Mastering AI-Powered Cyber Security Threat Detection
You're under pressure. Systems are scaling. Attack surfaces are expanding. The threats aren't just growing - they're evolving faster than your current tools can keep up. Every missed alert, every undetected anomaly, could be the one that triggers a breach, board scrutiny, or career fallout. Traditional security methods are no longer enough. You need faster detection, smarter responses, and predictive confidence - not just retroactive patching. The gap between reactive analysts and proactive defenders is widening. And the professionals who are closing it? They’re leveraging AI-driven threat intelligence with precision, consistency, and real-time decision-making authority. Mastering AI-Powered Cyber Security Threat Detection is your exact pathway from overwhelmed to in control. This is not theory. This is a battle-tested, implementation-ready framework that enables you to deploy AI-powered detection systems in as little as 21 days - with a board-ready threat model, measurable risk reduction, and documented ROI. Take Sarah M., Senior SOC Analyst in Frankfurt. Within four weeks of applying this curriculum, she designed an AI-based anomaly detection layer for her company’s cloud infrastructure. The system identified a lateral movement attempt that had evaded all signature-based tools - earning her a promotion and her team a $1.2M budget increase for AI integration. This isn’t just about tools or trends. It’s about positioning yourself as the go-to expert in the most critical shift in cybersecurity of the decade. You don’t need to be a data scientist. You need the right structure, the right methodology, and the right support. Here’s how this course is structured to help you get there.Course Format & Delivery Details Designed for working professionals, this self-paced program delivers immediate online access with no fixed start dates, rigid schedules, or time commitments. You progress on your own terms - whether you’re fitting this in after shifts, between meetings, or during deep focus blocks. Immediate Access, Complete Flexibility
The course is on-demand and fully accessible 24/7 from any device. With mobile-friendly design, you can study during transit, review key concepts between incidents, or revisit critical frameworks before high-stakes briefings. - Typical completion time: 4–6 weeks with 6–8 hours per week of structured engagement
- Many learners apply the first threat detection model within 10 days of enrollment
- All materials are optimised for real-world implementation, not just knowledge retention
Lifetime Access, Future-Proof Learning
You receive lifetime access to the full curriculum. As new AI models, detection techniques, and adversarial tactics emerge, the course content is updated - at no additional cost. You’re not buying a static product. You’re gaining permanent access to a living, evolving expertise platform. Expert-Led Guidance & Support
You are not alone. Enrolled learners receive direct guidance from certified AI security architects with real-world blue team, red team, and enterprise deployment experience. Support is delivered through structured feedback channels and practical implementation reviews - ensuring your use cases are technically sound and operationally viable. Official Certificate of Completion
Upon successful completion, you will receive a Certificate of Completion issued by The Art of Service - a globally recognised authority in professional cybersecurity education. This credential is shareable on LinkedIn, included in performance reviews, and cited by employers evaluating advanced threat detection expertise. Zero-Risk Enrollment, Maximum Trust
We remove all risk with a full money-back guarantee. If you follow the program and do not gain clarity, actionable skills, and confidence in deploying AI-powered detection systems, you will be refunded - no questions asked. - Pricing is straightforward with no hidden fees, subscriptions, or upsells
- Secure checkout accepts Visa, Mastercard, and PayPal
- After enrollment, you’ll receive a confirmation email, and your access details will be sent separately once your course materials are prepared
“Will This Work for Me?” – The Real Answer
You don’t need a PhD in machine learning. You don’t need prior AI experience. This works even if you’ve only worked with SIEM alerts and rule-based systems. This works even if your organisation hasn’t adopted AI yet. The curriculum starts at operational reality and builds upward - aligning technical depth with executive impact. Security Engineers in mid-tier enterprises, SOC Leads managing alert fatigue, and IT Risk Managers preparing for AI audits have all applied this framework to deliver measurable improvements - from 47% faster detection times to 90% reduction in false positives. With lifetime access, progress tracking, structured support, and a guaranteed outcome, you’re not just investing in knowledge. You’re securing a competitive, career-advancing advantage - with zero downside.
