AI-Driven Incident Response and Threat Hunting
You're under pressure. Every alert could be noise, or the first sign of a breach already in motion. Your team is stretched thin, chasing false positives while real threats slip through. You know legacy approaches aren't working-but you don’t have time to experiment, and you can’t afford to get it wrong. Manual triage. Slow containment. Reactive playbooks. They’re costing your organisation visibility, time, and trust. Meanwhile, adversaries evolve faster than your tooling. The window between detection and damage is closing. You need to act faster, with greater precision-and you need results, not theory. The AI-Driven Incident Response and Threat Hunting course is your blueprint for transforming uncertainty into decisive action. This is how security professionals go from overwhelmed to in control-turning AI from a buzzword into a board-ready capability that reduces mean time to respond by up to 73%. One graduate, a senior SOC analyst at a financial institution, applied the frameworks from this course to redesign their triage pipeline. Within six weeks, they reduced alert fatigue by 68% and uncovered two previously undetected lateral movement patterns-now part of their organisation’s standard detection strategy. This isn’t about chasing technology. It’s about mastering a structured, repeatable methodology that integrates AI into your incident response lifecycle and threat hunting operations-starting on day one. No more guesswork. No more delays. Here’s how this course is structured to help you get there.Course Format & Delivery Details Self-Paced, On-Demand, With Immediate Online Access
This course is designed for working professionals. Once enrolled, you gain self-paced, on-demand access to all content. There are no fixed start dates, no live sessions, and no time commitments. Begin anytime, progress at your own speed, and revisit material as needed. Most learners complete the core curriculum in 28–35 hours, with many applying key techniques to real-world investigations within their first week. The structure ensures rapid skill transfer, with immediate applicability to your current role. Lifetime Access & Future Updates Included
Your enrollment includes lifetime access to the full course. This is not a time-limited license. As AI threat models and detection techniques evolve, the content is updated at no additional cost. You’ll always have access to the latest industry-aligned strategies and tools. Materials are accessible 24/7 from any global location. Whether you’re on desktop, tablet, or mobile, the platform is fully responsive, ensuring you can learn during downtime, between incidents, or from remote sites-no compromise on usability or continuity. Instructor-Led Guidance, Practitioner-Tested Frameworks
You’re not alone. This course includes direct access to expert-led guidance. While the content is self-contained and structured for independent progress, instructor support is available to clarify complex topics, validate implementation strategies, and help troubleshoot real-world challenges you face in your environment. The methodology is grounded in thousands of hours of incident data, red team engagements, and production AI integration across enterprise networks. Every exercise reflects actual conditions-not hypothetical labs. Certificate of Completion Issued by The Art of Service
Upon finishing the course, you’ll receive a Certificate of Completion issued by The Art of Service. This credential is recognised by cybersecurity teams and hiring managers globally, demonstrating mastery of AI-enhanced detection, automated response orchestration, and intelligent threat hunting. The certificate includes a unique verification code and aligns with industry frameworks such as NIST, MITRE ATT&CK, and ISO/IEC 27035. It enhances your resume, supports internal promotions, and strengthens your position in competitive job markets. Transparent Pricing, No Hidden Fees
The course fee includes full access to all materials, the certification, and future updates. There are no hidden fees, no subscription traps, and no add-ons. What you see is exactly what you get. We accept all major payment methods, including Visa, Mastercard, and PayPal. Transactions are secure, encrypted, and processed instantly. 90-Day Satisfied-or-Refunded Guarantee
You’re fully protected by our 90-day money-back promise. If at any point during the first 90 days you feel the course doesn’t meet your expectations or deliver tangible value, simply request a refund. No forms, no hoops, no risk. This is confidence-built-in. You only keep it if it works for you. Seamless Enrollment and Access Workflow
After enrollment, you’ll receive an email confirmation. Access details to the course platform will be sent separately once your learning environment is fully provisioned. This ensures system stability and readiness, with no compromise on security or content integrity. But Will This Work for Me?
Yes-especially if you’re in a role where speed, accuracy, and decision authority matter. Whether you're a SOC analyst, incident responder, threat hunter, or security architect, the frameworks are role-adaptable and use-case specific. This works even if: You’ve never used AI in security operations, your team lacks dedicated data science resources, or you’re working with legacy EDR and SIEM infrastructure. The course includes integration blueprints for environments of all sizes and maturity levels. Graduates include Tier 1 analysts who automated alert prioritisation, CISOs who embedded AI into IR playbooks, and consultants who now offer AI-driven hunting as a premium service. Your background, tools, or organisation size don’t exclude you-they inform how you apply what you learn. Risk is reversed. Value is guaranteed. Your next-level capability starts here.
Module 1: Foundations of AI in Security Operations - Understanding the evolution of AI in cybersecurity
- Differentiating machine learning, deep learning, and rule-based automation
- Key terminology: supervised vs unsupervised learning, anomaly detection, classification
- How AI augments human analysts in incident response
- The role of data quality in AI performance
- Common misconceptions about AI in SOC environments
- Identifying high-impact use cases for AI in IR and hunting
- Evaluating AI readiness in your current security stack
- Privacy, ethics, and bias considerations in AI deployment
- Regulatory alignment: GDPR, CCPA, HIPAA in AI-augmented workflows
- Building cross-functional support for AI adoption
- Setting measurable success criteria for AI integration
- Establishing baselines for normal system behaviour
- Mapping AI capabilities to MITRE ATT&CK framework phases
- Overview of data sources suitable for AI analysis
Module 2: Data Engineering for Threat Detection - Principles of security data collection and normalisation
- Log source prioritisation: endpoints, network, cloud, identity
- Extracting actionable telemetry from EDR, SIEM, firewall, and proxy logs
- Data enrichment techniques for context-aware analysis
- Time-series data structuring for AI input
- Handling missing or corrupted data in security datasets
- Feature engineering: transforming raw logs into model-ready inputs
- Creating labels for supervised learning scenarios
- Strategies for unsupervised anomaly detection in unlabeled data
- Scaling data pipelines for enterprise environments
- Data retention policies aligned with AI training requirements
- Secure data handling and access controls for training datasets
- Using open-source tools for data preprocessing
- Validating data integrity across ingestion stages
- Automating data quality checks and alerts
Module 3: AI Models for Incident Triage and Prioritisation - Designing AI models to reduce alert fatigue
- Scoring incident severity using probabilistic classification
- Building custom risk scoring engines for security events
- Ensemble methods for combining multiple AI signals
- Threshold tuning to balance precision and recall
- Reducing false positives through contextual correlation
- Integrating threat intelligence feeds into model decision logic
- Dynamic reweighting of indicators based on evolving context
- Automated ticket summarisation using natural language generation
- Clustering similar incidents to identify campaign patterns
- Using temporal analysis to detect sudden behavioural shifts
- Real-time inference constraints and performance optimisation
- Model interpretability: explaining AI decisions to stakeholders
- Techniques for debugging model misclassifications
- Feedback loops to improve model accuracy over time
Module 4: Automated Response Orchestration - Designing AI-driven SOAR playbooks
- Automating containment actions based on confidence thresholds
- Isolating hosts dynamically using AI-generated triggers
- Automated user deactivation during credential compromise scenarios
- Quarantining malicious files across endpoints and email gateways
- Blocking IPs at firewall level based on AI-detected patterns
- Dynamic DNS sinkholing for C2 traffic disruption
- Orchestrating cross-platform responses using API integrations
- Safeguards to prevent over-automation and operational disruption
- Human-in-the-loop approvals for high-impact actions
- Rollback mechanisms for automated decisions
- Logging and auditing all automated response activities
- Benchmarking playbook performance over time
- Scaling orchestration across distributed environments
- Version control for playbook updates and revisions
Module 5: AI-Powered Threat Hunting Methodologies - Shifting from reactive to proactive detection using AI
- Defining hunter-driven AI queries and hypotheses
- Using unsupervised learning to surface unknown threats
- Clustering rare process executions for lateral movement detection
- Identifying covert data exfiltration through statistical deviation
- Detecting stealthy persistence mechanisms via behavioural profiling
- Time-window analysis for detecting slow-burn attacks
- Leveraging graph analytics to map attacker journeys
- Hypothesis validation techniques using AI-generated leads
- Documenting and prioritising hunting outcomes
- Integrating hunting findings into detection rules
- Building a backlog of AI-augmented hunting initiatives
- Collaborating across teams using shared hunting insights
- Measuring the ROI of threat hunting programmes
- Creating repeatable hunting workflows powered by AI
Module 6: Behavioural Analytics and User Entity Behaviour Analysis (UEBA) - Foundations of UEBA in AI-driven detection
- Establishing baselines for normal user, device, and service account activity
- Detecting compromised accounts through login pattern anomalies
- Profiling typical data access behaviours by role and department
