Mastering AI-Driven Security Analytics with Splunk
You're not behind. Not yet. But the clock is ticking. Every day your organisation’s security operations rely on manual triage, delayed threat detection, or fragmented tools, you're one exploit away from a breach that could make headlines. The threat landscape isn’t just evolving-it’s accelerating. And the professionals who thrive? They’re not just keeping pace, they're leading it. With automation, intelligence, and precision that only AI-driven analytics can deliver. You’ve likely heard about Splunk’s potential. Maybe you’ve even used it. But if you’re still reacting to alerts instead of predicting threats, consolidating dashboards instead of driving decisions, you’re not using Splunk-you’re being used by it. The gap isn’t your skill. It’s your framework. And that’s exactly what this course closes. Mastering AI-Driven Security Analytics with Splunk transforms you from a reactive analyst into a proactive architect of intelligent security systems. You will go from concept to execution in under 30 days, building a real-world, board-ready AI-powered threat detection use case-deployable in your environment, measurable in ROI, and recognised by CISOs as a strategic differentiator. One graduate, Maria C., a senior security analyst at a financial services firm, applied the framework to detect lateral movement anomalies across 12,000 endpoints. Within two weeks of completing the course, her model reduced false positives by 68% and cut incident response time from 4.7 hours to 18 minutes. Her tool became part of the org’s SOC playbook-and she was promoted to Threat Intelligence Lead. This isn't theoretical. This is tactical. A structured, no-fluff path that turns your Splunk access into a force multiplier. No guesswork. No dead ends. Just repeatable processes that align with MITRE ATT&CK, NIST frameworks, and enterprise-grade detection engineering standards. If you’re ready to stop playing catch-up and start shaping the future of security operations, this is your leverage. Here’s how this course is structured to help you get there.Course Format & Delivery Details This is a self-paced, on-demand learning experience with immediate online access. You can start today, progress at your own speed, and complete the program in as little as 25–30 hours-typically within three to four weeks for most professionals working full time. Your access never expires. You receive lifetime access to all course materials, including every future update, enhancement, and additional module released over time-completely free. These updates ensure your skills remain aligned with evolving Splunk features, AI integration advancements, and emerging cyber threat patterns. Flexible, Global, and Always Available
The course is 24/7 accessible from any device-laptop, tablet, or mobile-ensuring you can learn during commutes, between incidents, or after hours without disrupting your workflow. The interface is lightweight, fast-loading, and works seamlessly even on restricted corporate networks. - Self-paced, no deadlines or fixed schedules
- Lifetime access, including all future updates
- Optimised for mobile and offline reading modes
- Available globally, in any time zone
Expert Support and Accountability
You’re not on your own. This course includes direct instructor access through a dedicated support channel. Receive guidance on your use case design, data model implementation, AI rule tuning, and certification project feedback from certified Splunk architects with over a decade of defensive cyber experience. Have a question at 2 a.m. after a long shift? Submit it. You’ll receive a detailed response within 24 business hours. No chatbots. No templates. Just expert humans who understand your environment and objectives. Certificate of Completion from The Art of Service
Upon finishing all modules and submitting your final AI-driven detection project, you will receive a Certificate of Completion issued by The Art of Service. This credential is recognised by over 1,200 organisations worldwide, including Fortune 500 security teams, government contractors, and global consulting firms. The certificate includes a unique verification ID, portable digital badge, and integration-ready metadata-making it easy to showcase on LinkedIn, resumes, or compliance documentation. It signals not just completion, but applied mastery of AI-augmented security analytics. No Risk. No Hidden Fees. Full Confidence.
Pricing is transparent and straightforward-one flat fee with no hidden charges, recurring billing, or surprise costs. You pay once, own it forever. We accept all major payment methods including Visa, Mastercard, and PayPal. Your transaction is secured with bank-level encryption and processed through a PCI-compliant gateway. If you complete the first three modules and don’t believe this course will deliver measurable value to your career or security operations, simply request a refund. You’ll be fully reimbursed-no questions asked. This is a 100% satisfied or refunded guarantee, designed to eliminate all financial risk. What Happens After Enrollment?
After registration, you’ll receive a confirmation email. Once your course materials are fully activated, a separate access email will be sent with your login details and entry point to the platform. Activation is automated and typically completes within minutes, though processing may vary slightly based on system verification protocols. Will This Work for Me?
This course works even if you’ve never built a machine learning model, if your Splunk instance is outdated, or if your organisation resists change. The methodology starts where you are-whether you're a junior analyst, SOC team lead, or incident responder-and builds outward with incremental, deployable wins. We’ve had security engineers at healthcare providers use the threat clustering templates to identify ransomware staging behaviour. Cloud security architects at tech startups have embedded anomaly scoring directly into their CI/CD pipelines. One government contractor used the course framework to pass a stringent compliance audit with zero findings. This is not just for “experts.” It’s for professionals who want to become indispensable. If you have access to Splunk and a determination to build smarter defences, you have everything you need to succeed.
Module 1: Foundations of AI-Driven Security Analytics - Understanding the shift from reactive to predictive security
- Defining AI, ML, and automation in the context of threat detection
- Core principles of data-driven security operations
- How Splunk enables real-time telemetry ingestion and correlation
- Overview of the MITRE ATT&CK framework and its integration with AI models
- Introduction to NIST SP 800-150 and automated incident handling
- Common gaps in legacy SOC architectures
- Establishing a use case prioritisation matrix
- Mapping business risk to technical feasibility
- Creating your personal learning and impact roadmap
Module 2: Advanced Splunk Architecture for AI Integration - Splunk Enterprise vs Splunk Cloud: selecting the right environment
- Configuring indexers, forwarders, and search heads for high-volume analytics
- Optimising data retention and storage for AI model training
- Setting up distributed search and load balancing for performance
- Securing administrative access and role-based permissions
- Integrating Splunk with SIEM, firewalls, EDR, and cloud APIs
- Data normalisation using CIM (Common Information Model)
- Building reusable field extractions and event tagging
- Implementing data model acceleration for faster queries
- Monitoring internal Splunk performance with _internal logs
Module 3: Data Engineering for Security Machine Learning - Identifying high-fidelity data sources for AI training
- Understanding log diversity: Windows, Linux, cloud, network
- Enriching raw logs with threat intelligence feeds (STIX/TAXII)
- Using lookups and external data to enhance context
- Designing feature sets for anomaly detection models
- Creating derived metrics: session duration, login frequency, data volume
- Handling missing or malformed data in security streams
- Time binning and sliding window analysis for temporal patterns
- Scaling data preprocessing across multi-tenant environments
- Validating data integrity with statistical summary checks
Module 4: Building Predictive Detection Models - Choosing between supervised and unsupervised learning for security
- Introduction to Splunk Machine Learning Toolkit (MLTK)
- Training outlier detection models for rare event identification
- Using clustering algorithms to group similar attack behaviours
- Applying regression models to predict escalation timelines
- Classification models for malware vs benign process detection
- Feature selection and dimensionality reduction techniques
- Setting thresholds and sensitivity levels for alerts
- Interpreting model confidence and probability scores
- Validating model outputs against historical incident data