Module 1: Foundations of AI in Cyber Security - Understanding the evolution of cyber threats and the limitations of signature-based detection
- Key differences between rule-based systems and AI-driven detection models
- Real-world examples of breaches that evaded traditional tools but were detectable with AI
- Core principles of supervised and unsupervised learning in security contexts
- How AI augments human analysts rather than replaces them
- Overview of machine learning terminology for non-data scientists
- Common misconceptions about AI in cyber security and how to avoid them
- Introduction to threat intelligence integration with AI models
- Understanding data sources: logs, network flows, endpoints, cloud APIs
- Building a security data pipeline: collection, storage, and access
Module 2: Threat Detection Frameworks and Methodologies - The MITRE ATT&CK framework and its role in AI model training
- Mapping adversarial tactics to AI-detectable patterns
- Developing hypothesis-driven detection strategies
- From detection engineering to predictive analytics
- Designing detection rules using behavioural baselining
- Calculating detection efficacy: precision, recall, F1-score
- Reducing false positives through adaptive learning
- Building a threat detection backlog with prioritisation criteria
- Integrating threat hunting with AI-assisted correlation
- Creating detection playbooks for automated response triggers
Module 3: Data Preparation for AI Models - Identifying high-value data sources for anomaly detection
- Normalisation and standardisation of heterogeneous log formats
- Feature engineering for cyber security: what to extract and why
- Handling missing, incomplete, or corrupted data in real-world environments
- Time-series data processing for behavioural analysis
- Entity resolution: linking user, device, and application identities
- Creating ground truth datasets for model validation
- Labelling techniques for supervised learning in low-labelling environments
- Data privacy and compliance: GDPR, CCPA, and anonymisation strategies
- Building reusable data transformation workflows
Module 4: Machine Learning Models for Threat Detection - Selecting the right model type: decision trees, SVM, neural networks
- Clustering algorithms for unsupervised anomaly detection (K-means, DBSCAN)
- Isolation Forests: theory, implementation, and tuning
- Autoencoders for detecting rare or novel attack patterns
- Random Forests for multi-class classification of threats
- Gradient Boosting for high-precision detection in imbalanced datasets
- Recurrent Neural Networks for sequential attack pattern recognition
- Model interpretability: understanding why an alert was triggered
- Model drift: detecting and correcting performance decay over time
- Ensemble methods for combining multiple detectors into a unified system
Module 5: Real-Time Detection and Streaming Analytics - Architecting real-time detection systems using stream processing
- Apache Kafka for high-throughput security event ingestion
- Flink and Spark Streaming for windowed anomaly detection
- Event time vs. processing time in security analytics
- Reducing detection latency to sub-second levels
- Stateful processing for tracking multi-step attack sequences
- Real-time feature extraction from streaming data
- Scaling stream processors across distributed environments
- Monitoring stream health and processing backlog
- Designing fallback mechanisms for stream failures
Module 6: AI Integration with SIEM and SOAR Platforms - Integrating AI models with Splunk, QRadar, and ArcSight
- Pushing AI-generated alerts into existing case management systems
- Automating enrichment of security incidents using AI insights
- Triggering SOAR playbooks based on AI confidence scores
- Bi-directional feedback loops between AI models and SOAR actions
- Configuring alert suppression using AI risk scoring
- Building custom dashboards for AI detection performance
- Mapping AI output to SOC analyst workflows
- Deployment patterns: inline vs. offline AI modules
- Ensuring reliability and fail-safe operation in production
Module 7: Advanced Anomaly Detection Techniques - User and Entity Behaviour Analytics (UEBA) with machine learning
- Building individual user baselines using historical activity
- Detecting insider threats through subtle behavioural shifts
- Device fingerprinting and anomaly detection in IoT environments
- Network traffic anomaly detection using flow metadata
- DNS tunneling detection using entropy analysis
- Cryptomining detection through process and resource profiling
- API abuse detection using rate, sequence, and payload analysis
- Phishing detection using NLP and sender reputation models
- Dark web monitoring integration for proactive threat detection
Module 8: Model Evaluation and Performance Tuning - Establishing detection baselines before AI deployment
- Splitting data into training, validation, and test sets
- Confusion matrices and their role in tuning threshold sensitivity