- Identifying privilege escalation through behavioural deviations
- Monitoring lateral movement via access timing and sequence
- Detecting insider threats using outlier analysis
- Modelling peer group comparisons for anomaly detection
- Longitudinal analysis of behavioural drift over time
- Correlating UEBA alerts with endpoint telemetry
- Reducing noise by contextualising behavioural alerts
- Automating investigation steps for high-risk UEBA detections
- Handling shared accounts and service identities in UEBA
- Adjusting sensitivity based on environment risk level
- Integrating identity providers for richer UEBA context
Module 7: Deep Learning for Advanced Threat Detection - Introduction to neural networks in security contexts
- Using recurrent neural networks (RNNs) for sequence modelling
- Detecting attack chains using LSTM-based models
- Analysing process tree structures with graph neural networks
- Image-based analysis of memory dumps and network traffic heatmaps
- NLP techniques for analysing command-line arguments and scripts
- Detecting obfuscated PowerShell and batch scripts through syntax patterns
- Malware classification using embedded feature extraction
- Real-time inference challenges with deep learning models
- Model compression techniques for edge deployment
- Transfer learning for limited-data scenarios
- Using pre-trained models for rapid deployment
- Evaluating model confidence in deep learning outputs
- Addressing model drift in production environments
- Building confidence in deep learning decisions for analysts
Module 8: Adversarial AI and Defence Evasion Countermeasures - Understanding adversarial machine learning techniques
- Detecting evasion attempts such as input manipulation and obfuscation
- Defending models against data poisoning attacks
- Model hardening strategies for production AI systems
- Detecting mimicry attacks where malware emulates benign behaviour
- Using ensemble diversity to counter adversarial inputs
- Incorporating robustness checks into AI validation pipelines
- Monitoring for model inversion and membership inference attempts
- Behavioural hardening: detecting evasion through secondary signals
- Red teaming AI systems to identify weaknesses
- Building deception layers to trap adversarial actors
- Using honeypots with AI-powered anomaly detection
- Automated detection of AI-targeting tools in use
- Updating models in response to new evasion tactics
- Creating feedback mechanisms for field-observed evasion
Module 9: Cloud-Native AI Detection Strategies - Extending AI detection into AWS, Azure, and GCP environments
- Analysing CloudTrail, Azure Activity Logs, and GCP Audit Logs
- Detecting suspicious IAM changes using AI pattern recognition
- Identifying unauthorised resource provisioning in cloud accounts
- Monitoring for anomalous data egress from cloud storage
- Analysing VPC flow logs for command-and-control traffic
- Detecting privilege escalation in serverless environments
- Using AI to detect misconfigurations with security impact
- Correlating cloud logs with on-premises telemetry
- Building cloud-specific threat hunting hypotheses
- Automating remediation of risky cloud resource settings
- Scaling AI models across multi-cloud architectures
- Enforcing policy-as-code with AI feedback loops
- Analysing container and Kubernetes audit logs
- Securing CI/CD pipelines using AI-driven anomaly detection
Module 10: Endpoint AI for Real-Time Detection - Deploying lightweight AI models on endpoints
- Real-time process monitoring using on-device inference
- Detecting suspicious child processes and spawning chains
- Analysing API call sequences for malicious intent
- Identifying living-off-the-land binaries (LOLBins) through usage patterns
- Monitoring fileless execution techniques in memory
- Detecting credential dumping and LSASS access anomalies
- Profiling PowerShell and WMI activity for exploitation attempts
- Using behavioural heuristics to detect macro-based attacks
- Analysing browser extension installations and network callbacks
- Detecting registry persistence through deviation analysis
- Automating response based on local AI scoring
- Balancing performance impact with detection coverage
- Updating endpoint models without full re-deployment
- Integrating with EDR/XDR telemetry for central visibility
Module 11: Network Traffic Intelligence Using AI - Analysing NetFlow, PCAP, and Zeek/Bro logs with AI
- Detecting encrypted C2 traffic through behavioural proxies
- Identifying DNS tunneling using frequency and payload analysis
- Using statistical models to detect data exfiltration
- Profiling normal network conversation patterns
- Detecting port scanning and network enumeration via sequence learning
- Mapping attacker lateral movement through network paths
- Clustering suspicious destination IPs and domains
- Using TLS fingerprinting to identify malicious clients
- Analysing HTTP headers and user-agent anomalies
- Detecting beaconing behaviour through timing models
- Reducing network noise with intelligent flow filtering
- Correlating network AI alerts with endpoint events
- Automating firewall rule updates based on AI findings
- Scaling network AI across distributed分支机构
Module 12: AI Integration with SIEM and SOAR Platforms - Architecture patterns for embedding AI into existing tools
- Extending Splunk, Sentinel, and QRadar with custom AI models
- Parsing and enriching SIEM alerts with AI-generated context
- Using AI to group related alerts into coherent incidents
- Automating alert tagging and categorisation
- Routing high-priority incidents to appropriate analysts
- Generating preliminary investigation summaries
- Integrating AI predictions into SOAR decision trees
- Using confidence scores to gate automated actions
- Building dynamic dashboards with AI-derived metrics
- Scheduling AI model inference as part of rules
- Versioning and testing AI logic within detection pipelines
- Monitoring AI performance within the SIEM environment
- Establishing feedback loops from analysts to AI systems
- Documenting integration points for audit and compliance
Module 13: Measuring and Optimising AI Performance - Defining KPIs for AI-augmented security operations
- Tracking mean time to detect (MTTD) and mean time to respond (MTTR)
- Calculating reduction in alert volume and false positives
- Measuring analyst time savings and case throughput
- Analysing detection rate improvements for key threat types
- Using confusion matrices to evaluate model accuracy
- Calculating precision, recall, F1-score for security models
- Monitoring model drift and degradation over time
- Automating retraining triggers based on performance thresholds
- Conducting A/B testing of AI detection rules
- Assessing business impact of AI-integrated IR workflows
- Reporting AI programme outcomes to executive stakeholders
- Using benchmarks to compare performance across teams
- Identifying bottlenecks in AI model deployment
- Optimising inference speed and resource usage
Module 14: Building a Sustainable AI-Driven Security Programme - Creating a roadmap for AI adoption in your organisation
- Establishing cross-functional AI governance teams
- Developing policies for AI model development and deployment
- Defining ownership and accountability for AI systems
- Setting up model versioning and change control
- Documenting model assumptions, limitations, and constraints
- Conducting periodic AI system audits and reviews
- Training analysts to interpret and act on AI insights
- Creating knowledge transfer protocols for new team members
- Scaling AI capabilities across multiple use cases
- Integrating AI outcomes into incident post-mortems
- Encouraging a culture of data-driven decision making
- Measuring maturity progression across AI capabilities
- Aligning AI initiatives with overall security strategy
- Preparing for third-party audits of AI systems
Module 15: Real-World AI Incident Response Projects - Project 1: Automating phishing incident triage with AI classification
- Project 2: Building a model to detect abnormal database access
- Project 3: Developing an AI-powered lateral movement detector
- Project 4: Creating a UEBA system for high-privilege accounts
- Project 5: Implementing automated containment for ransomware alerts
- Project 6: Designing a deep learning model for process tree analysis
- Project 7: Developing a cloud anomaly detector for IAM changes
- Project 8: Building a network beaconing detection system
- Project 9: Creating an AI-enhanced threat hunting query library
- Project 10: Integrating AI insights into existing SOAR workflows
- Analysing real incident datasets to train custom models
- Validating model outputs against known breach patterns
- Drafting incident response playbooks based on AI findings
- Presenting AI-driven insights to virtual executive panels
- Graduating with a portfolio of applied AI security projects
Module 16: Certification, Career Advancement, and Next Steps - Preparing for the final assessment to earn your Certificate of Completion
- Reviewing key concepts and decision-making frameworks
- Practicing AI evaluation scenarios under time constraints
- Documenting your learning journey and project portfolio
- How to showcase your certification on LinkedIn and resumes
- Leveraging the credential in salary negotiations and promotions
- Joining the global alumni network of AI security practitioners
- Accessing advanced content and specialisation tracks
- Staying current with monthly AI threat intelligence updates
- Participating in exclusive roundtables with industry leaders
- Receiving job board access for AI-focused security roles
- Opportunities to contribute to open-source AI security tools
- Pathways to advanced certifications and specialisations
- Building a personal brand as an AI-aware defender
- Your roadmap: from course completion to career transformation
- Understanding the evolution of AI in cybersecurity
- Differentiating machine learning, deep learning, and rule-based automation
- Key terminology: supervised vs unsupervised learning, anomaly detection, classification
- How AI augments human analysts in incident response
- The role of data quality in AI performance
- Common misconceptions about AI in SOC environments
- Identifying high-impact use cases for AI in IR and hunting
- Evaluating AI readiness in your current security stack
- Privacy, ethics, and bias considerations in AI deployment
- Regulatory alignment: GDPR, CCPA, HIPAA in AI-augmented workflows
- Building cross-functional support for AI adoption
- Setting measurable success criteria for AI integration
- Establishing baselines for normal system behaviour
- Mapping AI capabilities to MITRE ATT&CK framework phases
- Overview of data sources suitable for AI analysis