Module 5: Anomaly Scoring and Risk-Based Alerting - Designing a unified risk scoring framework
- Weighting behavioural deviations by severity and context
- Calculating dynamic risk scores using weighted composite metrics
- Integrating anomaly scores into Splunk alerts
- Reducing alert fatigue through intelligent prioritisation
- Creating tiered escalation paths based on risk thresholds
- Linking anomaly scores to MITRE ATT&CK tactics
- Automating case creation in SOAR platforms via risk level
- Visualising risk trends over time with heatmaps
- Audit logging for model-driven decisions and compliance
Module 6: AI-Powered Use Case Development - Template-driven development of detection use cases
- Building a brute force attack predictor using login patterns
- Designing domain generation algorithm (DGA) detection
- Identifying lateral movement via PowerShell and WMI logs
- Modelling credential dumping with LSASS access patterns
- Detecting data exfiltration through DNS tunneling
- Creating insider threat profiles using UEBA logic
- Analysing cloud API misuse in AWS and Azure environments
- Tracking anomalous SaaS application access
- Developing ransomware early warning indicators
Module 7: Search Optimization and Performance Engineering - Writing efficient SPL (Search Processing Language) for large datasets
- Using stats, chart, and timechart for fast summarisation
- Applying early filtering with WHERE and SEARCH commands
- Minimising resource usage with subsearch optimisation
- Leveraging tstats for accelerated data model queries
- Using precomputed summaries to reduce computation time
- Monitoring search performance with Job Inspector
- Tuning timeouts and concurrency limits for enterprise loads
- Debugging slow or failing searches systematically
- Implementing query caching strategies for recurring reports
Module 8: Dashboarding and Executive Communication - Designing dashboards for technical and non-technical audiences
- Creating real-time threat landscape overviews
- Mapping detections to compliance requirements (GDPR, HIPAA, ISO 27001)
- Building board-ready risk heatmaps and KPIs
- Visualising AI model performance: precision, recall, F1 score
- Using single value, gauges, and trend indicators effectively
- Incorporating drill-downs for forensic investigation
- Embedding model confidence levels into dashboard alerts
- Sharing dashboards securely with stakeholders
- Automating PDF report generation for CISOs and auditors
Module 9: Automation and Orchestration Integration - Configuring Splunk alerts to trigger SOAR playbooks
- Sending parsed data to Phantom, Demisto, or Cortex XSOAR
- Using webhooks and REST API integrations for automation
- Automated enrichment of incidents with threat intel
- Initiating containment actions based on AI confidence scores
- Executing playbook branching logic based on severity
- Logging all automated actions for audit and review
- Building feedback loops from SOAR outcomes to model retraining
- Monitoring automation success rates and false triggers
- Managing automation safely in production environments
Module 10: Model Validation and Continuous Improvement - Measuring detection efficacy using ground truth data
- Calculating true positive, false positive, and false negative rates
- Tracking model drift over time with retraining triggers
- Updating models with new adversarial techniques
- Conducting red team collaboration for model testing
- Using controlled breach simulations to validate alerts
- Implementing A/B testing for detection rule optimisation
- Analysing operator feedback to refine scoring logic
- Automating performance reporting for ML models
- Scheduling periodic model health checks
Module 11: Real-World Threat Detection Projects - Project 1: Detecting suspicious service account activity
- Project 2: Identifying anomalous database query volumes
- Project 3: Monitoring for unauthorised cloud storage access
- Project 4: Predicting phishing campaign follow-ups
- Project 5: Clustering suspicious HR system access patterns
- Project 6: Detecting living-off-the-land binary usage
- Project 7: Identifying misconfigured S3 buckets via API logs
- Project 8: Correlating failed RDP attempts across subnets
- Project 9: Building a zero-day exploit early detection proxy
- Project 10: Analysing AV quarantine logs for evasion patterns
Module 12: Adversarial ML and Model Security - Understanding threats to machine learning systems
- Data poisoning attacks in security analytics
- Model inversion and evasion techniques
- Defending against gradient-based adversarial inputs
- Monitoring for manipulation of training data sources
- Implementing input sanitisation and validation pipelines
- Using ensemble models to increase robustness
- Logging and alerting on suspicious model access
- Securing model artifacts and version history
- Applying least privilege to MLTK workflows
Module 13: Cross-Platform AI Integration Strategies - Exporting Splunk-trained models to external systems
- Importing third-party models into Splunk via APIs
- Integrating with Python-based ML pipelines (scikit-learn, TensorFlow)
- Using Docker containers to run external AI services
- Streaming predictions from external models into Splunk
- Synchronising ground truth labels across platforms
- Centralising model governance and policy enforcement
- Monitoring hybrid AI environments for consistency
- Aligning Splunk tags with MITRE D3FEND controls
- Documenting integration architecture for audits
Module 14: Compliance and Governance of AI Systems - Meeting regulatory requirements for algorithmic transparency
- Documenting data provenance and model training lineage
- Ensuring fairness and avoiding bias in security models
- Conducting impact assessments for AI-driven decisions
- Creating model cards for internal review and approval
- Implementing model version control and rollback plans
- Encrypting sensitive training data at rest and in transit
- Managing consent and data rights in monitored environments
- Archiving model decisions for incident reconstruction
- Preparing for AI audits by internal and external assessors
Module 15: Operationalising AI at Scale - Deploying detection models across multiple Splunk instances
- Managing configurations with Splunk Deployment Server
- Using Git-based workflows for change control
- Automating deployment with Ansible and Terraform
- Rolling out updates in staged environments
- Monitoring model performance enterprise-wide
- Creating standard operating procedures for AI use cases
- Training peer analysts on new detection logic
- Establishing SOC-wide feedback mechanisms
- Scaling from pilot use cases to organisation-wide coverage
Module 16: Career Advancement and Certification Preparation - Building a professional portfolio of detection models
- Writing compelling case studies for performance reviews
- Preparing for Splunk certification exams (SPLK-3001, SPLK-3002)
- Translating project outcomes into business impact statements
- Positioning yourself as a candidate for promotion or new roles
- Networking within the Splunk and cybersecurity communities
- Contributing detection content to open-source repositories
- Presenting at internal tech talks or industry meetups
- Using your Certificate of Completion to validate expertise
- Accessing exclusive job boards and employer partnerships
Module 17: Capstone Project and Certification Submission - Selecting a high-impact, deployable use case from your environment
- Defining objectives, scope, and success criteria
- Collecting and preprocessing relevant data sources
- Training and validating an AI model using Splunk MLTK
- Configuring dynamic risk scoring and alerting
- Building a dashboard for operational visibility
- Integrating with SOAR or ticketing systems
- Documenting assumptions, limitations, and maintenance needs
- Creating a presentation deck for stakeholder review
- Submitting your project for evaluation and certification
Module 18: Post-Course Integration and Lifelong Learning - Setting up a personal update subscription for Splunk releases
- Joining advanced user groups and expert forums
- Accessing the private alumni network for peer support
- Receiving monthly updates on new detection techniques
- Participating in quarterly live Q&A review sessions
- Downloading expanded use case templates and cheat sheets
- Tracking your skill progression with gamified milestones
- Enrolling in advanced specialisations (UEBA, cloud threat hunting)
- Utilising progress tracking tools within the learning platform
- Leveraging your Certificate of Completion for career growth
- Understanding the shift from reactive to predictive security
- Defining AI, ML, and automation in the context of threat detection
- Core principles of data-driven security operations
- How Splunk enables real-time telemetry ingestion and correlation
- Overview of the MITRE ATT&CK framework and its integration with AI models
- Introduction to NIST SP 800-150 and automated incident handling
- Common gaps in legacy SOC architectures