- ROC curves and AUC analysis for comparing model performance
- Setting optimal thresholds for reducing false positives
- Calculating mean time to detect (MTTD) improvements
- Measuring reduction in analyst workload post-deployment
- Running controlled A/B tests of AI vs. non-AI detection
- Creating feedback loops for analyst validation of AI alerts
- Continuous monitoring of model performance and retraining cycles
Module 9: Secure Deployment and Operationalisation - Secure model hosting: containers, isolation, access controls
- Model versioning and rollback strategies
- Infrastructure as code for reproducible AI deployments
- Monitoring model inference latency and system load
- Role-based access control for AI system administration
- Encrypting model parameters and input data in transit and at rest
- Audit logging for every detection and model update
- Zero-trust integration of AI components into security architecture
- Handling adversarial attacks on AI models (evasion, poisoning)
- Red teaming your own AI detection system for resilience
Module 10: Cloud-Native AI Threat Detection - Threat detection in AWS, Azure, and GCP environments
- Analysing cloudTrail, Azure Activity Log, and Audit Logs
- Detecting misconfigurations using AI-assisted policy analysis
- Spotting credential misuse in federated identity systems
- Serverless attack detection: identifying function hijacking
- Container escape and Kubernetes API abuse detection
- Monitoring for unauthorised egress data transfers
- AI-powered detection of cryptojacking in cloud workloads
- Scaling detection models across multi-cloud environments
- Automated cost anomaly detection linked to security events
Module 11: Adversarial Machine Learning and Defence - Understanding adversarial examples in cyber security
- Poisoning attacks: how attackers corrupt training data
- Evasion techniques: manipulating inputs to bypass detection
- Model inversion attacks and privacy leakage risks
- Defensive distillation and robust model training
- Adversarial training: preparing models for attack scenarios
- Input sanitisation and anomaly rejection at inference time
- Detecting model probing and enumeration attempts
- Building resilience into AI detection pipelines
- Monitoring for indicators of adversarial model testing
Module 12: Practical Implementation Projects - Project 1: Building a login anomaly detector using SSH logs
- Feature extraction from authentication event logs
- Training a model to detect brute force and credential stuffing
- Evaluating detection accuracy across different user types
- Project 2: Detecting lateral movement in enterprise networks
- Analysing Windows event logs for SMB and WMI activity
- Clustering unusual connection patterns using DBSCAN
- Visualising detection results for SOC team review
- Project 3: Real-time phishing URL classifier
- Implementing NLP-based detection of malicious domains
- Project 4: AI-assisted insider threat scenario modelling
- Simulating data exfiltration patterns using synthetic data
- Detecting abnormal file access sequences
- Generating a board-ready threat assessment report
- Deploying a containerised model for production testing
Module 13: Governance, Ethics, and Compliance - Establishing AI governance policies for security teams
- Ensuring fairness and avoiding bias in detection models
- Audit readiness: documenting model decisions and data sources
- Regulatory requirements for AI use in financial and healthcare sectors
- Transparency and explainability requirements for AI alerts
- Handling consent and notification in UEBA systems
- Third-party AI vendor risk assessment frameworks
- Internal review boards for AI deployment approval
- Incident response planning for AI system failures
- Creating an AI ethics checklist for detection engineering
Module 14: Career Advancement and Professional Certification - Positioning your AI detection expertise in performance reviews
- Building a portfolio of implemented AI detection use cases
- Presenting technical work to non-technical stakeholders
- Preparing for interviews focused on AI and automation skills
- Networking with AI security professionals and communities
- Contributing to open-source AI security tools and frameworks
- Speaking at conferences and writing technical blog posts
- Earning recognition as a domain expert in your organisation
- Mapping skills to industry certifications and career paths
- Receiving your Certificate of Completion from The Art of Service
- Sharing your credential on LinkedIn and professional networks
- Accessing alumni resources and exclusive industry updates
- Invitations to advanced peer discussion groups
- Continued support for implementing new AI techniques
- Lifetime access to updated curriculum modules as threats evolve
- Understanding the evolution of cyber threats and the limitations of signature-based detection
- Key differences between rule-based systems and AI-driven detection models