Module 2: Data Engineering for Threat Detection - Principles of security data collection and normalisation
- Log source prioritisation: endpoints, network, cloud, identity
- Extracting actionable telemetry from EDR, SIEM, firewall, and proxy logs
- Data enrichment techniques for context-aware analysis
- Time-series data structuring for AI input
- Handling missing or corrupted data in security datasets
- Feature engineering: transforming raw logs into model-ready inputs
- Creating labels for supervised learning scenarios
- Strategies for unsupervised anomaly detection in unlabeled data
- Scaling data pipelines for enterprise environments
- Data retention policies aligned with AI training requirements
- Secure data handling and access controls for training datasets
- Using open-source tools for data preprocessing
- Validating data integrity across ingestion stages
- Automating data quality checks and alerts
Module 3: AI Models for Incident Triage and Prioritisation - Designing AI models to reduce alert fatigue
- Scoring incident severity using probabilistic classification
- Building custom risk scoring engines for security events
- Ensemble methods for combining multiple AI signals
- Threshold tuning to balance precision and recall
- Reducing false positives through contextual correlation
- Integrating threat intelligence feeds into model decision logic
- Dynamic reweighting of indicators based on evolving context
- Automated ticket summarisation using natural language generation
- Clustering similar incidents to identify campaign patterns
- Using temporal analysis to detect sudden behavioural shifts
- Real-time inference constraints and performance optimisation
- Model interpretability: explaining AI decisions to stakeholders
- Techniques for debugging model misclassifications
- Feedback loops to improve model accuracy over time
Module 4: Automated Response Orchestration - Designing AI-driven SOAR playbooks
- Automating containment actions based on confidence thresholds
- Isolating hosts dynamically using AI-generated triggers
- Automated user deactivation during credential compromise scenarios
- Quarantining malicious files across endpoints and email gateways
- Blocking IPs at firewall level based on AI-detected patterns
- Dynamic DNS sinkholing for C2 traffic disruption
- Orchestrating cross-platform responses using API integrations
- Safeguards to prevent over-automation and operational disruption
- Human-in-the-loop approvals for high-impact actions
- Rollback mechanisms for automated decisions
- Logging and auditing all automated response activities
- Benchmarking playbook performance over time
- Scaling orchestration across distributed environments
- Version control for playbook updates and revisions
Module 5: AI-Powered Threat Hunting Methodologies - Shifting from reactive to proactive detection using AI
- Defining hunter-driven AI queries and hypotheses
- Using unsupervised learning to surface unknown threats
- Clustering rare process executions for lateral movement detection
- Identifying covert data exfiltration through statistical deviation
- Detecting stealthy persistence mechanisms via behavioural profiling
- Time-window analysis for detecting slow-burn attacks
- Leveraging graph analytics to map attacker journeys
- Hypothesis validation techniques using AI-generated leads
- Documenting and prioritising hunting outcomes
- Integrating hunting findings into detection rules
- Building a backlog of AI-augmented hunting initiatives
- Collaborating across teams using shared hunting insights
- Measuring the ROI of threat hunting programmes
- Creating repeatable hunting workflows powered by AI
Module 6: Behavioural Analytics and User Entity Behaviour Analysis (UEBA) - Foundations of UEBA in AI-driven detection
- Establishing baselines for normal user, device, and service account activity
- Detecting compromised accounts through login pattern anomalies
- Profiling typical data access behaviours by role and department
- Identifying privilege escalation through behavioural deviations
- Monitoring lateral movement via access timing and sequence
- Detecting insider threats using outlier analysis
- Modelling peer group comparisons for anomaly detection
- Longitudinal analysis of behavioural drift over time
- Correlating UEBA alerts with endpoint telemetry
- Reducing noise by contextualising behavioural alerts
- Automating investigation steps for high-risk UEBA detections
- Handling shared accounts and service identities in UEBA
- Adjusting sensitivity based on environment risk level
- Integrating identity providers for richer UEBA context
Module 7: Deep Learning for Advanced Threat Detection - Introduction to neural networks in security contexts
- Using recurrent neural networks (RNNs) for sequence modelling
- Detecting attack chains using LSTM-based models
- Analysing process tree structures with graph neural networks
- Image-based analysis of memory dumps and network traffic heatmaps
- NLP techniques for analysing command-line arguments and scripts
- Detecting obfuscated PowerShell and batch scripts through syntax patterns
- Malware classification using embedded feature extraction
- Real-time inference challenges with deep learning models
- Model compression techniques for edge deployment
- Transfer learning for limited-data scenarios
- Using pre-trained models for rapid deployment
- Evaluating model confidence in deep learning outputs
- Addressing model drift in production environments
- Building confidence in deep learning decisions for analysts
Module 8: Adversarial AI and Defence Evasion Countermeasures - Understanding adversarial machine learning techniques
- Detecting evasion attempts such as input manipulation and obfuscation
- Defending models against data poisoning attacks
- Model hardening strategies for production AI systems
- Detecting mimicry attacks where malware emulates benign behaviour
- Using ensemble diversity to counter adversarial inputs
- Incorporating robustness checks into AI validation pipelines
- Monitoring for model inversion and membership inference attempts
- Behavioural hardening: detecting evasion through secondary signals
- Red teaming AI systems to identify weaknesses
- Building deception layers to trap adversarial actors
- Using honeypots with AI-powered anomaly detection
- Automated detection of AI-targeting tools in use
- Updating models in response to new evasion tactics
- Creating feedback mechanisms for field-observed evasion
Module 9: Cloud-Native AI Detection Strategies - Extending AI detection into AWS, Azure, and GCP environments
- Analysing CloudTrail, Azure Activity Logs, and GCP Audit Logs
- Detecting suspicious IAM changes using AI pattern recognition
- Identifying unauthorised resource provisioning in cloud accounts
- Monitoring for anomalous data egress from cloud storage
- Analysing VPC flow logs for command-and-control traffic
- Detecting privilege escalation in serverless environments
- Using AI to detect misconfigurations with security impact
- Correlating cloud logs with on-premises telemetry
- Building cloud-specific threat hunting hypotheses
- Automating remediation of risky cloud resource settings
- Scaling AI models across multi-cloud architectures
- Enforcing policy-as-code with AI feedback loops
- Analysing container and Kubernetes audit logs
- Securing CI/CD pipelines using AI-driven anomaly detection
Module 10: Endpoint AI for Real-Time Detection - Deploying lightweight AI models on endpoints
- Real-time process monitoring using on-device inference
- Detecting suspicious child processes and spawning chains
- Analysing API call sequences for malicious intent
- Identifying living-off-the-land binaries (LOLBins) through usage patterns
- Monitoring fileless execution techniques in memory
- Detecting credential dumping and LSASS access anomalies
- Profiling PowerShell and WMI activity for exploitation attempts
- Using behavioural heuristics to detect macro-based attacks
- Analysing browser extension installations and network callbacks
- Detecting registry persistence through deviation analysis
- Automating response based on local AI scoring
- Balancing performance impact with detection coverage
- Updating endpoint models without full re-deployment
- Integrating with EDR/XDR telemetry for central visibility
Module 11: Network Traffic Intelligence Using AI - Analysing NetFlow, PCAP, and Zeek/Bro logs with AI
- Detecting encrypted C2 traffic through behavioural proxies
- Identifying DNS tunneling using frequency and payload analysis
- Using statistical models to detect data exfiltration
- Profiling normal network conversation patterns
- Detecting port scanning and network enumeration via sequence learning
- Mapping attacker lateral movement through network paths
- Clustering suspicious destination IPs and domains
- Using TLS fingerprinting to identify malicious clients
- Analysing HTTP headers and user-agent anomalies
- Detecting beaconing behaviour through timing models
- Reducing network noise with intelligent flow filtering
- Correlating network AI alerts with endpoint events
- Automating firewall rule updates based on AI findings
- Scaling network AI across distributed分支机构
Module 12: AI Integration with SIEM and SOAR Platforms - Architecture patterns for embedding AI into existing tools
- Extending Splunk, Sentinel, and QRadar with custom AI models
- Parsing and enriching SIEM alerts with AI-generated context
- Using AI to group related alerts into coherent incidents
- Automating alert tagging and categorisation
- Routing high-priority incidents to appropriate analysts
- Generating preliminary investigation summaries
- Integrating AI predictions into SOAR decision trees
- Using confidence scores to gate automated actions
- Building dynamic dashboards with AI-derived metrics
- Scheduling AI model inference as part of rules
- Versioning and testing AI logic within detection pipelines
- Monitoring AI performance within the SIEM environment
- Establishing feedback loops from analysts to AI systems
- Documenting integration points for audit and compliance
Module 13: Measuring and Optimising AI Performance - Defining KPIs for AI-augmented security operations
- Tracking mean time to detect (MTTD) and mean time to respond (MTTR)
- Calculating reduction in alert volume and false positives
- Measuring analyst time savings and case throughput
- Analysing detection rate improvements for key threat types
- Using confusion matrices to evaluate model accuracy
- Calculating precision, recall, F1-score for security models
- Monitoring model drift and degradation over time
- Automating retraining triggers based on performance thresholds
- Conducting A/B testing of AI detection rules
