for AI tools and infrastructure
- Evaluating third-party AI vendors and solutions
- Integrating AI into incident response plans (IRPs)
- Conducting AI-informed tabletop exercises
- Testing AI components during red team engagements
- Handling AI failure scenarios and fallback procedures
- Incident escalation paths when AI is compromised
- Preparing for AI-targeted attacks
- Ethical considerations in autonomous response
- Long-term maintenance of AI systems
- Staying updated on emerging AI threats and defences
Module 11: Capstone Project: Build Your AI-Driven Incident Workflow - Selecting a high-impact use case from your environment
- Defining success criteria and KPIs
- Data sourcing and preparation for your model
- Designing detection logic and confidence thresholds
- Mapping the full incident lifecycle: Detect, Triage, Respond, Report
- Building automated enrichment steps
- Designing automated containment actions
- Creating human review checkpoints for critical actions
- Generating audit-ready documentation for each step
- Testing workflow with historical incident data
- Measuring accuracy and efficiency gains
- Refining thresholds and logic based on results
- Preparing a presentation for internal stakeholders
- Documenting lessons learned and next steps
- Submitting your project for feedback
- Receiving expert evaluation and improvement guidance
Module 12: Certification, Career Advancement, and Next Steps - Requirements for Certificate of Completion
- Final assessment structure and expectations
- Submitting your capstone project for review
- Receiving your official certificate from The Art of Service
- Adding your credential to LinkedIn and professional profiles
- Highlighting AI-driven IR experience in job applications
- Transitioning from analyst to automation specialist
- Becoming an internal AI advisor
- Preparing for interviews with real-world implementation stories
- Networking within the AI security community
- Accessing exclusive alumni resources
- Advanced learning paths in AI and cybersecurity
- Contributing to open-source AI security projects
- Mentorship opportunities for new learners
- Staying updated through The Art of Service newsletters
- Expanding into AI roles: Threat Data Scientist, SOC Architect, CISO Advisor
- Overview of machine learning types: Supervised, unsupervised, reinforcement
- Clustering algorithms for detecting unknown threats
- Classification models for categorising attack types
- Regression analysis for predicting attack impact
- Random forests and ensemble methods in malware detection
- Neural networks: When to use them in IR
- Autoencoders for detecting lateral movement
- Principal Component Analysis (PCA) for dimension reduction
- Bayesian networks for probabilistic threat assessment
- Natural Language Processing (NLP) for log analysis
- Temporal pattern recognition in attack sequences
- Model training lifecycle: From hypothesis to deployment
- Detecting model drift and retraining triggers
- Calibrating model sensitivity to reduce false positives
- Using confidence intervals to prioritise alerts
- Evaluating model performance: Precision, recall, F1-score
Module 4: Automated Incident Triage and Prioritisation - Automated alert scoring using AI confidence levels
- Dynamic severity assignment based on context
- Entity-based risk scoring: User, device, application
- Correlating multiple low-severity events into high-risk incidents
- Semantic analysis of alert descriptions for context
- Incident clustering to avoid duplicate handling
- Automated enrichment with Active Directory, IAM, and asset data
- Time-based alert suppression to reduce fatigue
- Automated de-duplication of phishing reports
- Prioritisation rules based on business criticality
- Handling incomplete data in triage decisions
- Building AI-assisted triage playbooks
- Routing incidents to appropriate teams based on content
- Automating initial ticket creation and assignment
- Integrating with ITSM tools for seamless handoff
- Measuring triage efficiency gains post-automation
Module 5: AI-Driven Threat Hunting and Proactive Detection - Shifting from reactive to proactive detection with AI
- Unsupervised learning for zero-day pattern discovery
- Baseline creation for normal user and system behaviour
- Deviation detection in Active Directory activity
- Identifying credential dumping and pass-the-hash
- Detecting data exfiltration through network entropy
- AI-enhanced YARA and Sigma rule generation
- Automated query suggestion for threat hunters
- Creating hypothesis-driven hunting campaigns
- Using AI to prioritise hunting queues
- Validating AI-generated leads with forensic workflows
- Generating automated threat hunting reports
- Integrating threat hunting findings into detection rules
- Measuring detection efficacy over time
- Automated feedback loops for model improvement
- Sharing threat patterns across peer organisations securely
Module 6: SOAR and AI Integration Strategies - Understanding SOAR architecture and capabilities
- Where AI fits within SOAR workflows
- Automating playbook decision logic with AI
- Dynamic playbook selection based on incident context
- AI-guided enrichment steps within playbooks
- Automating containment actions with risk assessment
- Using AI to validate containment success
- Automated evidence collection and chain-of-custody logging