- Real-world examples of breaches that evaded traditional tools but were detectable with AI
- Core principles of supervised and unsupervised learning in security contexts
- How AI augments human analysts rather than replaces them
- Overview of machine learning terminology for non-data scientists
- Common misconceptions about AI in cyber security and how to avoid them
- Introduction to threat intelligence integration with AI models
- Understanding data sources: logs, network flows, endpoints, cloud APIs
- Building a security data pipeline: collection, storage, and access
Module 2: Threat Detection Frameworks and Methodologies - The MITRE ATT&CK framework and its role in AI model training
- Mapping adversarial tactics to AI-detectable patterns
- Developing hypothesis-driven detection strategies
- From detection engineering to predictive analytics
- Designing detection rules using behavioural baselining
- Calculating detection efficacy: precision, recall, F1-score
- Reducing false positives through adaptive learning
- Building a threat detection backlog with prioritisation criteria
- Integrating threat hunting with AI-assisted correlation
- Creating detection playbooks for automated response triggers
Module 3: Data Preparation for AI Models - Identifying high-value data sources for anomaly detection
- Normalisation and standardisation of heterogeneous log formats
- Feature engineering for cyber security: what to extract and why
- Handling missing, incomplete, or corrupted data in real-world environments
- Time-series data processing for behavioural analysis
- Entity resolution: linking user, device, and application identities
- Creating ground truth datasets for model validation
- Labelling techniques for supervised learning in low-labelling environments
- Data privacy and compliance: GDPR, CCPA, and anonymisation strategies
- Building reusable data transformation workflows
Module 4: Machine Learning Models for Threat Detection - Selecting the right model type: decision trees, SVM, neural networks
- Clustering algorithms for unsupervised anomaly detection (K-means, DBSCAN)
- Isolation Forests: theory, implementation, and tuning
- Autoencoders for detecting rare or novel attack patterns
- Random Forests for multi-class classification of threats
- Gradient Boosting for high-precision detection in imbalanced datasets
- Recurrent Neural Networks for sequential attack pattern recognition
- Model interpretability: understanding why an alert was triggered
- Model drift: detecting and correcting performance decay over time
- Ensemble methods for combining multiple detectors into a unified system
Module 5: Real-Time Detection and Streaming Analytics - Architecting real-time detection systems using stream processing
- Apache Kafka for high-throughput security event ingestion
- Flink and Spark Streaming for windowed anomaly detection
- Event time vs. processing time in security analytics
- Reducing detection latency to sub-second levels
- Stateful processing for tracking multi-step attack sequences
- Real-time feature extraction from streaming data
- Scaling stream processors across distributed environments
- Monitoring stream health and processing backlog
- Designing fallback mechanisms for stream failures
Module 6: AI Integration with SIEM and SOAR Platforms - Integrating AI models with Splunk, QRadar, and ArcSight
- Pushing AI-generated alerts into existing case management systems
- Automating enrichment of security incidents using AI insights
- Triggering SOAR playbooks based on AI confidence scores
- Bi-directional feedback loops between AI models and SOAR actions
- Configuring alert suppression using AI risk scoring
- Building custom dashboards for AI detection performance
- Mapping AI output to SOC analyst workflows
- Deployment patterns: inline vs. offline AI modules
- Ensuring reliability and fail-safe operation in production
Module 7: Advanced Anomaly Detection Techniques - User and Entity Behaviour Analytics (UEBA) with machine learning
- Building individual user baselines using historical activity
- Detecting insider threats through subtle behavioural shifts
- Device fingerprinting and anomaly detection in IoT environments
- Network traffic anomaly detection using flow metadata
- DNS tunneling detection using entropy analysis
- Cryptomining detection through process and resource profiling
- API abuse detection using rate, sequence, and payload analysis
- Phishing detection using NLP and sender reputation models
- Dark web monitoring integration for proactive threat detection
Module 8: Model Evaluation and Performance Tuning - Establishing detection baselines before AI deployment
- Splitting data into training, validation, and test sets
- Confusion matrices and their role in tuning threshold sensitivity
- ROC curves and AUC analysis for comparing model performance
- Setting optimal thresholds for reducing false positives
- Calculating mean time to detect (MTTD) improvements
- Measuring reduction in analyst workload post-deployment