- Establishing a use case prioritisation matrix
- Mapping business risk to technical feasibility
- Creating your personal learning and impact roadmap
Module 2: Advanced Splunk Architecture for AI Integration - Splunk Enterprise vs Splunk Cloud: selecting the right environment
- Configuring indexers, forwarders, and search heads for high-volume analytics
- Optimising data retention and storage for AI model training
- Setting up distributed search and load balancing for performance
- Securing administrative access and role-based permissions
- Integrating Splunk with SIEM, firewalls, EDR, and cloud APIs
- Data normalisation using CIM (Common Information Model)
- Building reusable field extractions and event tagging
- Implementing data model acceleration for faster queries
- Monitoring internal Splunk performance with _internal logs
Module 3: Data Engineering for Security Machine Learning - Identifying high-fidelity data sources for AI training
- Understanding log diversity: Windows, Linux, cloud, network
- Enriching raw logs with threat intelligence feeds (STIX/TAXII)
- Using lookups and external data to enhance context
- Designing feature sets for anomaly detection models
- Creating derived metrics: session duration, login frequency, data volume
- Handling missing or malformed data in security streams
- Time binning and sliding window analysis for temporal patterns
- Scaling data preprocessing across multi-tenant environments
- Validating data integrity with statistical summary checks
Module 4: Building Predictive Detection Models - Choosing between supervised and unsupervised learning for security
- Introduction to Splunk Machine Learning Toolkit (MLTK)
- Training outlier detection models for rare event identification
- Using clustering algorithms to group similar attack behaviours
- Applying regression models to predict escalation timelines
- Classification models for malware vs benign process detection
- Feature selection and dimensionality reduction techniques
- Setting thresholds and sensitivity levels for alerts
- Interpreting model confidence and probability scores
- Validating model outputs against historical incident data
Module 5: Anomaly Scoring and Risk-Based Alerting - Designing a unified risk scoring framework
- Weighting behavioural deviations by severity and context
- Calculating dynamic risk scores using weighted composite metrics
- Integrating anomaly scores into Splunk alerts
- Reducing alert fatigue through intelligent prioritisation
- Creating tiered escalation paths based on risk thresholds
- Linking anomaly scores to MITRE ATT&CK tactics
- Automating case creation in SOAR platforms via risk level
- Visualising risk trends over time with heatmaps
- Audit logging for model-driven decisions and compliance
Module 6: AI-Powered Use Case Development - Template-driven development of detection use cases
- Building a brute force attack predictor using login patterns
- Designing domain generation algorithm (DGA) detection
- Identifying lateral movement via PowerShell and WMI logs
- Modelling credential dumping with LSASS access patterns
- Detecting data exfiltration through DNS tunneling
- Creating insider threat profiles using UEBA logic
- Analysing cloud API misuse in AWS and Azure environments
- Tracking anomalous SaaS application access
- Developing ransomware early warning indicators
Module 7: Search Optimization and Performance Engineering - Writing efficient SPL (Search Processing Language) for large datasets
- Using stats, chart, and timechart for fast summarisation
- Applying early filtering with WHERE and SEARCH commands
- Minimising resource usage with subsearch optimisation
- Leveraging tstats for accelerated data model queries
- Using precomputed summaries to reduce computation time
- Monitoring search performance with Job Inspector
- Tuning timeouts and concurrency limits for enterprise loads
- Debugging slow or failing searches systematically
- Implementing query caching strategies for recurring reports
Module 8: Dashboarding and Executive Communication - Designing dashboards for technical and non-technical audiences
- Creating real-time threat landscape overviews
- Mapping detections to compliance requirements (GDPR, HIPAA, ISO 27001)
- Building board-ready risk heatmaps and KPIs
- Visualising AI model performance: precision, recall, F1 score
- Using single value, gauges, and trend indicators effectively
- Incorporating drill-downs for forensic investigation
- Embedding model confidence levels into dashboard alerts
- Sharing dashboards securely with stakeholders
- Automating PDF report generation for CISOs and auditors
Module 9: Automation and Orchestration Integration - Configuring Splunk alerts to trigger SOAR playbooks
- Sending parsed data to Phantom, Demisto, or Cortex XSOAR
- Using webhooks and REST API integrations for automation
- Automated enrichment of incidents with threat intel
- Initiating containment actions based on AI confidence scores
- Executing playbook branching logic based on severity
- Logging all automated actions for audit and review
- Building feedback loops from SOAR outcomes to model retraining
- Monitoring automation success rates and false triggers
- Managing automation safely in production environments
Module 10: Model Validation and Continuous Improvement - Measuring detection efficacy using ground truth data
- Calculating true positive, false positive, and false negative rates
- Tracking model drift over time with retraining triggers
- Updating models with new adversarial techniques
- Conducting red team collaboration for model testing
- Using controlled breach simulations to validate alerts
- Implementing A/B testing for detection rule optimisation
- Analysing operator feedback to refine scoring logic
- Automating performance reporting for ML models
- Scheduling periodic model health checks
Module 11: Real-World Threat Detection Projects - Project 1: Detecting suspicious service account activity
- Project 2: Identifying anomalous database query volumes
- Project 3: Monitoring for unauthorised cloud storage access
- Project 4: Predicting phishing campaign follow-ups
- Project 5: Clustering suspicious HR system access patterns
- Project 6: Detecting living-off-the-land binary usage
- Project 7: Identifying misconfigured S3 buckets via API logs
- Project 8: Correlating failed RDP attempts across subnets
- Project 9: Building a zero-day exploit early detection proxy
- Project 10: Analysing AV quarantine logs for evasion patterns
Module 12: Adversarial ML and Model Security - Understanding threats to machine learning systems
- Data poisoning attacks in security analytics
- Model inversion and evasion techniques
- Defending against gradient-based adversarial inputs
- Monitoring for manipulation of training data sources
- Implementing input sanitisation and validation pipelines
- Using ensemble models to increase robustness
- Logging and alerting on suspicious model access
- Securing model artifacts and version history
- Applying least privilege to MLTK workflows
Module 13: Cross-Platform AI Integration Strategies - Exporting Splunk-trained models to external systems
- Importing third-party models into Splunk via APIs
- Integrating with Python-based ML pipelines (scikit-learn, TensorFlow)
- Using Docker containers to run external AI services
- Streaming predictions from external models into Splunk
- Synchronising ground truth labels across platforms
- Centralising model governance and policy enforcement
- Monitoring hybrid AI environments for consistency
- Aligning Splunk tags with MITRE D3FEND controls
- Documenting integration architecture for audits
Module 14: Compliance and Governance of AI Systems - Meeting regulatory requirements for algorithmic transparency
- Documenting data provenance and model training lineage
- Ensuring fairness and avoiding bias in security models
- Conducting impact assessments for AI-driven decisions
- Creating model cards for internal review and approval
- Implementing model version control and rollback plans
- Encrypting sensitive training data at rest and in transit
- Managing consent and data rights in monitored environments
- Archiving model decisions for incident reconstruction
- Preparing for AI audits by internal and external assessors
Module 15: Operationalising AI at Scale - Deploying detection models across multiple Splunk instances
- Managing configurations with Splunk Deployment Server
- Using Git-based workflows for change control
- Automating deployment with Ansible and Terraform
- Rolling out updates in staged environments
- Monitoring model performance enterprise-wide
- Creating standard operating procedures for AI use cases
- Training peer analysts on new detection logic
- Establishing SOC-wide feedback mechanisms
- Scaling from pilot use cases to organisation-wide coverage
Module 16: Career Advancement and Certification Preparation - Building a professional portfolio of detection models
- Writing compelling case studies for performance reviews