- Integrating external threat intelligence into playbooks
- Automated notification and escalation routing
- Adaptive response based on attacker behaviour
- Time-based response throttling to prevent overreaction
- Parallel execution of non-conflicting actions
- Validating playbook outcomes against success criteria
- AI-driven playbook optimisation over time
- Testing and versioning AI-enhanced playbooks
Module 7: Real-World Threat Scenarios and Response Automation - Simulated phishing campaign detection and response
- Automated identification of malicious Office macros
- Endpoint ransomware behaviour detection
- Automated isolation of infected devices
- Cloud credential compromise detection
- Automated revocation and rotation of compromised keys
- Detecting lateral movement through SSH and RDP
- Automating user session termination on high-risk sign-ins
- Identifying data staging and compression before exfil
- Automating network segmentation upon detection
- Detecting DNS tunneling through anomaly detection
- Blocking malicious domains in real-time
- Identifying insider threat indicators through behavioural AI
- Automating HR and legal notifications on policy breaches
- Handling false positives caused by AI models
- Creating rollback procedures for automated actions
Module 8: Explainability, Auditability, and Governance of AI Systems - Why explainable AI (XAI) matters in incident response
- Generating human-readable justifications for AI actions
- Creating audit trails for AI-driven decisions
- Compliance requirements for automated security actions
- Documentation standards for AI model usage
- Establishing approval layers for high-risk automations
- Human-in-the-loop vs full automation: When to apply each
- Logging model input, output, and decision pathways
- Version control for AI models and rule sets
- Conducting third-party AI audits
- Mapping AI actions to NIST or ISO 27001 controls
- Board-level reporting on AI performance and risk
- Creating an AI oversight committee
- Handling model bias in security contexts
- Ensuring vendor transparency in third-party AI tools
- Maintaining chain of custody in AI-influenced investigations
Module 9: Performance Monitoring and Incident Metrics - Key performance indicators for AI-driven IR
- Measuring mean time to detect (MTTD) reduction
- Tracking mean time to respond (MTTR) improvements
- Calculating false positive and false negative rates
- Tracking automation success and failure rates
- Measuring analyst productivity gains
- Creating dashboards for AI system health
- Monitoring model confidence over time
- Alert volume trends before and after AI deployment
- Analyst workload reduction metrics
- Cost-benefit analysis of AI implementation
- ROI calculation for automation initiatives
- Incident closure rate improvements
- Tracking repeat incidents and recurrence patterns
- Using metrics to justify AI investment to leadership
- Continuous feedback loop between metrics and improvement
Module 10: Future-Proofing Your AI-Driven SOC - Building a culture of AI adoption in security teams
- Upskilling analysts for AI collaboration
- Creating internal AI champions and advocates
- Developing an AI integration roadmap
- Prioritising automation use cases by impact and feasibility
- Budgeting for AI tools and infrastructure
- Evaluating third-party AI vendors and solutions
- Integrating AI into incident response plans (IRPs)
- Conducting AI-informed tabletop exercises
- Testing AI components during red team engagements
- Handling AI failure scenarios and fallback procedures
- Incident escalation paths when AI is compromised
- Preparing for AI-targeted attacks
- Ethical considerations in autonomous response
- Long-term maintenance of AI systems
- Staying updated on emerging AI threats and defences
Module 11: Capstone Project: Build Your AI-Driven Incident Workflow - Selecting a high-impact use case from your environment
- Defining success criteria and KPIs
- Data sourcing and preparation for your model
- Designing detection logic and confidence thresholds
- Mapping the full incident lifecycle: Detect, Triage, Respond, Report
- Building automated enrichment steps
- Designing automated containment actions
- Creating human review checkpoints for critical actions
- Generating audit-ready documentation for each step
- Testing workflow with historical incident data
- Measuring accuracy and efficiency gains
- Refining thresholds and logic based on results
- Preparing a presentation for internal stakeholders
- Documenting lessons learned and next steps
- Submitting your project for feedback
- Receiving expert evaluation and improvement guidance
Module 12: Certification, Career Advancement, and Next Steps - Requirements for Certificate of Completion
- Final assessment structure and expectations
- Submitting your capstone project for review
- Receiving your official certificate from The Art of Service
- Adding your credential to LinkedIn and professional profiles
- Highlighting AI-driven IR experience in job applications
- Transitioning from analyst to automation specialist
- Becoming an internal AI advisor
- Preparing for interviews with real-world implementation stories
- Networking within the AI security community
- Accessing exclusive alumni resources
- Advanced learning paths in AI and cybersecurity
- Contributing to open-source AI security projects
- Mentorship opportunities for new learners