- Running controlled A/B tests of AI vs. non-AI detection
- Creating feedback loops for analyst validation of AI alerts
- Continuous monitoring of model performance and retraining cycles
Module 9: Secure Deployment and Operationalisation - Secure model hosting: containers, isolation, access controls
- Model versioning and rollback strategies
- Infrastructure as code for reproducible AI deployments
- Monitoring model inference latency and system load
- Role-based access control for AI system administration
- Encrypting model parameters and input data in transit and at rest
- Audit logging for every detection and model update
- Zero-trust integration of AI components into security architecture
- Handling adversarial attacks on AI models (evasion, poisoning)
- Red teaming your own AI detection system for resilience
Module 10: Cloud-Native AI Threat Detection - Threat detection in AWS, Azure, and GCP environments
- Analysing cloudTrail, Azure Activity Log, and Audit Logs
- Detecting misconfigurations using AI-assisted policy analysis
- Spotting credential misuse in federated identity systems
- Serverless attack detection: identifying function hijacking
- Container escape and Kubernetes API abuse detection
- Monitoring for unauthorised egress data transfers
- AI-powered detection of cryptojacking in cloud workloads
- Scaling detection models across multi-cloud environments
- Automated cost anomaly detection linked to security events
Module 11: Adversarial Machine Learning and Defence - Understanding adversarial examples in cyber security
- Poisoning attacks: how attackers corrupt training data
- Evasion techniques: manipulating inputs to bypass detection
- Model inversion attacks and privacy leakage risks
- Defensive distillation and robust model training
- Adversarial training: preparing models for attack scenarios
- Input sanitisation and anomaly rejection at inference time
- Detecting model probing and enumeration attempts
- Building resilience into AI detection pipelines
- Monitoring for indicators of adversarial model testing
Module 12: Practical Implementation Projects - Project 1: Building a login anomaly detector using SSH logs
- Feature extraction from authentication event logs
- Training a model to detect brute force and credential stuffing
- Evaluating detection accuracy across different user types
- Project 2: Detecting lateral movement in enterprise networks
- Analysing Windows event logs for SMB and WMI activity
- Clustering unusual connection patterns using DBSCAN
- Visualising detection results for SOC team review
- Project 3: Real-time phishing URL classifier
- Implementing NLP-based detection of malicious domains
- Project 4: AI-assisted insider threat scenario modelling
- Simulating data exfiltration patterns using synthetic data
- Detecting abnormal file access sequences
- Generating a board-ready threat assessment report
- Deploying a containerised model for production testing
Module 13: Governance, Ethics, and Compliance - Establishing AI governance policies for security teams
- Ensuring fairness and avoiding bias in detection models
- Audit readiness: documenting model decisions and data sources
- Regulatory requirements for AI use in financial and healthcare sectors
- Transparency and explainability requirements for AI alerts
- Handling consent and notification in UEBA systems
- Third-party AI vendor risk assessment frameworks
- Internal review boards for AI deployment approval
- Incident response planning for AI system failures
- Creating an AI ethics checklist for detection engineering
Module 14: Career Advancement and Professional Certification - Positioning your AI detection expertise in performance reviews
- Building a portfolio of implemented AI detection use cases
- Presenting technical work to non-technical stakeholders
- Preparing for interviews focused on AI and automation skills
- Networking with AI security professionals and communities
- Contributing to open-source AI security tools and frameworks
- Speaking at conferences and writing technical blog posts
- Earning recognition as a domain expert in your organisation
- Mapping skills to industry certifications and career paths
- Receiving your Certificate of Completion from The Art of Service
- Sharing your credential on LinkedIn and professional networks
- Accessing alumni resources and exclusive industry updates
- Invitations to advanced peer discussion groups
- Continued support for implementing new AI techniques
- Lifetime access to updated curriculum modules as threats evolve
- Identifying high-value data sources for anomaly detection
- Normalisation and standardisation of heterogeneous log formats
- Feature engineering for cyber security: what to extract and why
- Handling missing, incomplete, or corrupted data in real-world environments
- Time-series data processing for behavioural analysis
- Entity resolution: linking user, device, and application identities