- Preparing for Splunk certification exams (SPLK-3001, SPLK-3002)
- Translating project outcomes into business impact statements
- Positioning yourself as a candidate for promotion or new roles
- Networking within the Splunk and cybersecurity communities
- Contributing detection content to open-source repositories
- Presenting at internal tech talks or industry meetups
- Using your Certificate of Completion to validate expertise
- Accessing exclusive job boards and employer partnerships
Module 17: Capstone Project and Certification Submission - Selecting a high-impact, deployable use case from your environment
- Defining objectives, scope, and success criteria
- Collecting and preprocessing relevant data sources
- Training and validating an AI model using Splunk MLTK
- Configuring dynamic risk scoring and alerting
- Building a dashboard for operational visibility
- Integrating with SOAR or ticketing systems
- Documenting assumptions, limitations, and maintenance needs
- Creating a presentation deck for stakeholder review
- Submitting your project for evaluation and certification
Module 18: Post-Course Integration and Lifelong Learning - Setting up a personal update subscription for Splunk releases
- Joining advanced user groups and expert forums
- Accessing the private alumni network for peer support
- Receiving monthly updates on new detection techniques
- Participating in quarterly live Q&A review sessions
- Downloading expanded use case templates and cheat sheets
- Tracking your skill progression with gamified milestones
- Enrolling in advanced specialisations (UEBA, cloud threat hunting)
- Utilising progress tracking tools within the learning platform
- Leveraging your Certificate of Completion for career growth
- Identifying high-fidelity data sources for AI training
- Understanding log diversity: Windows, Linux, cloud, network
- Enriching raw logs with threat intelligence feeds (STIX/TAXII)
- Using lookups and external data to enhance context
- Designing feature sets for anomaly detection models
- Creating derived metrics: session duration, login frequency, data volume
- Handling missing or malformed data in security streams
- Time binning and sliding window analysis for temporal patterns
- Scaling data preprocessing across multi-tenant environments
- Validating data integrity with statistical summary checks
Module 4: Building Predictive Detection Models - Choosing between supervised and unsupervised learning for security
- Introduction to Splunk Machine Learning Toolkit (MLTK)
- Training outlier detection models for rare event identification
- Using clustering algorithms to group similar attack behaviours
- Applying regression models to predict escalation timelines
- Classification models for malware vs benign process detection
- Feature selection and dimensionality reduction techniques
- Setting thresholds and sensitivity levels for alerts
- Interpreting model confidence and probability scores
- Validating model outputs against historical incident data
Module 5: Anomaly Scoring and Risk-Based Alerting - Designing a unified risk scoring framework
- Weighting behavioural deviations by severity and context
- Calculating dynamic risk scores using weighted composite metrics
- Integrating anomaly scores into Splunk alerts
- Reducing alert fatigue through intelligent prioritisation
- Creating tiered escalation paths based on risk thresholds
- Linking anomaly scores to MITRE ATT&CK tactics
- Automating case creation in SOAR platforms via risk level
- Visualising risk trends over time with heatmaps
- Audit logging for model-driven decisions and compliance
Module 6: AI-Powered Use Case Development - Template-driven development of detection use cases
- Building a brute force attack predictor using login patterns
- Designing domain generation algorithm (DGA) detection
- Identifying lateral movement via PowerShell and WMI logs
- Modelling credential dumping with LSASS access patterns
- Detecting data exfiltration through DNS tunneling
- Creating insider threat profiles using UEBA logic
- Analysing cloud API misuse in AWS and Azure environments
- Tracking anomalous SaaS application access
- Developing ransomware early warning indicators
Module 7: Search Optimization and Performance Engineering - Writing efficient SPL (Search Processing Language) for large datasets
- Using stats, chart, and timechart for fast summarisation
- Applying early filtering with WHERE and SEARCH commands
- Minimising resource usage with subsearch optimisation
- Leveraging tstats for accelerated data model queries
- Using precomputed summaries to reduce computation time
- Monitoring search performance with Job Inspector
- Tuning timeouts and concurrency limits for enterprise loads
- Debugging slow or failing searches systematically
- Implementing query caching strategies for recurring reports
Module 8: Dashboarding and Executive Communication - Designing dashboards for technical and non-technical audiences
- Creating real-time threat landscape overviews
- Mapping detections to compliance requirements (GDPR, HIPAA, ISO 27001)
- Building board-ready risk heatmaps and KPIs
- Visualising AI model performance: precision, recall, F1 score
- Using single value, gauges, and trend indicators effectively
- Incorporating drill-downs for forensic investigation
- Embedding model confidence levels into dashboard alerts
- Sharing dashboards securely with stakeholders
- Automating PDF report generation for CISOs and auditors
Module 9: Automation and Orchestration Integration - Configuring Splunk alerts to trigger SOAR playbooks
- Sending parsed data to Phantom, Demisto, or Cortex XSOAR
- Using webhooks and REST API integrations for automation
- Automated enrichment of incidents with threat intel
- Initiating containment actions based on AI confidence scores
- Executing playbook branching logic based on severity
- Logging all automated actions for audit and review
- Building feedback loops from SOAR outcomes to model retraining
- Monitoring automation success rates and false triggers
- Managing automation safely in production environments
Module 10: Model Validation and Continuous Improvement - Measuring detection efficacy using ground truth data
- Calculating true positive, false positive, and false negative rates
- Tracking model drift over time with retraining triggers
- Updating models with new adversarial techniques
- Conducting red team collaboration for model testing
- Using controlled breach simulations to validate alerts
- Implementing A/B testing for detection rule optimisation
- Analysing operator feedback to refine scoring logic
- Automating performance reporting for ML models
- Scheduling periodic model health checks
Module 11: Real-World Threat Detection Projects - Project 1: Detecting suspicious service account activity
- Project 2: Identifying anomalous database query volumes
- Project 3: Monitoring for unauthorised cloud storage access
- Project 4: Predicting phishing campaign follow-ups
- Project 5: Clustering suspicious HR system access patterns
- Project 6: Detecting living-off-the-land binary usage
- Project 7: Identifying misconfigured S3 buckets via API logs
- Project 8: Correlating failed RDP attempts across subnets
- Project 9: Building a zero-day exploit early detection proxy
- Project 10: Analysing AV quarantine logs for evasion patterns
Module 12: Adversarial ML and Model Security - Understanding threats to machine learning systems
- Data poisoning attacks in security analytics
- Model inversion and evasion techniques
- Defending against gradient-based adversarial inputs
- Monitoring for manipulation of training data sources
- Implementing input sanitisation and validation pipelines
- Using ensemble models to increase robustness
- Logging and alerting on suspicious model access
- Securing model artifacts and version history
- Applying least privilege to MLTK workflows
Module 13: Cross-Platform AI Integration Strategies - Exporting Splunk-trained models to external systems
- Importing third-party models into Splunk via APIs
- Integrating with Python-based ML pipelines (scikit-learn, TensorFlow)
- Using Docker containers to run external AI services
- Streaming predictions from external models into Splunk
- Synchronising ground truth labels across platforms
- Centralising model governance and policy enforcement
- Monitoring hybrid AI environments for consistency
- Aligning Splunk tags with MITRE D3FEND controls
- Documenting integration architecture for audits
Module 14: Compliance and Governance of AI Systems - Meeting regulatory requirements for algorithmic transparency
- Documenting data provenance and model training lineage
- Ensuring fairness and avoiding bias in security models
- Conducting impact assessments for AI-driven decisions
- Creating model cards for internal review and approval
- Implementing model version control and rollback plans
- Encrypting sensitive training data at rest and in transit
- Managing consent and data rights in monitored environments