- Assessing business impact of AI-integrated IR workflows
- Reporting AI programme outcomes to executive stakeholders
- Using benchmarks to compare performance across teams
- Identifying bottlenecks in AI model deployment
- Optimising inference speed and resource usage
Module 14: Building a Sustainable AI-Driven Security Programme - Creating a roadmap for AI adoption in your organisation
- Establishing cross-functional AI governance teams
- Developing policies for AI model development and deployment
- Defining ownership and accountability for AI systems
- Setting up model versioning and change control
- Documenting model assumptions, limitations, and constraints
- Conducting periodic AI system audits and reviews
- Training analysts to interpret and act on AI insights
- Creating knowledge transfer protocols for new team members
- Scaling AI capabilities across multiple use cases
- Integrating AI outcomes into incident post-mortems
- Encouraging a culture of data-driven decision making
- Measuring maturity progression across AI capabilities
- Aligning AI initiatives with overall security strategy
- Preparing for third-party audits of AI systems
Module 15: Real-World AI Incident Response Projects - Project 1: Automating phishing incident triage with AI classification
- Project 2: Building a model to detect abnormal database access
- Project 3: Developing an AI-powered lateral movement detector
- Project 4: Creating a UEBA system for high-privilege accounts
- Project 5: Implementing automated containment for ransomware alerts
- Project 6: Designing a deep learning model for process tree analysis
- Project 7: Developing a cloud anomaly detector for IAM changes
- Project 8: Building a network beaconing detection system
- Project 9: Creating an AI-enhanced threat hunting query library
- Project 10: Integrating AI insights into existing SOAR workflows
- Analysing real incident datasets to train custom models
- Validating model outputs against known breach patterns
- Drafting incident response playbooks based on AI findings
- Presenting AI-driven insights to virtual executive panels
- Graduating with a portfolio of applied AI security projects
Module 16: Certification, Career Advancement, and Next Steps - Preparing for the final assessment to earn your Certificate of Completion
- Reviewing key concepts and decision-making frameworks
- Practicing AI evaluation scenarios under time constraints
- Documenting your learning journey and project portfolio
- How to showcase your certification on LinkedIn and resumes
- Leveraging the credential in salary negotiations and promotions
- Joining the global alumni network of AI security practitioners
- Accessing advanced content and specialisation tracks
- Staying current with monthly AI threat intelligence updates
- Participating in exclusive roundtables with industry leaders
- Receiving job board access for AI-focused security roles
- Opportunities to contribute to open-source AI security tools
- Pathways to advanced certifications and specialisations
- Building a personal brand as an AI-aware defender
- Your roadmap: from course completion to career transformation
- Designing AI models to reduce alert fatigue
- Scoring incident severity using probabilistic classification
- Building custom risk scoring engines for security events
- Ensemble methods for combining multiple AI signals
- Threshold tuning to balance precision and recall
- Reducing false positives through contextual correlation
- Integrating threat intelligence feeds into model decision logic
- Dynamic reweighting of indicators based on evolving context
- Automated ticket summarisation using natural language generation
- Clustering similar incidents to identify campaign patterns
- Using temporal analysis to detect sudden behavioural shifts
- Real-time inference constraints and performance optimisation
- Model interpretability: explaining AI decisions to stakeholders
- Techniques for debugging model misclassifications
- Feedback loops to improve model accuracy over time
Module 4: Automated Response Orchestration - Designing AI-driven SOAR playbooks
- Automating containment actions based on confidence thresholds
- Isolating hosts dynamically using AI-generated triggers
- Automated user deactivation during credential compromise scenarios
- Quarantining malicious files across endpoints and email gateways
- Blocking IPs at firewall level based on AI-detected patterns
- Dynamic DNS sinkholing for C2 traffic disruption
- Orchestrating cross-platform responses using API integrations
- Safeguards to prevent over-automation and operational disruption
- Human-in-the-loop approvals for high-impact actions
- Rollback mechanisms for automated decisions
- Logging and auditing all automated response activities
- Benchmarking playbook performance over time
- Scaling orchestration across distributed environments
- Version control for playbook updates and revisions
Module 5: AI-Powered Threat Hunting Methodologies - Shifting from reactive to proactive detection using AI
- Defining hunter-driven AI queries and hypotheses
- Using unsupervised learning to surface unknown threats
- Clustering rare process executions for lateral movement detection
- Identifying covert data exfiltration through statistical deviation
- Detecting stealthy persistence mechanisms via behavioural profiling
- Time-window analysis for detecting slow-burn attacks
- Leveraging graph analytics to map attacker journeys
- Hypothesis validation techniques using AI-generated leads
- Documenting and prioritising hunting outcomes
- Integrating hunting findings into detection rules
- Building a backlog of AI-augmented hunting initiatives
- Collaborating across teams using shared hunting insights
- Measuring the ROI of threat hunting programmes
- Creating repeatable hunting workflows powered by AI
Module 6: Behavioural Analytics and User Entity Behaviour Analysis (UEBA) - Foundations of UEBA in AI-driven detection
- Establishing baselines for normal user, device, and service account activity
- Detecting compromised accounts through login pattern anomalies
- Profiling typical data access behaviours by role and department
- Identifying privilege escalation through behavioural deviations
- Monitoring lateral movement via access timing and sequence
- Detecting insider threats using outlier analysis
- Modelling peer group comparisons for anomaly detection
- Longitudinal analysis of behavioural drift over time
- Correlating UEBA alerts with endpoint telemetry
- Reducing noise by contextualising behavioural alerts
- Automating investigation steps for high-risk UEBA detections
- Handling shared accounts and service identities in UEBA
- Adjusting sensitivity based on environment risk level
- Integrating identity providers for richer UEBA context
Module 7: Deep Learning for Advanced Threat Detection - Introduction to neural networks in security contexts
- Using recurrent neural networks (RNNs) for sequence modelling
- Detecting attack chains using LSTM-based models
- Analysing process tree structures with graph neural networks
- Image-based analysis of memory dumps and network traffic heatmaps
- NLP techniques for analysing command-line arguments and scripts
- Detecting obfuscated PowerShell and batch scripts through syntax patterns
- Malware classification using embedded feature extraction
- Real-time inference challenges with deep learning models
- Model compression techniques for edge deployment
- Transfer learning for limited-data scenarios
- Using pre-trained models for rapid deployment
- Evaluating model confidence in deep learning outputs
- Addressing model drift in production environments
- Building confidence in deep learning decisions for analysts
Module 8: Adversarial AI and Defence Evasion Countermeasures - Understanding adversarial machine learning techniques
- Detecting evasion attempts such as input manipulation and obfuscation
- Defending models against data poisoning attacks
- Model hardening strategies for production AI systems
- Detecting mimicry attacks where malware emulates benign behaviour
- Using ensemble diversity to counter adversarial inputs
- Incorporating robustness checks into AI validation pipelines
- Monitoring for model inversion and membership inference attempts
- Behavioural hardening: detecting evasion through secondary signals
- Red teaming AI systems to identify weaknesses
- Building deception layers to trap adversarial actors
- Using honeypots with AI-powered anomaly detection
- Automated detection of AI-targeting tools in use
- Updating models in response to new evasion tactics
- Creating feedback mechanisms for field-observed evasion
Module 9: Cloud-Native AI Detection Strategies - Extending AI detection into AWS, Azure, and GCP environments
- Analysing CloudTrail, Azure Activity Logs, and GCP Audit Logs
- Detecting suspicious IAM changes using AI pattern recognition
- Identifying unauthorised resource provisioning in cloud accounts
- Monitoring for anomalous data egress from cloud storage
- Analysing VPC flow logs for command-and-control traffic
- Detecting privilege escalation in serverless environments
- Using AI to detect misconfigurations with security impact
- Correlating cloud logs with on-premises telemetry
- Building cloud-specific threat hunting hypotheses
- Automating remediation of risky cloud resource settings
- Scaling AI models across multi-cloud architectures
- Enforcing policy-as-code with AI feedback loops
- Analysing container and Kubernetes audit logs
- Securing CI/CD pipelines using AI-driven anomaly detection
Module 10: Endpoint AI for Real-Time Detection - Deploying lightweight AI models on endpoints
- Real-time process monitoring using on-device inference
- Detecting suspicious child processes and spawning chains
- Analysing API call sequences for malicious intent
- Identifying living-off-the-land binaries (LOLBins) through usage patterns
- Monitoring fileless execution techniques in memory
- Detecting credential dumping and LSASS access anomalies
- Profiling PowerShell and WMI activity for exploitation attempts
- Using behavioural heuristics to detect macro-based attacks
- Analysing browser extension installations and network callbacks
- Detecting registry persistence through deviation analysis
- Automating response based on local AI scoring
- Balancing performance impact with detection coverage
- Updating endpoint models without full re-deployment
- Integrating with EDR/XDR telemetry for central visibility
Module 11: Network Traffic Intelligence Using AI - Analysing NetFlow, PCAP, and Zeek/Bro logs with AI
- Detecting encrypted C2 traffic through behavioural proxies
- Identifying DNS tunneling using frequency and payload analysis
- Using statistical models to detect data exfiltration