- Creating ground truth datasets for model validation
- Labelling techniques for supervised learning in low-labelling environments
- Data privacy and compliance: GDPR, CCPA, and anonymisation strategies
- Building reusable data transformation workflows
Module 4: Machine Learning Models for Threat Detection - Selecting the right model type: decision trees, SVM, neural networks
- Clustering algorithms for unsupervised anomaly detection (K-means, DBSCAN)
- Isolation Forests: theory, implementation, and tuning
- Autoencoders for detecting rare or novel attack patterns
- Random Forests for multi-class classification of threats
- Gradient Boosting for high-precision detection in imbalanced datasets
- Recurrent Neural Networks for sequential attack pattern recognition
- Model interpretability: understanding why an alert was triggered
- Model drift: detecting and correcting performance decay over time
- Ensemble methods for combining multiple detectors into a unified system
Module 5: Real-Time Detection and Streaming Analytics - Architecting real-time detection systems using stream processing
- Apache Kafka for high-throughput security event ingestion
- Flink and Spark Streaming for windowed anomaly detection
- Event time vs. processing time in security analytics
- Reducing detection latency to sub-second levels
- Stateful processing for tracking multi-step attack sequences
- Real-time feature extraction from streaming data
- Scaling stream processors across distributed environments
- Monitoring stream health and processing backlog
- Designing fallback mechanisms for stream failures
Module 6: AI Integration with SIEM and SOAR Platforms - Integrating AI models with Splunk, QRadar, and ArcSight
- Pushing AI-generated alerts into existing case management systems
- Automating enrichment of security incidents using AI insights
- Triggering SOAR playbooks based on AI confidence scores
- Bi-directional feedback loops between AI models and SOAR actions
- Configuring alert suppression using AI risk scoring
- Building custom dashboards for AI detection performance
- Mapping AI output to SOC analyst workflows
- Deployment patterns: inline vs. offline AI modules
- Ensuring reliability and fail-safe operation in production
Module 7: Advanced Anomaly Detection Techniques - User and Entity Behaviour Analytics (UEBA) with machine learning
- Building individual user baselines using historical activity
- Detecting insider threats through subtle behavioural shifts
- Device fingerprinting and anomaly detection in IoT environments
- Network traffic anomaly detection using flow metadata
- DNS tunneling detection using entropy analysis
- Cryptomining detection through process and resource profiling
- API abuse detection using rate, sequence, and payload analysis
- Phishing detection using NLP and sender reputation models
- Dark web monitoring integration for proactive threat detection
Module 8: Model Evaluation and Performance Tuning - Establishing detection baselines before AI deployment
- Splitting data into training, validation, and test sets
- Confusion matrices and their role in tuning threshold sensitivity
- ROC curves and AUC analysis for comparing model performance
- Setting optimal thresholds for reducing false positives
- Calculating mean time to detect (MTTD) improvements
- Measuring reduction in analyst workload post-deployment
- Running controlled A/B tests of AI vs. non-AI detection
- Creating feedback loops for analyst validation of AI alerts
- Continuous monitoring of model performance and retraining cycles
Module 9: Secure Deployment and Operationalisation - Secure model hosting: containers, isolation, access controls
- Model versioning and rollback strategies
- Infrastructure as code for reproducible AI deployments
- Monitoring model inference latency and system load
- Role-based access control for AI system administration
- Encrypting model parameters and input data in transit and at rest
- Audit logging for every detection and model update
- Zero-trust integration of AI components into security architecture
- Handling adversarial attacks on AI models (evasion, poisoning)
- Red teaming your own AI detection system for resilience
Module 10: Cloud-Native AI Threat Detection - Threat detection in AWS, Azure, and GCP environments
- Analysing cloudTrail, Azure Activity Log, and Audit Logs
- Detecting misconfigurations using AI-assisted policy analysis
- Spotting credential misuse in federated identity systems
- Serverless attack detection: identifying function hijacking
- Container escape and Kubernetes API abuse detection
- Monitoring for unauthorised egress data transfers
- AI-powered detection of cryptojacking in cloud workloads
- Scaling detection models across multi-cloud environments
- Automated cost anomaly detection linked to security events
Module 11: Adversarial Machine Learning and Defence - Understanding adversarial examples in cyber security
- Poisoning attacks: how attackers corrupt training data