- Archiving model decisions for incident reconstruction
- Preparing for AI audits by internal and external assessors
Module 15: Operationalising AI at Scale - Deploying detection models across multiple Splunk instances
- Managing configurations with Splunk Deployment Server
- Using Git-based workflows for change control
- Automating deployment with Ansible and Terraform
- Rolling out updates in staged environments
- Monitoring model performance enterprise-wide
- Creating standard operating procedures for AI use cases
- Training peer analysts on new detection logic
- Establishing SOC-wide feedback mechanisms
- Scaling from pilot use cases to organisation-wide coverage
Module 16: Career Advancement and Certification Preparation - Building a professional portfolio of detection models
- Writing compelling case studies for performance reviews
- Preparing for Splunk certification exams (SPLK-3001, SPLK-3002)
- Translating project outcomes into business impact statements
- Positioning yourself as a candidate for promotion or new roles
- Networking within the Splunk and cybersecurity communities
- Contributing detection content to open-source repositories
- Presenting at internal tech talks or industry meetups
- Using your Certificate of Completion to validate expertise
- Accessing exclusive job boards and employer partnerships
Module 17: Capstone Project and Certification Submission - Selecting a high-impact, deployable use case from your environment
- Defining objectives, scope, and success criteria
- Collecting and preprocessing relevant data sources
- Training and validating an AI model using Splunk MLTK
- Configuring dynamic risk scoring and alerting
- Building a dashboard for operational visibility
- Integrating with SOAR or ticketing systems
- Documenting assumptions, limitations, and maintenance needs
- Creating a presentation deck for stakeholder review
- Submitting your project for evaluation and certification
Module 18: Post-Course Integration and Lifelong Learning - Setting up a personal update subscription for Splunk releases
- Joining advanced user groups and expert forums
- Accessing the private alumni network for peer support
- Receiving monthly updates on new detection techniques
- Participating in quarterly live Q&A review sessions
- Downloading expanded use case templates and cheat sheets
- Tracking your skill progression with gamified milestones
- Enrolling in advanced specialisations (UEBA, cloud threat hunting)
- Utilising progress tracking tools within the learning platform
- Leveraging your Certificate of Completion for career growth
- Designing a unified risk scoring framework
- Weighting behavioural deviations by severity and context
- Calculating dynamic risk scores using weighted composite metrics
- Integrating anomaly scores into Splunk alerts
- Reducing alert fatigue through intelligent prioritisation
- Creating tiered escalation paths based on risk thresholds
- Linking anomaly scores to MITRE ATT&CK tactics
- Automating case creation in SOAR platforms via risk level
- Visualising risk trends over time with heatmaps
- Audit logging for model-driven decisions and compliance
Module 6: AI-Powered Use Case Development - Template-driven development of detection use cases
- Building a brute force attack predictor using login patterns
- Designing domain generation algorithm (DGA) detection
- Identifying lateral movement via PowerShell and WMI logs
- Modelling credential dumping with LSASS access patterns
- Detecting data exfiltration through DNS tunneling
- Creating insider threat profiles using UEBA logic
- Analysing cloud API misuse in AWS and Azure environments
- Tracking anomalous SaaS application access
- Developing ransomware early warning indicators
Module 7: Search Optimization and Performance Engineering - Writing efficient SPL (Search Processing Language) for large datasets
- Using stats, chart, and timechart for fast summarisation
- Applying early filtering with WHERE and SEARCH commands
- Minimising resource usage with subsearch optimisation
- Leveraging tstats for accelerated data model queries
- Using precomputed summaries to reduce computation time
- Monitoring search performance with Job Inspector
- Tuning timeouts and concurrency limits for enterprise loads
- Debugging slow or failing searches systematically
- Implementing query caching strategies for recurring reports
Module 8: Dashboarding and Executive Communication - Designing dashboards for technical and non-technical audiences
- Creating real-time threat landscape overviews
- Mapping detections to compliance requirements (GDPR, HIPAA, ISO 27001)
- Building board-ready risk heatmaps and KPIs
- Visualising AI model performance: precision, recall, F1 score
- Using single value, gauges, and trend indicators effectively
- Incorporating drill-downs for forensic investigation
- Embedding model confidence levels into dashboard alerts
- Sharing dashboards securely with stakeholders
- Automating PDF report generation for CISOs and auditors
Module 9: Automation and Orchestration Integration - Configuring Splunk alerts to trigger SOAR playbooks
- Sending parsed data to Phantom, Demisto, or Cortex XSOAR
- Using webhooks and REST API integrations for automation
- Automated enrichment of incidents with threat intel
- Initiating containment actions based on AI confidence scores
- Executing playbook branching logic based on severity
- Logging all automated actions for audit and review
- Building feedback loops from SOAR outcomes to model retraining
- Monitoring automation success rates and false triggers
- Managing automation safely in production environments
Module 10: Model Validation and Continuous Improvement - Measuring detection efficacy using ground truth data
- Calculating true positive, false positive, and false negative rates
- Tracking model drift over time with retraining triggers
- Updating models with new adversarial techniques
- Conducting red team collaboration for model testing
- Using controlled breach simulations to validate alerts
- Implementing A/B testing for detection rule optimisation
- Analysing operator feedback to refine scoring logic
- Automating performance reporting for ML models
- Scheduling periodic model health checks
Module 11: Real-World Threat Detection Projects - Project 1: Detecting suspicious service account activity
- Project 2: Identifying anomalous database query volumes
- Project 3: Monitoring for unauthorised cloud storage access
- Project 4: Predicting phishing campaign follow-ups
- Project 5: Clustering suspicious HR system access patterns
- Project 6: Detecting living-off-the-land binary usage
- Project 7: Identifying misconfigured S3 buckets via API logs
- Project 8: Correlating failed RDP attempts across subnets
- Project 9: Building a zero-day exploit early detection proxy
- Project 10: Analysing AV quarantine logs for evasion patterns
Module 12: Adversarial ML and Model Security - Understanding threats to machine learning systems
- Data poisoning attacks in security analytics
- Model inversion and evasion techniques
- Defending against gradient-based adversarial inputs
- Monitoring for manipulation of training data sources
- Implementing input sanitisation and validation pipelines
- Using ensemble models to increase robustness
- Logging and alerting on suspicious model access
- Securing model artifacts and version history
- Applying least privilege to MLTK workflows
Module 13: Cross-Platform AI Integration Strategies - Exporting Splunk-trained models to external systems
- Importing third-party models into Splunk via APIs
- Integrating with Python-based ML pipelines (scikit-learn, TensorFlow)
- Using Docker containers to run external AI services
- Streaming predictions from external models into Splunk
- Synchronising ground truth labels across platforms
- Centralising model governance and policy enforcement
- Monitoring hybrid AI environments for consistency
- Aligning Splunk tags with MITRE D3FEND controls
- Documenting integration architecture for audits
Module 14: Compliance and Governance of AI Systems - Meeting regulatory requirements for algorithmic transparency
- Documenting data provenance and model training lineage
- Ensuring fairness and avoiding bias in security models
- Conducting impact assessments for AI-driven decisions
- Creating model cards for internal review and approval
- Implementing model version control and rollback plans
- Encrypting sensitive training data at rest and in transit
- Managing consent and data rights in monitored environments
- Archiving model decisions for incident reconstruction
- Preparing for AI audits by internal and external assessors
Module 15: Operationalising AI at Scale - Deploying detection models across multiple Splunk instances
- Managing configurations with Splunk Deployment Server
- Using Git-based workflows for change control
- Automating deployment with Ansible and Terraform
- Rolling out updates in staged environments
- Monitoring model performance enterprise-wide