- Staying updated through The Art of Service newsletters
- Expanding into AI roles: Threat Data Scientist, SOC Architect, CISO Advisor
- Shifting from reactive to proactive detection with AI
- Unsupervised learning for zero-day pattern discovery
- Baseline creation for normal user and system behaviour
- Deviation detection in Active Directory activity
- Identifying credential dumping and pass-the-hash
- Detecting data exfiltration through network entropy
- AI-enhanced YARA and Sigma rule generation
- Automated query suggestion for threat hunters
- Creating hypothesis-driven hunting campaigns
- Using AI to prioritise hunting queues
- Validating AI-generated leads with forensic workflows
- Generating automated threat hunting reports
- Integrating threat hunting findings into detection rules
- Measuring detection efficacy over time
- Automated feedback loops for model improvement
- Sharing threat patterns across peer organisations securely
Module 6: SOAR and AI Integration Strategies - Understanding SOAR architecture and capabilities
- Where AI fits within SOAR workflows
- Automating playbook decision logic with AI
- Dynamic playbook selection based on incident context
- AI-guided enrichment steps within playbooks
- Automating containment actions with risk assessment
- Using AI to validate containment success
- Automated evidence collection and chain-of-custody logging
- Integrating external threat intelligence into playbooks
- Automated notification and escalation routing
- Adaptive response based on attacker behaviour
- Time-based response throttling to prevent overreaction
- Parallel execution of non-conflicting actions
- Validating playbook outcomes against success criteria
- AI-driven playbook optimisation over time
- Testing and versioning AI-enhanced playbooks
Module 7: Real-World Threat Scenarios and Response Automation - Simulated phishing campaign detection and response
- Automated identification of malicious Office macros
- Endpoint ransomware behaviour detection
- Automated isolation of infected devices
- Cloud credential compromise detection
- Automated revocation and rotation of compromised keys
- Detecting lateral movement through SSH and RDP
- Automating user session termination on high-risk sign-ins
- Identifying data staging and compression before exfil
- Automating network segmentation upon detection
- Detecting DNS tunneling through anomaly detection
- Blocking malicious domains in real-time
- Identifying insider threat indicators through behavioural AI
- Automating HR and legal notifications on policy breaches
- Handling false positives caused by AI models
- Creating rollback procedures for automated actions
Module 8: Explainability, Auditability, and Governance of AI Systems - Why explainable AI (XAI) matters in incident response
- Generating human-readable justifications for AI actions
- Creating audit trails for AI-driven decisions
- Compliance requirements for automated security actions
- Documentation standards for AI model usage
- Establishing approval layers for high-risk automations
- Human-in-the-loop vs full automation: When to apply each
- Logging model input, output, and decision pathways
- Version control for AI models and rule sets
- Conducting third-party AI audits
- Mapping AI actions to NIST or ISO 27001 controls
- Board-level reporting on AI performance and risk
- Creating an AI oversight committee
- Handling model bias in security contexts
- Ensuring vendor transparency in third-party AI tools
- Maintaining chain of custody in AI-influenced investigations
Module 9: Performance Monitoring and Incident Metrics - Key performance indicators for AI-driven IR
- Measuring mean time to detect (MTTD) reduction
- Tracking mean time to respond (MTTR) improvements
- Calculating false positive and false negative rates
- Tracking automation success and failure rates
- Measuring analyst productivity gains
- Creating dashboards for AI system health
- Monitoring model confidence over time
- Alert volume trends before and after AI deployment
- Analyst workload reduction metrics
- Cost-benefit analysis of AI implementation
- ROI calculation for automation initiatives
- Incident closure rate improvements
- Tracking repeat incidents and recurrence patterns
- Using metrics to justify AI investment to leadership
- Continuous feedback loop between metrics and improvement
Module 10: Future-Proofing Your AI-Driven SOC - Building a culture of AI adoption in security teams
- Upskilling analysts for AI collaboration
- Creating internal AI champions and advocates
- Developing an AI integration roadmap
- Prioritising automation use cases by impact and feasibility
- Budgeting for AI tools and infrastructure
- Evaluating third-party AI vendors and solutions
- Integrating AI into incident response plans (IRPs)
- Conducting AI-informed tabletop exercises
- Testing AI components during red team engagements
- Handling AI failure scenarios and fallback procedures
- Incident escalation paths when AI is compromised
- Preparing for AI-targeted attacks
- Ethical considerations in autonomous response
- Long-term maintenance of AI systems
- Staying updated on emerging AI threats and defences
Module 11: Capstone Project: Build Your AI-Driven Incident Workflow - Selecting a high-impact use case from your environment
- Defining success criteria and KPIs
- Data sourcing and preparation for your model