- Evasion techniques: manipulating inputs to bypass detection
- Model inversion attacks and privacy leakage risks
- Defensive distillation and robust model training
- Adversarial training: preparing models for attack scenarios
- Input sanitisation and anomaly rejection at inference time
- Detecting model probing and enumeration attempts
- Building resilience into AI detection pipelines
- Monitoring for indicators of adversarial model testing
Module 12: Practical Implementation Projects - Project 1: Building a login anomaly detector using SSH logs
- Feature extraction from authentication event logs
- Training a model to detect brute force and credential stuffing
- Evaluating detection accuracy across different user types
- Project 2: Detecting lateral movement in enterprise networks
- Analysing Windows event logs for SMB and WMI activity
- Clustering unusual connection patterns using DBSCAN
- Visualising detection results for SOC team review
- Project 3: Real-time phishing URL classifier
- Implementing NLP-based detection of malicious domains
- Project 4: AI-assisted insider threat scenario modelling
- Simulating data exfiltration patterns using synthetic data
- Detecting abnormal file access sequences
- Generating a board-ready threat assessment report
- Deploying a containerised model for production testing
Module 13: Governance, Ethics, and Compliance - Establishing AI governance policies for security teams
- Ensuring fairness and avoiding bias in detection models
- Audit readiness: documenting model decisions and data sources
- Regulatory requirements for AI use in financial and healthcare sectors
- Transparency and explainability requirements for AI alerts
- Handling consent and notification in UEBA systems
- Third-party AI vendor risk assessment frameworks
- Internal review boards for AI deployment approval
- Incident response planning for AI system failures
- Creating an AI ethics checklist for detection engineering
Module 14: Career Advancement and Professional Certification - Positioning your AI detection expertise in performance reviews
- Building a portfolio of implemented AI detection use cases
- Presenting technical work to non-technical stakeholders
- Preparing for interviews focused on AI and automation skills
- Networking with AI security professionals and communities
- Contributing to open-source AI security tools and frameworks
- Speaking at conferences and writing technical blog posts
- Earning recognition as a domain expert in your organisation
- Mapping skills to industry certifications and career paths
- Receiving your Certificate of Completion from The Art of Service
- Sharing your credential on LinkedIn and professional networks
- Accessing alumni resources and exclusive industry updates
- Invitations to advanced peer discussion groups
- Continued support for implementing new AI techniques
- Lifetime access to updated curriculum modules as threats evolve
- Architecting real-time detection systems using stream processing
- Apache Kafka for high-throughput security event ingestion
- Flink and Spark Streaming for windowed anomaly detection
- Event time vs. processing time in security analytics
- Reducing detection latency to sub-second levels
- Stateful processing for tracking multi-step attack sequences
- Real-time feature extraction from streaming data
- Scaling stream processors across distributed environments
- Monitoring stream health and processing backlog
- Designing fallback mechanisms for stream failures
Module 6: AI Integration with SIEM and SOAR Platforms - Integrating AI models with Splunk, QRadar, and ArcSight
- Pushing AI-generated alerts into existing case management systems
- Automating enrichment of security incidents using AI insights
- Triggering SOAR playbooks based on AI confidence scores
- Bi-directional feedback loops between AI models and SOAR actions
- Configuring alert suppression using AI risk scoring
- Building custom dashboards for AI detection performance
- Mapping AI output to SOC analyst workflows
- Deployment patterns: inline vs. offline AI modules
- Ensuring reliability and fail-safe operation in production
Module 7: Advanced Anomaly Detection Techniques - User and Entity Behaviour Analytics (UEBA) with machine learning
- Building individual user baselines using historical activity
- Detecting insider threats through subtle behavioural shifts
- Device fingerprinting and anomaly detection in IoT environments
- Network traffic anomaly detection using flow metadata
- DNS tunneling detection using entropy analysis
- Cryptomining detection through process and resource profiling
- API abuse detection using rate, sequence, and payload analysis
- Phishing detection using NLP and sender reputation models
- Dark web monitoring integration for proactive threat detection
Module 8: Model Evaluation and Performance Tuning - Establishing detection baselines before AI deployment
- Splitting data into training, validation, and test sets