- Profiling normal network conversation patterns
- Detecting port scanning and network enumeration via sequence learning
- Mapping attacker lateral movement through network paths
- Clustering suspicious destination IPs and domains
- Using TLS fingerprinting to identify malicious clients
- Analysing HTTP headers and user-agent anomalies
- Detecting beaconing behaviour through timing models
- Reducing network noise with intelligent flow filtering
- Correlating network AI alerts with endpoint events
- Automating firewall rule updates based on AI findings
- Scaling network AI across distributed分支机构
Module 12: AI Integration with SIEM and SOAR Platforms - Architecture patterns for embedding AI into existing tools
- Extending Splunk, Sentinel, and QRadar with custom AI models
- Parsing and enriching SIEM alerts with AI-generated context
- Using AI to group related alerts into coherent incidents
- Automating alert tagging and categorisation
- Routing high-priority incidents to appropriate analysts
- Generating preliminary investigation summaries
- Integrating AI predictions into SOAR decision trees
- Using confidence scores to gate automated actions
- Building dynamic dashboards with AI-derived metrics
- Scheduling AI model inference as part of rules
- Versioning and testing AI logic within detection pipelines
- Monitoring AI performance within the SIEM environment
- Establishing feedback loops from analysts to AI systems
- Documenting integration points for audit and compliance
Module 13: Measuring and Optimising AI Performance - Defining KPIs for AI-augmented security operations
- Tracking mean time to detect (MTTD) and mean time to respond (MTTR)
- Calculating reduction in alert volume and false positives
- Measuring analyst time savings and case throughput
- Analysing detection rate improvements for key threat types
- Using confusion matrices to evaluate model accuracy
- Calculating precision, recall, F1-score for security models
- Monitoring model drift and degradation over time
- Automating retraining triggers based on performance thresholds
- Conducting A/B testing of AI detection rules
- Assessing business impact of AI-integrated IR workflows
- Reporting AI programme outcomes to executive stakeholders
- Using benchmarks to compare performance across teams
- Identifying bottlenecks in AI model deployment
- Optimising inference speed and resource usage
Module 14: Building a Sustainable AI-Driven Security Programme - Creating a roadmap for AI adoption in your organisation
- Establishing cross-functional AI governance teams
- Developing policies for AI model development and deployment
- Defining ownership and accountability for AI systems
- Setting up model versioning and change control
- Documenting model assumptions, limitations, and constraints
- Conducting periodic AI system audits and reviews
- Training analysts to interpret and act on AI insights
- Creating knowledge transfer protocols for new team members
- Scaling AI capabilities across multiple use cases
- Integrating AI outcomes into incident post-mortems
- Encouraging a culture of data-driven decision making
- Measuring maturity progression across AI capabilities
- Aligning AI initiatives with overall security strategy
- Preparing for third-party audits of AI systems
Module 15: Real-World AI Incident Response Projects - Project 1: Automating phishing incident triage with AI classification
- Project 2: Building a model to detect abnormal database access
- Project 3: Developing an AI-powered lateral movement detector
- Project 4: Creating a UEBA system for high-privilege accounts
- Project 5: Implementing automated containment for ransomware alerts
- Project 6: Designing a deep learning model for process tree analysis
- Project 7: Developing a cloud anomaly detector for IAM changes
- Project 8: Building a network beaconing detection system
- Project 9: Creating an AI-enhanced threat hunting query library
- Project 10: Integrating AI insights into existing SOAR workflows
- Analysing real incident datasets to train custom models
- Validating model outputs against known breach patterns
- Drafting incident response playbooks based on AI findings
- Presenting AI-driven insights to virtual executive panels
- Graduating with a portfolio of applied AI security projects
Module 16: Certification, Career Advancement, and Next Steps - Preparing for the final assessment to earn your Certificate of Completion
- Reviewing key concepts and decision-making frameworks
- Practicing AI evaluation scenarios under time constraints
- Documenting your learning journey and project portfolio
- How to showcase your certification on LinkedIn and resumes
- Leveraging the credential in salary negotiations and promotions
- Joining the global alumni network of AI security practitioners
- Accessing advanced content and specialisation tracks
- Staying current with monthly AI threat intelligence updates
- Participating in exclusive roundtables with industry leaders
- Receiving job board access for AI-focused security roles
- Opportunities to contribute to open-source AI security tools
- Pathways to advanced certifications and specialisations
- Building a personal brand as an AI-aware defender
- Your roadmap: from course completion to career transformation
- Shifting from reactive to proactive detection using AI
- Defining hunter-driven AI queries and hypotheses
- Using unsupervised learning to surface unknown threats
- Clustering rare process executions for lateral movement detection
- Identifying covert data exfiltration through statistical deviation
- Detecting stealthy persistence mechanisms via behavioural profiling
- Time-window analysis for detecting slow-burn attacks
- Leveraging graph analytics to map attacker journeys
- Hypothesis validation techniques using AI-generated leads
- Documenting and prioritising hunting outcomes
- Integrating hunting findings into detection rules
- Building a backlog of AI-augmented hunting initiatives
- Collaborating across teams using shared hunting insights
- Measuring the ROI of threat hunting programmes
- Creating repeatable hunting workflows powered by AI
Module 6: Behavioural Analytics and User Entity Behaviour Analysis (UEBA) - Foundations of UEBA in AI-driven detection
- Establishing baselines for normal user, device, and service account activity
- Detecting compromised accounts through login pattern anomalies
- Profiling typical data access behaviours by role and department
- Identifying privilege escalation through behavioural deviations
- Monitoring lateral movement via access timing and sequence
- Detecting insider threats using outlier analysis
- Modelling peer group comparisons for anomaly detection
- Longitudinal analysis of behavioural drift over time
- Correlating UEBA alerts with endpoint telemetry
- Reducing noise by contextualising behavioural alerts
- Automating investigation steps for high-risk UEBA detections
- Handling shared accounts and service identities in UEBA
- Adjusting sensitivity based on environment risk level
- Integrating identity providers for richer UEBA context
Module 7: Deep Learning for Advanced Threat Detection - Introduction to neural networks in security contexts
- Using recurrent neural networks (RNNs) for sequence modelling
- Detecting attack chains using LSTM-based models
- Analysing process tree structures with graph neural networks
- Image-based analysis of memory dumps and network traffic heatmaps
- NLP techniques for analysing command-line arguments and scripts
- Detecting obfuscated PowerShell and batch scripts through syntax patterns
- Malware classification using embedded feature extraction
- Real-time inference challenges with deep learning models
- Model compression techniques for edge deployment
- Transfer learning for limited-data scenarios
- Using pre-trained models for rapid deployment
- Evaluating model confidence in deep learning outputs
- Addressing model drift in production environments
- Building confidence in deep learning decisions for analysts
Module 8: Adversarial AI and Defence Evasion Countermeasures - Understanding adversarial machine learning techniques
- Detecting evasion attempts such as input manipulation and obfuscation
- Defending models against data poisoning attacks
- Model hardening strategies for production AI systems
- Detecting mimicry attacks where malware emulates benign behaviour
- Using ensemble diversity to counter adversarial inputs
- Incorporating robustness checks into AI validation pipelines
- Monitoring for model inversion and membership inference attempts
- Behavioural hardening: detecting evasion through secondary signals
- Red teaming AI systems to identify weaknesses
- Building deception layers to trap adversarial actors
- Using honeypots with AI-powered anomaly detection
- Automated detection of AI-targeting tools in use
- Updating models in response to new evasion tactics
- Creating feedback mechanisms for field-observed evasion
Module 9: Cloud-Native AI Detection Strategies - Extending AI detection into AWS, Azure, and GCP environments
- Analysing CloudTrail, Azure Activity Logs, and GCP Audit Logs
- Detecting suspicious IAM changes using AI pattern recognition
- Identifying unauthorised resource provisioning in cloud accounts
- Monitoring for anomalous data egress from cloud storage
- Analysing VPC flow logs for command-and-control traffic
- Detecting privilege escalation in serverless environments
- Using AI to detect misconfigurations with security impact
- Correlating cloud logs with on-premises telemetry
- Building cloud-specific threat hunting hypotheses
- Automating remediation of risky cloud resource settings
- Scaling AI models across multi-cloud architectures
- Enforcing policy-as-code with AI feedback loops
- Analysing container and Kubernetes audit logs
- Securing CI/CD pipelines using AI-driven anomaly detection
Module 10: Endpoint AI for Real-Time Detection - Deploying lightweight AI models on endpoints
- Real-time process monitoring using on-device inference
- Detecting suspicious child processes and spawning chains
- Analysing API call sequences for malicious intent
- Identifying living-off-the-land binaries (LOLBins) through usage patterns
- Monitoring fileless execution techniques in memory
- Detecting credential dumping and LSASS access anomalies
- Profiling PowerShell and WMI activity for exploitation attempts
- Using behavioural heuristics to detect macro-based attacks
- Analysing browser extension installations and network callbacks
- Detecting registry persistence through deviation analysis
- Automating response based on local AI scoring
- Balancing performance impact with detection coverage
- Updating endpoint models without full re-deployment
- Integrating with EDR/XDR telemetry for central visibility