- Designing detection logic and confidence thresholds
- Mapping the full incident lifecycle: Detect, Triage, Respond, Report
- Building automated enrichment steps
- Designing automated containment actions
- Creating human review checkpoints for critical actions
- Generating audit-ready documentation for each step
- Testing workflow with historical incident data
- Measuring accuracy and efficiency gains
- Refining thresholds and logic based on results
- Preparing a presentation for internal stakeholders
- Documenting lessons learned and next steps
- Submitting your project for feedback
- Receiving expert evaluation and improvement guidance
Module 12: Certification, Career Advancement, and Next Steps - Requirements for Certificate of Completion
- Final assessment structure and expectations
- Submitting your capstone project for review
- Receiving your official certificate from The Art of Service
- Adding your credential to LinkedIn and professional profiles
- Highlighting AI-driven IR experience in job applications
- Transitioning from analyst to automation specialist
- Becoming an internal AI advisor
- Preparing for interviews with real-world implementation stories
- Networking within the AI security community
- Accessing exclusive alumni resources
- Advanced learning paths in AI and cybersecurity
- Contributing to open-source AI security projects
- Mentorship opportunities for new learners
- Staying updated through The Art of Service newsletters
- Expanding into AI roles: Threat Data Scientist, SOC Architect, CISO Advisor
- Simulated phishing campaign detection and response
- Automated identification of malicious Office macros
- Endpoint ransomware behaviour detection
- Automated isolation of infected devices
- Cloud credential compromise detection
- Automated revocation and rotation of compromised keys
- Detecting lateral movement through SSH and RDP
- Automating user session termination on high-risk sign-ins
- Identifying data staging and compression before exfil
- Automating network segmentation upon detection
- Detecting DNS tunneling through anomaly detection
- Blocking malicious domains in real-time
- Identifying insider threat indicators through behavioural AI
- Automating HR and legal notifications on policy breaches
- Handling false positives caused by AI models
- Creating rollback procedures for automated actions
Module 8: Explainability, Auditability, and Governance of AI Systems - Why explainable AI (XAI) matters in incident response
- Generating human-readable justifications for AI actions
- Creating audit trails for AI-driven decisions
- Compliance requirements for automated security actions
- Documentation standards for AI model usage
- Establishing approval layers for high-risk automations
- Human-in-the-loop vs full automation: When to apply each
- Logging model input, output, and decision pathways
- Version control for AI models and rule sets
- Conducting third-party AI audits
- Mapping AI actions to NIST or ISO 27001 controls
- Board-level reporting on AI performance and risk
- Creating an AI oversight committee
- Handling model bias in security contexts
- Ensuring vendor transparency in third-party AI tools
- Maintaining chain of custody in AI-influenced investigations
Module 9: Performance Monitoring and Incident Metrics - Key performance indicators for AI-driven IR
- Measuring mean time to detect (MTTD) reduction
- Tracking mean time to respond (MTTR) improvements
- Calculating false positive and false negative rates
- Tracking automation success and failure rates
- Measuring analyst productivity gains
- Creating dashboards for AI system health
- Monitoring model confidence over time
- Alert volume trends before and after AI deployment
- Analyst workload reduction metrics
- Cost-benefit analysis of AI implementation
- ROI calculation for automation initiatives
- Incident closure rate improvements
- Tracking repeat incidents and recurrence patterns
- Using metrics to justify AI investment to leadership
- Continuous feedback loop between metrics and improvement
Module 10: Future-Proofing Your AI-Driven SOC - Building a culture of AI adoption in security teams
- Upskilling analysts for AI collaboration
- Creating internal AI champions and advocates
- Developing an AI integration roadmap
- Prioritising automation use cases by impact and feasibility
- Budgeting for AI tools and infrastructure
- Evaluating third-party AI vendors and solutions
- Integrating AI into incident response plans (IRPs)
- Conducting AI-informed tabletop exercises
- Testing AI components during red team engagements
- Handling AI failure scenarios and fallback procedures
- Incident escalation paths when AI is compromised
- Preparing for AI-targeted attacks
- Ethical considerations in autonomous response
- Long-term maintenance of AI systems
- Staying updated on emerging AI threats and defences
Module 11: Capstone Project: Build Your AI-Driven Incident Workflow - Selecting a high-impact use case from your environment
- Defining success criteria and KPIs
- Data sourcing and preparation for your model
- Designing detection logic and confidence thresholds
- Mapping the full incident lifecycle: Detect, Triage, Respond, Report
- Building automated enrichment steps
- Designing automated containment actions
- Creating human review checkpoints for critical actions