- Creating standard operating procedures for AI use cases
- Training peer analysts on new detection logic
- Establishing SOC-wide feedback mechanisms
- Scaling from pilot use cases to organisation-wide coverage
Module 16: Career Advancement and Certification Preparation - Building a professional portfolio of detection models
- Writing compelling case studies for performance reviews
- Preparing for Splunk certification exams (SPLK-3001, SPLK-3002)
- Translating project outcomes into business impact statements
- Positioning yourself as a candidate for promotion or new roles
- Networking within the Splunk and cybersecurity communities
- Contributing detection content to open-source repositories
- Presenting at internal tech talks or industry meetups
- Using your Certificate of Completion to validate expertise
- Accessing exclusive job boards and employer partnerships
Module 17: Capstone Project and Certification Submission - Selecting a high-impact, deployable use case from your environment
- Defining objectives, scope, and success criteria
- Collecting and preprocessing relevant data sources
- Training and validating an AI model using Splunk MLTK
- Configuring dynamic risk scoring and alerting
- Building a dashboard for operational visibility
- Integrating with SOAR or ticketing systems
- Documenting assumptions, limitations, and maintenance needs
- Creating a presentation deck for stakeholder review
- Submitting your project for evaluation and certification
Module 18: Post-Course Integration and Lifelong Learning - Setting up a personal update subscription for Splunk releases
- Joining advanced user groups and expert forums
- Accessing the private alumni network for peer support
- Receiving monthly updates on new detection techniques
- Participating in quarterly live Q&A review sessions
- Downloading expanded use case templates and cheat sheets
- Tracking your skill progression with gamified milestones
- Enrolling in advanced specialisations (UEBA, cloud threat hunting)
- Utilising progress tracking tools within the learning platform
- Leveraging your Certificate of Completion for career growth
- Writing efficient SPL (Search Processing Language) for large datasets
- Using stats, chart, and timechart for fast summarisation
- Applying early filtering with WHERE and SEARCH commands
- Minimising resource usage with subsearch optimisation
- Leveraging tstats for accelerated data model queries
- Using precomputed summaries to reduce computation time
- Monitoring search performance with Job Inspector
- Tuning timeouts and concurrency limits for enterprise loads
- Debugging slow or failing searches systematically
- Implementing query caching strategies for recurring reports
Module 8: Dashboarding and Executive Communication - Designing dashboards for technical and non-technical audiences
- Creating real-time threat landscape overviews
- Mapping detections to compliance requirements (GDPR, HIPAA, ISO 27001)
- Building board-ready risk heatmaps and KPIs
- Visualising AI model performance: precision, recall, F1 score
- Using single value, gauges, and trend indicators effectively
- Incorporating drill-downs for forensic investigation
- Embedding model confidence levels into dashboard alerts
- Sharing dashboards securely with stakeholders
- Automating PDF report generation for CISOs and auditors
Module 9: Automation and Orchestration Integration - Configuring Splunk alerts to trigger SOAR playbooks
- Sending parsed data to Phantom, Demisto, or Cortex XSOAR
- Using webhooks and REST API integrations for automation
- Automated enrichment of incidents with threat intel
- Initiating containment actions based on AI confidence scores
- Executing playbook branching logic based on severity
- Logging all automated actions for audit and review
- Building feedback loops from SOAR outcomes to model retraining
- Monitoring automation success rates and false triggers
- Managing automation safely in production environments
Module 10: Model Validation and Continuous Improvement - Measuring detection efficacy using ground truth data
- Calculating true positive, false positive, and false negative rates
- Tracking model drift over time with retraining triggers
- Updating models with new adversarial techniques
- Conducting red team collaboration for model testing
- Using controlled breach simulations to validate alerts
- Implementing A/B testing for detection rule optimisation
- Analysing operator feedback to refine scoring logic
- Automating performance reporting for ML models
- Scheduling periodic model health checks
Module 11: Real-World Threat Detection Projects - Project 1: Detecting suspicious service account activity
- Project 2: Identifying anomalous database query volumes
- Project 3: Monitoring for unauthorised cloud storage access
- Project 4: Predicting phishing campaign follow-ups
- Project 5: Clustering suspicious HR system access patterns
- Project 6: Detecting living-off-the-land binary usage
- Project 7: Identifying misconfigured S3 buckets via API logs
- Project 8: Correlating failed RDP attempts across subnets
- Project 9: Building a zero-day exploit early detection proxy
- Project 10: Analysing AV quarantine logs for evasion patterns
Module 12: Adversarial ML and Model Security - Understanding threats to machine learning systems
- Data poisoning attacks in security analytics
- Model inversion and evasion techniques
- Defending against gradient-based adversarial inputs
- Monitoring for manipulation of training data sources
- Implementing input sanitisation and validation pipelines
- Using ensemble models to increase robustness
- Logging and alerting on suspicious model access
- Securing model artifacts and version history
- Applying least privilege to MLTK workflows
Module 13: Cross-Platform AI Integration Strategies - Exporting Splunk-trained models to external systems
- Importing third-party models into Splunk via APIs
- Integrating with Python-based ML pipelines (scikit-learn, TensorFlow)
- Using Docker containers to run external AI services
- Streaming predictions from external models into Splunk
- Synchronising ground truth labels across platforms
- Centralising model governance and policy enforcement
- Monitoring hybrid AI environments for consistency
- Aligning Splunk tags with MITRE D3FEND controls
- Documenting integration architecture for audits
Module 14: Compliance and Governance of AI Systems - Meeting regulatory requirements for algorithmic transparency
- Documenting data provenance and model training lineage
- Ensuring fairness and avoiding bias in security models
- Conducting impact assessments for AI-driven decisions
- Creating model cards for internal review and approval
- Implementing model version control and rollback plans
- Encrypting sensitive training data at rest and in transit
- Managing consent and data rights in monitored environments
- Archiving model decisions for incident reconstruction
- Preparing for AI audits by internal and external assessors
Module 15: Operationalising AI at Scale - Deploying detection models across multiple Splunk instances
- Managing configurations with Splunk Deployment Server
- Using Git-based workflows for change control
- Automating deployment with Ansible and Terraform
- Rolling out updates in staged environments
- Monitoring model performance enterprise-wide
- Creating standard operating procedures for AI use cases
- Training peer analysts on new detection logic
- Establishing SOC-wide feedback mechanisms
- Scaling from pilot use cases to organisation-wide coverage
Module 16: Career Advancement and Certification Preparation - Building a professional portfolio of detection models
- Writing compelling case studies for performance reviews
- Preparing for Splunk certification exams (SPLK-3001, SPLK-3002)
- Translating project outcomes into business impact statements
- Positioning yourself as a candidate for promotion or new roles
- Networking within the Splunk and cybersecurity communities
- Contributing detection content to open-source repositories
- Presenting at internal tech talks or industry meetups
- Using your Certificate of Completion to validate expertise
- Accessing exclusive job boards and employer partnerships
Module 17: Capstone Project and Certification Submission - Selecting a high-impact, deployable use case from your environment
- Defining objectives, scope, and success criteria
- Collecting and preprocessing relevant data sources
- Training and validating an AI model using Splunk MLTK
- Configuring dynamic risk scoring and alerting
- Building a dashboard for operational visibility
- Integrating with SOAR or ticketing systems
- Documenting assumptions, limitations, and maintenance needs
- Creating a presentation deck for stakeholder review
- Submitting your project for evaluation and certification
Module 18: Post-Course Integration and Lifelong Learning - Setting up a personal update subscription for Splunk releases
- Joining advanced user groups and expert forums