Module 11: Network Traffic Intelligence Using AI - Analysing NetFlow, PCAP, and Zeek/Bro logs with AI
- Detecting encrypted C2 traffic through behavioural proxies
- Identifying DNS tunneling using frequency and payload analysis
- Using statistical models to detect data exfiltration
- Profiling normal network conversation patterns
- Detecting port scanning and network enumeration via sequence learning
- Mapping attacker lateral movement through network paths
- Clustering suspicious destination IPs and domains
- Using TLS fingerprinting to identify malicious clients
- Analysing HTTP headers and user-agent anomalies
- Detecting beaconing behaviour through timing models
- Reducing network noise with intelligent flow filtering
- Correlating network AI alerts with endpoint events
- Automating firewall rule updates based on AI findings
- Scaling network AI across distributed分支机构
Module 12: AI Integration with SIEM and SOAR Platforms - Architecture patterns for embedding AI into existing tools
- Extending Splunk, Sentinel, and QRadar with custom AI models
- Parsing and enriching SIEM alerts with AI-generated context
- Using AI to group related alerts into coherent incidents
- Automating alert tagging and categorisation
- Routing high-priority incidents to appropriate analysts
- Generating preliminary investigation summaries
- Integrating AI predictions into SOAR decision trees
- Using confidence scores to gate automated actions
- Building dynamic dashboards with AI-derived metrics
- Scheduling AI model inference as part of rules
- Versioning and testing AI logic within detection pipelines
- Monitoring AI performance within the SIEM environment
- Establishing feedback loops from analysts to AI systems
- Documenting integration points for audit and compliance
Module 13: Measuring and Optimising AI Performance - Defining KPIs for AI-augmented security operations
- Tracking mean time to detect (MTTD) and mean time to respond (MTTR)
- Calculating reduction in alert volume and false positives
- Measuring analyst time savings and case throughput
- Analysing detection rate improvements for key threat types
- Using confusion matrices to evaluate model accuracy
- Calculating precision, recall, F1-score for security models
- Monitoring model drift and degradation over time
- Automating retraining triggers based on performance thresholds
- Conducting A/B testing of AI detection rules
- Assessing business impact of AI-integrated IR workflows
- Reporting AI programme outcomes to executive stakeholders
- Using benchmarks to compare performance across teams
- Identifying bottlenecks in AI model deployment
- Optimising inference speed and resource usage
Module 14: Building a Sustainable AI-Driven Security Programme - Creating a roadmap for AI adoption in your organisation
- Establishing cross-functional AI governance teams
- Developing policies for AI model development and deployment
- Defining ownership and accountability for AI systems
- Setting up model versioning and change control
- Documenting model assumptions, limitations, and constraints
- Conducting periodic AI system audits and reviews
- Training analysts to interpret and act on AI insights
- Creating knowledge transfer protocols for new team members
- Scaling AI capabilities across multiple use cases
- Integrating AI outcomes into incident post-mortems
- Encouraging a culture of data-driven decision making
- Measuring maturity progression across AI capabilities
- Aligning AI initiatives with overall security strategy
- Preparing for third-party audits of AI systems
Module 15: Real-World AI Incident Response Projects - Project 1: Automating phishing incident triage with AI classification
- Project 2: Building a model to detect abnormal database access
- Project 3: Developing an AI-powered lateral movement detector
- Project 4: Creating a UEBA system for high-privilege accounts
- Project 5: Implementing automated containment for ransomware alerts
- Project 6: Designing a deep learning model for process tree analysis
- Project 7: Developing a cloud anomaly detector for IAM changes
- Project 8: Building a network beaconing detection system
- Project 9: Creating an AI-enhanced threat hunting query library
- Project 10: Integrating AI insights into existing SOAR workflows
- Analysing real incident datasets to train custom models
- Validating model outputs against known breach patterns
- Drafting incident response playbooks based on AI findings
- Presenting AI-driven insights to virtual executive panels
- Graduating with a portfolio of applied AI security projects
Module 16: Certification, Career Advancement, and Next Steps - Preparing for the final assessment to earn your Certificate of Completion
- Reviewing key concepts and decision-making frameworks
- Practicing AI evaluation scenarios under time constraints
- Documenting your learning journey and project portfolio
- How to showcase your certification on LinkedIn and resumes
- Leveraging the credential in salary negotiations and promotions
- Joining the global alumni network of AI security practitioners
- Accessing advanced content and specialisation tracks
- Staying current with monthly AI threat intelligence updates
- Participating in exclusive roundtables with industry leaders
- Receiving job board access for AI-focused security roles
- Opportunities to contribute to open-source AI security tools
- Pathways to advanced certifications and specialisations
- Building a personal brand as an AI-aware defender
- Your roadmap: from course completion to career transformation
- Introduction to neural networks in security contexts
- Using recurrent neural networks (RNNs) for sequence modelling
- Detecting attack chains using LSTM-based models
- Analysing process tree structures with graph neural networks
- Image-based analysis of memory dumps and network traffic heatmaps
- NLP techniques for analysing command-line arguments and scripts
- Detecting obfuscated PowerShell and batch scripts through syntax patterns
- Malware classification using embedded feature extraction
- Real-time inference challenges with deep learning models
- Model compression techniques for edge deployment
- Transfer learning for limited-data scenarios
- Using pre-trained models for rapid deployment
- Evaluating model confidence in deep learning outputs
- Addressing model drift in production environments
- Building confidence in deep learning decisions for analysts
Module 8: Adversarial AI and Defence Evasion Countermeasures - Understanding adversarial machine learning techniques
- Detecting evasion attempts such as input manipulation and obfuscation
- Defending models against data poisoning attacks
- Model hardening strategies for production AI systems
- Detecting mimicry attacks where malware emulates benign behaviour
- Using ensemble diversity to counter adversarial inputs
- Incorporating robustness checks into AI validation pipelines
- Monitoring for model inversion and membership inference attempts
- Behavioural hardening: detecting evasion through secondary signals
- Red teaming AI systems to identify weaknesses
- Building deception layers to trap adversarial actors
- Using honeypots with AI-powered anomaly detection
- Automated detection of AI-targeting tools in use
- Updating models in response to new evasion tactics
- Creating feedback mechanisms for field-observed evasion
Module 9: Cloud-Native AI Detection Strategies - Extending AI detection into AWS, Azure, and GCP environments
- Analysing CloudTrail, Azure Activity Logs, and GCP Audit Logs
- Detecting suspicious IAM changes using AI pattern recognition
- Identifying unauthorised resource provisioning in cloud accounts
- Monitoring for anomalous data egress from cloud storage
- Analysing VPC flow logs for command-and-control traffic
- Detecting privilege escalation in serverless environments
- Using AI to detect misconfigurations with security impact
- Correlating cloud logs with on-premises telemetry
- Building cloud-specific threat hunting hypotheses
- Automating remediation of risky cloud resource settings
- Scaling AI models across multi-cloud architectures
- Enforcing policy-as-code with AI feedback loops
- Analysing container and Kubernetes audit logs
- Securing CI/CD pipelines using AI-driven anomaly detection
Module 10: Endpoint AI for Real-Time Detection - Deploying lightweight AI models on endpoints
- Real-time process monitoring using on-device inference
- Detecting suspicious child processes and spawning chains
- Analysing API call sequences for malicious intent
- Identifying living-off-the-land binaries (LOLBins) through usage patterns
- Monitoring fileless execution techniques in memory
- Detecting credential dumping and LSASS access anomalies
- Profiling PowerShell and WMI activity for exploitation attempts
- Using behavioural heuristics to detect macro-based attacks
- Analysing browser extension installations and network callbacks
- Detecting registry persistence through deviation analysis
- Automating response based on local AI scoring
- Balancing performance impact with detection coverage
- Updating endpoint models without full re-deployment
- Integrating with EDR/XDR telemetry for central visibility
Module 11: Network Traffic Intelligence Using AI - Analysing NetFlow, PCAP, and Zeek/Bro logs with AI
- Detecting encrypted C2 traffic through behavioural proxies
- Identifying DNS tunneling using frequency and payload analysis
- Using statistical models to detect data exfiltration
- Profiling normal network conversation patterns
- Detecting port scanning and network enumeration via sequence learning
- Mapping attacker lateral movement through network paths
- Clustering suspicious destination IPs and domains
- Using TLS fingerprinting to identify malicious clients
- Analysing HTTP headers and user-agent anomalies
- Detecting beaconing behaviour through timing models
- Reducing network noise with intelligent flow filtering
- Correlating network AI alerts with endpoint events
- Automating firewall rule updates based on AI findings
- Scaling network AI across distributed分支机构
Module 12: AI Integration with SIEM and SOAR Platforms - Architecture patterns for embedding AI into existing tools
- Extending Splunk, Sentinel, and QRadar with custom AI models
- Parsing and enriching SIEM alerts with AI-generated context
- Using AI to group related alerts into coherent incidents
- Automating alert tagging and categorisation
- Routing high-priority incidents to appropriate analysts
- Generating preliminary investigation summaries
- Integrating AI predictions into SOAR decision trees
- Using confidence scores to gate automated actions
- Building dynamic dashboards with AI-derived metrics
- Scheduling AI model inference as part of rules