- Generating audit-ready documentation for each step
- Testing workflow with historical incident data
- Measuring accuracy and efficiency gains
- Refining thresholds and logic based on results
- Preparing a presentation for internal stakeholders
- Documenting lessons learned and next steps
- Submitting your project for feedback
- Receiving expert evaluation and improvement guidance
Module 12: Certification, Career Advancement, and Next Steps - Requirements for Certificate of Completion
- Final assessment structure and expectations
- Submitting your capstone project for review
- Receiving your official certificate from The Art of Service
- Adding your credential to LinkedIn and professional profiles
- Highlighting AI-driven IR experience in job applications
- Transitioning from analyst to automation specialist
- Becoming an internal AI advisor
- Preparing for interviews with real-world implementation stories
- Networking within the AI security community
- Accessing exclusive alumni resources
- Advanced learning paths in AI and cybersecurity
- Contributing to open-source AI security projects
- Mentorship opportunities for new learners
- Staying updated through The Art of Service newsletters
- Expanding into AI roles: Threat Data Scientist, SOC Architect, CISO Advisor
- Key performance indicators for AI-driven IR
- Measuring mean time to detect (MTTD) reduction
- Tracking mean time to respond (MTTR) improvements
- Calculating false positive and false negative rates
- Tracking automation success and failure rates
- Measuring analyst productivity gains
- Creating dashboards for AI system health
- Monitoring model confidence over time
- Alert volume trends before and after AI deployment
- Analyst workload reduction metrics
- Cost-benefit analysis of AI implementation
- ROI calculation for automation initiatives
- Incident closure rate improvements
- Tracking repeat incidents and recurrence patterns
- Using metrics to justify AI investment to leadership
- Continuous feedback loop between metrics and improvement
Module 10: Future-Proofing Your AI-Driven SOC - Building a culture of AI adoption in security teams
- Upskilling analysts for AI collaboration
- Creating internal AI champions and advocates
- Developing an AI integration roadmap
- Prioritising automation use cases by impact and feasibility
- Budgeting for AI tools and infrastructure
- Evaluating third-party AI vendors and solutions
- Integrating AI into incident response plans (IRPs)
- Conducting AI-informed tabletop exercises
- Testing AI components during red team engagements
- Handling AI failure scenarios and fallback procedures
- Incident escalation paths when AI is compromised
- Preparing for AI-targeted attacks
- Ethical considerations in autonomous response
- Long-term maintenance of AI systems
- Staying updated on emerging AI threats and defences
Module 11: Capstone Project: Build Your AI-Driven Incident Workflow - Selecting a high-impact use case from your environment
- Defining success criteria and KPIs
- Data sourcing and preparation for your model
- Designing detection logic and confidence thresholds
- Mapping the full incident lifecycle: Detect, Triage, Respond, Report
- Building automated enrichment steps
- Designing automated containment actions
- Creating human review checkpoints for critical actions
- Generating audit-ready documentation for each step
- Testing workflow with historical incident data
- Measuring accuracy and efficiency gains
- Refining thresholds and logic based on results
- Preparing a presentation for internal stakeholders
- Documenting lessons learned and next steps
- Submitting your project for feedback
- Receiving expert evaluation and improvement guidance
Module 12: Certification, Career Advancement, and Next Steps - Requirements for Certificate of Completion
- Final assessment structure and expectations
- Submitting your capstone project for review
- Receiving your official certificate from The Art of Service
- Adding your credential to LinkedIn and professional profiles
- Highlighting AI-driven IR experience in job applications
- Transitioning from analyst to automation specialist
- Becoming an internal AI advisor
- Preparing for interviews with real-world implementation stories
- Networking within the AI security community
- Accessing exclusive alumni resources
- Advanced learning paths in AI and cybersecurity
- Contributing to open-source AI security projects
- Mentorship opportunities for new learners
- Staying updated through The Art of Service newsletters
- Expanding into AI roles: Threat Data Scientist, SOC Architect, CISO Advisor
- Selecting a high-impact use case from your environment
- Defining success criteria and KPIs
- Data sourcing and preparation for your model
- Designing detection logic and confidence thresholds
- Mapping the full incident lifecycle: Detect, Triage, Respond, Report
- Building automated enrichment steps
- Designing automated containment actions
- Creating human review checkpoints for critical actions
- Generating audit-ready documentation for each step
- Testing workflow with historical incident data
- Measuring accuracy and efficiency gains
- Refining thresholds and logic based on results
- Preparing a presentation for internal stakeholders
- Documenting lessons learned and next steps
- Submitting your project for feedback
- Receiving expert evaluation and improvement guidance