- Accessing the private alumni network for peer support
- Receiving monthly updates on new detection techniques
- Participating in quarterly live Q&A review sessions
- Downloading expanded use case templates and cheat sheets
- Tracking your skill progression with gamified milestones
- Enrolling in advanced specialisations (UEBA, cloud threat hunting)
- Utilising progress tracking tools within the learning platform
- Leveraging your Certificate of Completion for career growth
- Configuring Splunk alerts to trigger SOAR playbooks
- Sending parsed data to Phantom, Demisto, or Cortex XSOAR
- Using webhooks and REST API integrations for automation
- Automated enrichment of incidents with threat intel
- Initiating containment actions based on AI confidence scores
- Executing playbook branching logic based on severity
- Logging all automated actions for audit and review
- Building feedback loops from SOAR outcomes to model retraining
- Monitoring automation success rates and false triggers
- Managing automation safely in production environments
Module 10: Model Validation and Continuous Improvement - Measuring detection efficacy using ground truth data
- Calculating true positive, false positive, and false negative rates
- Tracking model drift over time with retraining triggers
- Updating models with new adversarial techniques
- Conducting red team collaboration for model testing
- Using controlled breach simulations to validate alerts
- Implementing A/B testing for detection rule optimisation
- Analysing operator feedback to refine scoring logic
- Automating performance reporting for ML models
- Scheduling periodic model health checks
Module 11: Real-World Threat Detection Projects - Project 1: Detecting suspicious service account activity
- Project 2: Identifying anomalous database query volumes
- Project 3: Monitoring for unauthorised cloud storage access
- Project 4: Predicting phishing campaign follow-ups
- Project 5: Clustering suspicious HR system access patterns
- Project 6: Detecting living-off-the-land binary usage
- Project 7: Identifying misconfigured S3 buckets via API logs
- Project 8: Correlating failed RDP attempts across subnets
- Project 9: Building a zero-day exploit early detection proxy
- Project 10: Analysing AV quarantine logs for evasion patterns
Module 12: Adversarial ML and Model Security - Understanding threats to machine learning systems
- Data poisoning attacks in security analytics
- Model inversion and evasion techniques
- Defending against gradient-based adversarial inputs
- Monitoring for manipulation of training data sources
- Implementing input sanitisation and validation pipelines
- Using ensemble models to increase robustness
- Logging and alerting on suspicious model access
- Securing model artifacts and version history
- Applying least privilege to MLTK workflows
Module 13: Cross-Platform AI Integration Strategies - Exporting Splunk-trained models to external systems
- Importing third-party models into Splunk via APIs
- Integrating with Python-based ML pipelines (scikit-learn, TensorFlow)
- Using Docker containers to run external AI services
- Streaming predictions from external models into Splunk
- Synchronising ground truth labels across platforms
- Centralising model governance and policy enforcement
- Monitoring hybrid AI environments for consistency
- Aligning Splunk tags with MITRE D3FEND controls
- Documenting integration architecture for audits
Module 14: Compliance and Governance of AI Systems - Meeting regulatory requirements for algorithmic transparency
- Documenting data provenance and model training lineage
- Ensuring fairness and avoiding bias in security models
- Conducting impact assessments for AI-driven decisions
- Creating model cards for internal review and approval
- Implementing model version control and rollback plans
- Encrypting sensitive training data at rest and in transit
- Managing consent and data rights in monitored environments
- Archiving model decisions for incident reconstruction
- Preparing for AI audits by internal and external assessors
Module 15: Operationalising AI at Scale - Deploying detection models across multiple Splunk instances
- Managing configurations with Splunk Deployment Server
- Using Git-based workflows for change control
- Automating deployment with Ansible and Terraform
- Rolling out updates in staged environments
- Monitoring model performance enterprise-wide
- Creating standard operating procedures for AI use cases
- Training peer analysts on new detection logic
- Establishing SOC-wide feedback mechanisms
- Scaling from pilot use cases to organisation-wide coverage
Module 16: Career Advancement and Certification Preparation - Building a professional portfolio of detection models
- Writing compelling case studies for performance reviews
- Preparing for Splunk certification exams (SPLK-3001, SPLK-3002)
- Translating project outcomes into business impact statements
- Positioning yourself as a candidate for promotion or new roles
- Networking within the Splunk and cybersecurity communities
- Contributing detection content to open-source repositories
- Presenting at internal tech talks or industry meetups
- Using your Certificate of Completion to validate expertise
- Accessing exclusive job boards and employer partnerships
Module 17: Capstone Project and Certification Submission - Selecting a high-impact, deployable use case from your environment
- Defining objectives, scope, and success criteria
- Collecting and preprocessing relevant data sources
- Training and validating an AI model using Splunk MLTK
- Configuring dynamic risk scoring and alerting
- Building a dashboard for operational visibility
- Integrating with SOAR or ticketing systems
- Documenting assumptions, limitations, and maintenance needs
- Creating a presentation deck for stakeholder review
- Submitting your project for evaluation and certification
Module 18: Post-Course Integration and Lifelong Learning - Setting up a personal update subscription for Splunk releases
- Joining advanced user groups and expert forums
- Accessing the private alumni network for peer support
- Receiving monthly updates on new detection techniques
- Participating in quarterly live Q&A review sessions
- Downloading expanded use case templates and cheat sheets
- Tracking your skill progression with gamified milestones
- Enrolling in advanced specialisations (UEBA, cloud threat hunting)
- Utilising progress tracking tools within the learning platform
- Leveraging your Certificate of Completion for career growth
- Project 1: Detecting suspicious service account activity
- Project 2: Identifying anomalous database query volumes
- Project 3: Monitoring for unauthorised cloud storage access
- Project 4: Predicting phishing campaign follow-ups
- Project 5: Clustering suspicious HR system access patterns
- Project 6: Detecting living-off-the-land binary usage
- Project 7: Identifying misconfigured S3 buckets via API logs
- Project 8: Correlating failed RDP attempts across subnets
- Project 9: Building a zero-day exploit early detection proxy
- Project 10: Analysing AV quarantine logs for evasion patterns
Module 12: Adversarial ML and Model Security - Understanding threats to machine learning systems
- Data poisoning attacks in security analytics
- Model inversion and evasion techniques
- Defending against gradient-based adversarial inputs
- Monitoring for manipulation of training data sources
- Implementing input sanitisation and validation pipelines
- Using ensemble models to increase robustness
- Logging and alerting on suspicious model access
- Securing model artifacts and version history
- Applying least privilege to MLTK workflows
Module 13: Cross-Platform AI Integration Strategies - Exporting Splunk-trained models to external systems
- Importing third-party models into Splunk via APIs
- Integrating with Python-based ML pipelines (scikit-learn, TensorFlow)
- Using Docker containers to run external AI services
- Streaming predictions from external models into Splunk
- Synchronising ground truth labels across platforms
- Centralising model governance and policy enforcement
- Monitoring hybrid AI environments for consistency
- Aligning Splunk tags with MITRE D3FEND controls
- Documenting integration architecture for audits
Module 14: Compliance and Governance of AI Systems - Meeting regulatory requirements for algorithmic transparency
- Documenting data provenance and model training lineage
- Ensuring fairness and avoiding bias in security models
- Conducting impact assessments for AI-driven decisions
- Creating model cards for internal review and approval
- Implementing model version control and rollback plans
- Encrypting sensitive training data at rest and in transit
- Managing consent and data rights in monitored environments
- Archiving model decisions for incident reconstruction
- Preparing for AI audits by internal and external assessors
Module 15: Operationalising AI at Scale - Deploying detection models across multiple Splunk instances