- Versioning and testing AI logic within detection pipelines
- Monitoring AI performance within the SIEM environment
- Establishing feedback loops from analysts to AI systems
- Documenting integration points for audit and compliance
Module 13: Measuring and Optimising AI Performance - Defining KPIs for AI-augmented security operations
- Tracking mean time to detect (MTTD) and mean time to respond (MTTR)
- Calculating reduction in alert volume and false positives
- Measuring analyst time savings and case throughput
- Analysing detection rate improvements for key threat types
- Using confusion matrices to evaluate model accuracy
- Calculating precision, recall, F1-score for security models
- Monitoring model drift and degradation over time
- Automating retraining triggers based on performance thresholds
- Conducting A/B testing of AI detection rules
- Assessing business impact of AI-integrated IR workflows
- Reporting AI programme outcomes to executive stakeholders
- Using benchmarks to compare performance across teams
- Identifying bottlenecks in AI model deployment
- Optimising inference speed and resource usage
Module 14: Building a Sustainable AI-Driven Security Programme - Creating a roadmap for AI adoption in your organisation
- Establishing cross-functional AI governance teams
- Developing policies for AI model development and deployment
- Defining ownership and accountability for AI systems
- Setting up model versioning and change control
- Documenting model assumptions, limitations, and constraints
- Conducting periodic AI system audits and reviews
- Training analysts to interpret and act on AI insights
- Creating knowledge transfer protocols for new team members
- Scaling AI capabilities across multiple use cases
- Integrating AI outcomes into incident post-mortems
- Encouraging a culture of data-driven decision making
- Measuring maturity progression across AI capabilities
- Aligning AI initiatives with overall security strategy
- Preparing for third-party audits of AI systems
Module 15: Real-World AI Incident Response Projects - Project 1: Automating phishing incident triage with AI classification
- Project 2: Building a model to detect abnormal database access
- Project 3: Developing an AI-powered lateral movement detector
- Project 4: Creating a UEBA system for high-privilege accounts
- Project 5: Implementing automated containment for ransomware alerts
- Project 6: Designing a deep learning model for process tree analysis
- Project 7: Developing a cloud anomaly detector for IAM changes
- Project 8: Building a network beaconing detection system
- Project 9: Creating an AI-enhanced threat hunting query library
- Project 10: Integrating AI insights into existing SOAR workflows
- Analysing real incident datasets to train custom models
- Validating model outputs against known breach patterns
- Drafting incident response playbooks based on AI findings
- Presenting AI-driven insights to virtual executive panels
- Graduating with a portfolio of applied AI security projects
Module 16: Certification, Career Advancement, and Next Steps - Preparing for the final assessment to earn your Certificate of Completion
- Reviewing key concepts and decision-making frameworks
- Practicing AI evaluation scenarios under time constraints
- Documenting your learning journey and project portfolio
- How to showcase your certification on LinkedIn and resumes
- Leveraging the credential in salary negotiations and promotions
- Joining the global alumni network of AI security practitioners
- Accessing advanced content and specialisation tracks
- Staying current with monthly AI threat intelligence updates
- Participating in exclusive roundtables with industry leaders
- Receiving job board access for AI-focused security roles
- Opportunities to contribute to open-source AI security tools
- Pathways to advanced certifications and specialisations
- Building a personal brand as an AI-aware defender
- Your roadmap: from course completion to career transformation
- Extending AI detection into AWS, Azure, and GCP environments
- Analysing CloudTrail, Azure Activity Logs, and GCP Audit Logs
- Detecting suspicious IAM changes using AI pattern recognition
- Identifying unauthorised resource provisioning in cloud accounts
- Monitoring for anomalous data egress from cloud storage
- Analysing VPC flow logs for command-and-control traffic
- Detecting privilege escalation in serverless environments
- Using AI to detect misconfigurations with security impact
- Correlating cloud logs with on-premises telemetry
- Building cloud-specific threat hunting hypotheses
- Automating remediation of risky cloud resource settings
- Scaling AI models across multi-cloud architectures
- Enforcing policy-as-code with AI feedback loops
- Analysing container and Kubernetes audit logs
- Securing CI/CD pipelines using AI-driven anomaly detection
Module 10: Endpoint AI for Real-Time Detection - Deploying lightweight AI models on endpoints
- Real-time process monitoring using on-device inference
- Detecting suspicious child processes and spawning chains
- Analysing API call sequences for malicious intent
- Identifying living-off-the-land binaries (LOLBins) through usage patterns
- Monitoring fileless execution techniques in memory
- Detecting credential dumping and LSASS access anomalies
- Profiling PowerShell and WMI activity for exploitation attempts
- Using behavioural heuristics to detect macro-based attacks
- Analysing browser extension installations and network callbacks
- Detecting registry persistence through deviation analysis
- Automating response based on local AI scoring
- Balancing performance impact with detection coverage
- Updating endpoint models without full re-deployment
- Integrating with EDR/XDR telemetry for central visibility
Module 11: Network Traffic Intelligence Using AI - Analysing NetFlow, PCAP, and Zeek/Bro logs with AI
- Detecting encrypted C2 traffic through behavioural proxies
- Identifying DNS tunneling using frequency and payload analysis
- Using statistical models to detect data exfiltration
- Profiling normal network conversation patterns
- Detecting port scanning and network enumeration via sequence learning
- Mapping attacker lateral movement through network paths
- Clustering suspicious destination IPs and domains
- Using TLS fingerprinting to identify malicious clients
- Analysing HTTP headers and user-agent anomalies
- Detecting beaconing behaviour through timing models
- Reducing network noise with intelligent flow filtering
- Correlating network AI alerts with endpoint events
- Automating firewall rule updates based on AI findings
- Scaling network AI across distributed分支机构
Module 12: AI Integration with SIEM and SOAR Platforms - Architecture patterns for embedding AI into existing tools
- Extending Splunk, Sentinel, and QRadar with custom AI models
- Parsing and enriching SIEM alerts with AI-generated context
- Using AI to group related alerts into coherent incidents
- Automating alert tagging and categorisation
- Routing high-priority incidents to appropriate analysts
- Generating preliminary investigation summaries
- Integrating AI predictions into SOAR decision trees
- Using confidence scores to gate automated actions
- Building dynamic dashboards with AI-derived metrics
- Scheduling AI model inference as part of rules
- Versioning and testing AI logic within detection pipelines
- Monitoring AI performance within the SIEM environment
- Establishing feedback loops from analysts to AI systems
- Documenting integration points for audit and compliance
Module 13: Measuring and Optimising AI Performance - Defining KPIs for AI-augmented security operations
- Tracking mean time to detect (MTTD) and mean time to respond (MTTR)
- Calculating reduction in alert volume and false positives
- Measuring analyst time savings and case throughput
- Analysing detection rate improvements for key threat types
- Using confusion matrices to evaluate model accuracy
- Calculating precision, recall, F1-score for security models
- Monitoring model drift and degradation over time
- Automating retraining triggers based on performance thresholds
- Conducting A/B testing of AI detection rules
- Assessing business impact of AI-integrated IR workflows
- Reporting AI programme outcomes to executive stakeholders
- Using benchmarks to compare performance across teams
- Identifying bottlenecks in AI model deployment
- Optimising inference speed and resource usage
Module 14: Building a Sustainable AI-Driven Security Programme - Creating a roadmap for AI adoption in your organisation
- Establishing cross-functional AI governance teams
- Developing policies for AI model development and deployment
- Defining ownership and accountability for AI systems
- Setting up model versioning and change control
- Documenting model assumptions, limitations, and constraints
- Conducting periodic AI system audits and reviews
- Training analysts to interpret and act on AI insights
- Creating knowledge transfer protocols for new team members
- Scaling AI capabilities across multiple use cases
- Integrating AI outcomes into incident post-mortems
- Encouraging a culture of data-driven decision making
- Measuring maturity progression across AI capabilities
- Aligning AI initiatives with overall security strategy
- Preparing for third-party audits of AI systems
Module 15: Real-World AI Incident Response Projects - Project 1: Automating phishing incident triage with AI classification
- Project 2: Building a model to detect abnormal database access
- Project 3: Developing an AI-powered lateral movement detector
- Project 4: Creating a UEBA system for high-privilege accounts
- Project 5: Implementing automated containment for ransomware alerts
- Project 6: Designing a deep learning model for process tree analysis
- Project 7: Developing a cloud anomaly detector for IAM changes
- Project 8: Building a network beaconing detection system
- Project 9: Creating an AI-enhanced threat hunting query library
- Project 10: Integrating AI insights into existing SOAR workflows
- Analysing real incident datasets to train custom models
- Validating model outputs against known breach patterns
- Drafting incident response playbooks based on AI findings
- Presenting AI-driven insights to virtual executive panels
- Graduating with a portfolio of applied AI security projects
Module 16: Certification, Career Advancement, and Next Steps - Preparing for the final assessment to earn your Certificate of Completion
- Reviewing key concepts and decision-making frameworks
- Practicing AI evaluation scenarios under time constraints
- Documenting your learning journey and project portfolio
- How to showcase your certification on LinkedIn and resumes
- Leveraging the credential in salary negotiations and promotions