- Managing configurations with Splunk Deployment Server
- Using Git-based workflows for change control
- Automating deployment with Ansible and Terraform
- Rolling out updates in staged environments
- Monitoring model performance enterprise-wide
- Creating standard operating procedures for AI use cases
- Training peer analysts on new detection logic
- Establishing SOC-wide feedback mechanisms
- Scaling from pilot use cases to organisation-wide coverage
Module 16: Career Advancement and Certification Preparation - Building a professional portfolio of detection models
- Writing compelling case studies for performance reviews
- Preparing for Splunk certification exams (SPLK-3001, SPLK-3002)
- Translating project outcomes into business impact statements
- Positioning yourself as a candidate for promotion or new roles
- Networking within the Splunk and cybersecurity communities
- Contributing detection content to open-source repositories
- Presenting at internal tech talks or industry meetups
- Using your Certificate of Completion to validate expertise
- Accessing exclusive job boards and employer partnerships
Module 17: Capstone Project and Certification Submission - Selecting a high-impact, deployable use case from your environment
- Defining objectives, scope, and success criteria
- Collecting and preprocessing relevant data sources
- Training and validating an AI model using Splunk MLTK
- Configuring dynamic risk scoring and alerting
- Building a dashboard for operational visibility
- Integrating with SOAR or ticketing systems
- Documenting assumptions, limitations, and maintenance needs
- Creating a presentation deck for stakeholder review
- Submitting your project for evaluation and certification
Module 18: Post-Course Integration and Lifelong Learning - Setting up a personal update subscription for Splunk releases
- Joining advanced user groups and expert forums
- Accessing the private alumni network for peer support
- Receiving monthly updates on new detection techniques
- Participating in quarterly live Q&A review sessions
- Downloading expanded use case templates and cheat sheets
- Tracking your skill progression with gamified milestones
- Enrolling in advanced specialisations (UEBA, cloud threat hunting)
- Utilising progress tracking tools within the learning platform
- Leveraging your Certificate of Completion for career growth
- Exporting Splunk-trained models to external systems
- Importing third-party models into Splunk via APIs
- Integrating with Python-based ML pipelines (scikit-learn, TensorFlow)
- Using Docker containers to run external AI services
- Streaming predictions from external models into Splunk
- Synchronising ground truth labels across platforms
- Centralising model governance and policy enforcement
- Monitoring hybrid AI environments for consistency
- Aligning Splunk tags with MITRE D3FEND controls
- Documenting integration architecture for audits
Module 14: Compliance and Governance of AI Systems - Meeting regulatory requirements for algorithmic transparency
- Documenting data provenance and model training lineage
- Ensuring fairness and avoiding bias in security models
- Conducting impact assessments for AI-driven decisions
- Creating model cards for internal review and approval
- Implementing model version control and rollback plans
- Encrypting sensitive training data at rest and in transit
- Managing consent and data rights in monitored environments
- Archiving model decisions for incident reconstruction
- Preparing for AI audits by internal and external assessors
Module 15: Operationalising AI at Scale - Deploying detection models across multiple Splunk instances
- Managing configurations with Splunk Deployment Server
- Using Git-based workflows for change control
- Automating deployment with Ansible and Terraform
- Rolling out updates in staged environments
- Monitoring model performance enterprise-wide
- Creating standard operating procedures for AI use cases
- Training peer analysts on new detection logic
- Establishing SOC-wide feedback mechanisms
- Scaling from pilot use cases to organisation-wide coverage
Module 16: Career Advancement and Certification Preparation - Building a professional portfolio of detection models
- Writing compelling case studies for performance reviews
- Preparing for Splunk certification exams (SPLK-3001, SPLK-3002)
- Translating project outcomes into business impact statements
- Positioning yourself as a candidate for promotion or new roles
- Networking within the Splunk and cybersecurity communities
- Contributing detection content to open-source repositories
- Presenting at internal tech talks or industry meetups
- Using your Certificate of Completion to validate expertise
- Accessing exclusive job boards and employer partnerships
Module 17: Capstone Project and Certification Submission - Selecting a high-impact, deployable use case from your environment
- Defining objectives, scope, and success criteria
- Collecting and preprocessing relevant data sources
- Training and validating an AI model using Splunk MLTK
- Configuring dynamic risk scoring and alerting
- Building a dashboard for operational visibility
- Integrating with SOAR or ticketing systems
- Documenting assumptions, limitations, and maintenance needs
- Creating a presentation deck for stakeholder review
- Submitting your project for evaluation and certification
Module 18: Post-Course Integration and Lifelong Learning - Setting up a personal update subscription for Splunk releases
- Joining advanced user groups and expert forums
- Accessing the private alumni network for peer support
- Receiving monthly updates on new detection techniques
- Participating in quarterly live Q&A review sessions
- Downloading expanded use case templates and cheat sheets
- Tracking your skill progression with gamified milestones
- Enrolling in advanced specialisations (UEBA, cloud threat hunting)
- Utilising progress tracking tools within the learning platform
- Leveraging your Certificate of Completion for career growth
- Deploying detection models across multiple Splunk instances
- Managing configurations with Splunk Deployment Server
- Using Git-based workflows for change control
- Automating deployment with Ansible and Terraform
- Rolling out updates in staged environments
- Monitoring model performance enterprise-wide
- Creating standard operating procedures for AI use cases
- Training peer analysts on new detection logic
- Establishing SOC-wide feedback mechanisms
- Scaling from pilot use cases to organisation-wide coverage
Module 16: Career Advancement and Certification Preparation - Building a professional portfolio of detection models
- Writing compelling case studies for performance reviews
- Preparing for Splunk certification exams (SPLK-3001, SPLK-3002)
- Translating project outcomes into business impact statements
- Positioning yourself as a candidate for promotion or new roles
- Networking within the Splunk and cybersecurity communities
- Contributing detection content to open-source repositories
- Presenting at internal tech talks or industry meetups
- Using your Certificate of Completion to validate expertise
- Accessing exclusive job boards and employer partnerships
Module 17: Capstone Project and Certification Submission - Selecting a high-impact, deployable use case from your environment
- Defining objectives, scope, and success criteria
- Collecting and preprocessing relevant data sources
- Training and validating an AI model using Splunk MLTK
- Configuring dynamic risk scoring and alerting
- Building a dashboard for operational visibility
- Integrating with SOAR or ticketing systems
- Documenting assumptions, limitations, and maintenance needs
- Creating a presentation deck for stakeholder review
- Submitting your project for evaluation and certification
Module 18: Post-Course Integration and Lifelong Learning - Setting up a personal update subscription for Splunk releases
- Joining advanced user groups and expert forums
- Accessing the private alumni network for peer support
- Receiving monthly updates on new detection techniques
- Participating in quarterly live Q&A review sessions
- Downloading expanded use case templates and cheat sheets
- Tracking your skill progression with gamified milestones
- Enrolling in advanced specialisations (UEBA, cloud threat hunting)
- Utilising progress tracking tools within the learning platform
- Leveraging your Certificate of Completion for career growth
- Selecting a high-impact, deployable use case from your environment
- Defining objectives, scope, and success criteria
- Collecting and preprocessing relevant data sources
- Training and validating an AI model using Splunk MLTK
- Configuring dynamic risk scoring and alerting
- Building a dashboard for operational visibility
- Integrating with SOAR or ticketing systems
- Documenting assumptions, limitations, and maintenance needs
- Creating a presentation deck for stakeholder review
- Submitting your project for evaluation and certification