- Joining the global alumni network of AI security practitioners
- Accessing advanced content and specialisation tracks
- Staying current with monthly AI threat intelligence updates
- Participating in exclusive roundtables with industry leaders
- Receiving job board access for AI-focused security roles
- Opportunities to contribute to open-source AI security tools
- Pathways to advanced certifications and specialisations
- Building a personal brand as an AI-aware defender
- Your roadmap: from course completion to career transformation
- Analysing NetFlow, PCAP, and Zeek/Bro logs with AI
- Detecting encrypted C2 traffic through behavioural proxies
- Identifying DNS tunneling using frequency and payload analysis
- Using statistical models to detect data exfiltration
- Profiling normal network conversation patterns
- Detecting port scanning and network enumeration via sequence learning
- Mapping attacker lateral movement through network paths
- Clustering suspicious destination IPs and domains
- Using TLS fingerprinting to identify malicious clients
- Analysing HTTP headers and user-agent anomalies
- Detecting beaconing behaviour through timing models
- Reducing network noise with intelligent flow filtering
- Correlating network AI alerts with endpoint events
- Automating firewall rule updates based on AI findings
- Scaling network AI across distributed分支机构
Module 12: AI Integration with SIEM and SOAR Platforms - Architecture patterns for embedding AI into existing tools
- Extending Splunk, Sentinel, and QRadar with custom AI models
- Parsing and enriching SIEM alerts with AI-generated context
- Using AI to group related alerts into coherent incidents
- Automating alert tagging and categorisation
- Routing high-priority incidents to appropriate analysts
- Generating preliminary investigation summaries
- Integrating AI predictions into SOAR decision trees
- Using confidence scores to gate automated actions
- Building dynamic dashboards with AI-derived metrics
- Scheduling AI model inference as part of rules
- Versioning and testing AI logic within detection pipelines
- Monitoring AI performance within the SIEM environment
- Establishing feedback loops from analysts to AI systems
- Documenting integration points for audit and compliance
Module 13: Measuring and Optimising AI Performance - Defining KPIs for AI-augmented security operations
- Tracking mean time to detect (MTTD) and mean time to respond (MTTR)
- Calculating reduction in alert volume and false positives
- Measuring analyst time savings and case throughput
- Analysing detection rate improvements for key threat types
- Using confusion matrices to evaluate model accuracy
- Calculating precision, recall, F1-score for security models
- Monitoring model drift and degradation over time
- Automating retraining triggers based on performance thresholds
- Conducting A/B testing of AI detection rules
- Assessing business impact of AI-integrated IR workflows
- Reporting AI programme outcomes to executive stakeholders
- Using benchmarks to compare performance across teams
- Identifying bottlenecks in AI model deployment
- Optimising inference speed and resource usage
Module 14: Building a Sustainable AI-Driven Security Programme - Creating a roadmap for AI adoption in your organisation
- Establishing cross-functional AI governance teams
- Developing policies for AI model development and deployment
- Defining ownership and accountability for AI systems
- Setting up model versioning and change control
- Documenting model assumptions, limitations, and constraints
- Conducting periodic AI system audits and reviews
- Training analysts to interpret and act on AI insights
- Creating knowledge transfer protocols for new team members
- Scaling AI capabilities across multiple use cases
- Integrating AI outcomes into incident post-mortems
- Encouraging a culture of data-driven decision making
- Measuring maturity progression across AI capabilities
- Aligning AI initiatives with overall security strategy
- Preparing for third-party audits of AI systems
Module 15: Real-World AI Incident Response Projects - Project 1: Automating phishing incident triage with AI classification
- Project 2: Building a model to detect abnormal database access
- Project 3: Developing an AI-powered lateral movement detector
- Project 4: Creating a UEBA system for high-privilege accounts
- Project 5: Implementing automated containment for ransomware alerts
- Project 6: Designing a deep learning model for process tree analysis
- Project 7: Developing a cloud anomaly detector for IAM changes
- Project 8: Building a network beaconing detection system
- Project 9: Creating an AI-enhanced threat hunting query library
- Project 10: Integrating AI insights into existing SOAR workflows
- Analysing real incident datasets to train custom models
- Validating model outputs against known breach patterns
- Drafting incident response playbooks based on AI findings
- Presenting AI-driven insights to virtual executive panels
- Graduating with a portfolio of applied AI security projects
Module 16: Certification, Career Advancement, and Next Steps - Preparing for the final assessment to earn your Certificate of Completion
- Reviewing key concepts and decision-making frameworks
- Practicing AI evaluation scenarios under time constraints
- Documenting your learning journey and project portfolio
- How to showcase your certification on LinkedIn and resumes
- Leveraging the credential in salary negotiations and promotions
- Joining the global alumni network of AI security practitioners
- Accessing advanced content and specialisation tracks
- Staying current with monthly AI threat intelligence updates
- Participating in exclusive roundtables with industry leaders
- Receiving job board access for AI-focused security roles
- Opportunities to contribute to open-source AI security tools
- Pathways to advanced certifications and specialisations
- Building a personal brand as an AI-aware defender
- Your roadmap: from course completion to career transformation
- Defining KPIs for AI-augmented security operations
- Tracking mean time to detect (MTTD) and mean time to respond (MTTR)
- Calculating reduction in alert volume and false positives
- Measuring analyst time savings and case throughput
- Analysing detection rate improvements for key threat types
- Using confusion matrices to evaluate model accuracy
- Calculating precision, recall, F1-score for security models
- Monitoring model drift and degradation over time
- Automating retraining triggers based on performance thresholds
- Conducting A/B testing of AI detection rules
- Assessing business impact of AI-integrated IR workflows
- Reporting AI programme outcomes to executive stakeholders
- Using benchmarks to compare performance across teams
- Identifying bottlenecks in AI model deployment
- Optimising inference speed and resource usage
Module 14: Building a Sustainable AI-Driven Security Programme - Creating a roadmap for AI adoption in your organisation
- Establishing cross-functional AI governance teams
- Developing policies for AI model development and deployment
- Defining ownership and accountability for AI systems
- Setting up model versioning and change control
- Documenting model assumptions, limitations, and constraints
- Conducting periodic AI system audits and reviews
- Training analysts to interpret and act on AI insights
- Creating knowledge transfer protocols for new team members
- Scaling AI capabilities across multiple use cases
- Integrating AI outcomes into incident post-mortems
- Encouraging a culture of data-driven decision making
- Measuring maturity progression across AI capabilities
- Aligning AI initiatives with overall security strategy
- Preparing for third-party audits of AI systems
Module 15: Real-World AI Incident Response Projects - Project 1: Automating phishing incident triage with AI classification
- Project 2: Building a model to detect abnormal database access
- Project 3: Developing an AI-powered lateral movement detector
- Project 4: Creating a UEBA system for high-privilege accounts
- Project 5: Implementing automated containment for ransomware alerts
- Project 6: Designing a deep learning model for process tree analysis
- Project 7: Developing a cloud anomaly detector for IAM changes
- Project 8: Building a network beaconing detection system
- Project 9: Creating an AI-enhanced threat hunting query library
- Project 10: Integrating AI insights into existing SOAR workflows
- Analysing real incident datasets to train custom models
- Validating model outputs against known breach patterns
- Drafting incident response playbooks based on AI findings
- Presenting AI-driven insights to virtual executive panels
- Graduating with a portfolio of applied AI security projects
Module 16: Certification, Career Advancement, and Next Steps - Preparing for the final assessment to earn your Certificate of Completion
- Reviewing key concepts and decision-making frameworks
- Practicing AI evaluation scenarios under time constraints
- Documenting your learning journey and project portfolio
- How to showcase your certification on LinkedIn and resumes
- Leveraging the credential in salary negotiations and promotions
- Joining the global alumni network of AI security practitioners
- Accessing advanced content and specialisation tracks
- Staying current with monthly AI threat intelligence updates
- Participating in exclusive roundtables with industry leaders
- Receiving job board access for AI-focused security roles
- Opportunities to contribute to open-source AI security tools
- Pathways to advanced certifications and specialisations
- Building a personal brand as an AI-aware defender
- Your roadmap: from course completion to career transformation
- Project 1: Automating phishing incident triage with AI classification
- Project 2: Building a model to detect abnormal database access
- Project 3: Developing an AI-powered lateral movement detector
- Project 4: Creating a UEBA system for high-privilege accounts
- Project 5: Implementing automated containment for ransomware alerts
- Project 6: Designing a deep learning model for process tree analysis
- Project 7: Developing a cloud anomaly detector for IAM changes
- Project 8: Building a network beaconing detection system
- Project 9: Creating an AI-enhanced threat hunting query library
- Project 10: Integrating AI insights into existing SOAR workflows
- Analysing real incident datasets to train custom models
- Validating model outputs against known breach patterns
- Drafting incident response playbooks based on AI findings
- Presenting AI-driven insights to virtual executive panels
- Graduating with a portfolio of applied AI security projects