- Confusion matrices and their role in tuning threshold sensitivity
- ROC curves and AUC analysis for comparing model performance
- Setting optimal thresholds for reducing false positives
- Calculating mean time to detect (MTTD) improvements
- Measuring reduction in analyst workload post-deployment
- Running controlled A/B tests of AI vs. non-AI detection
- Creating feedback loops for analyst validation of AI alerts
- Continuous monitoring of model performance and retraining cycles
Module 9: Secure Deployment and Operationalisation - Secure model hosting: containers, isolation, access controls
- Model versioning and rollback strategies
- Infrastructure as code for reproducible AI deployments
- Monitoring model inference latency and system load
- Role-based access control for AI system administration
- Encrypting model parameters and input data in transit and at rest
- Audit logging for every detection and model update
- Zero-trust integration of AI components into security architecture
- Handling adversarial attacks on AI models (evasion, poisoning)
- Red teaming your own AI detection system for resilience
Module 10: Cloud-Native AI Threat Detection - Threat detection in AWS, Azure, and GCP environments
- Analysing cloudTrail, Azure Activity Log, and Audit Logs
- Detecting misconfigurations using AI-assisted policy analysis
- Spotting credential misuse in federated identity systems
- Serverless attack detection: identifying function hijacking
- Container escape and Kubernetes API abuse detection
- Monitoring for unauthorised egress data transfers
- AI-powered detection of cryptojacking in cloud workloads
- Scaling detection models across multi-cloud environments
- Automated cost anomaly detection linked to security events
Module 11: Adversarial Machine Learning and Defence - Understanding adversarial examples in cyber security
- Poisoning attacks: how attackers corrupt training data
- Evasion techniques: manipulating inputs to bypass detection
- Model inversion attacks and privacy leakage risks
- Defensive distillation and robust model training
- Adversarial training: preparing models for attack scenarios
- Input sanitisation and anomaly rejection at inference time
- Detecting model probing and enumeration attempts
- Building resilience into AI detection pipelines
- Monitoring for indicators of adversarial model testing
Module 12: Practical Implementation Projects - Project 1: Building a login anomaly detector using SSH logs
- Feature extraction from authentication event logs
- Training a model to detect brute force and credential stuffing
- Evaluating detection accuracy across different user types
- Project 2: Detecting lateral movement in enterprise networks
- Analysing Windows event logs for SMB and WMI activity
- Clustering unusual connection patterns using DBSCAN
- Visualising detection results for SOC team review
- Project 3: Real-time phishing URL classifier
- Implementing NLP-based detection of malicious domains
- Project 4: AI-assisted insider threat scenario modelling
- Simulating data exfiltration patterns using synthetic data
- Detecting abnormal file access sequences
- Generating a board-ready threat assessment report
- Deploying a containerised model for production testing
Module 13: Governance, Ethics, and Compliance - Establishing AI governance policies for security teams
- Ensuring fairness and avoiding bias in detection models
- Audit readiness: documenting model decisions and data sources
- Regulatory requirements for AI use in financial and healthcare sectors
- Transparency and explainability requirements for AI alerts
- Handling consent and notification in UEBA systems
- Third-party AI vendor risk assessment frameworks
- Internal review boards for AI deployment approval
- Incident response planning for AI system failures
- Creating an AI ethics checklist for detection engineering
Module 14: Career Advancement and Professional Certification - Positioning your AI detection expertise in performance reviews
- Building a portfolio of implemented AI detection use cases
- Presenting technical work to non-technical stakeholders
- Preparing for interviews focused on AI and automation skills
- Networking with AI security professionals and communities
- Contributing to open-source AI security tools and frameworks
- Speaking at conferences and writing technical blog posts
- Earning recognition as a domain expert in your organisation
- Mapping skills to industry certifications and career paths
- Receiving your Certificate of Completion from The Art of Service
- Sharing your credential on LinkedIn and professional networks
- Accessing alumni resources and exclusive industry updates
- Invitations to advanced peer discussion groups
- Continued support for implementing new AI techniques
- Lifetime access to updated curriculum modules as threats evolve
- User and Entity Behaviour Analytics (UEBA) with machine learning
- Building individual user baselines using historical activity
- Detecting insider threats through subtle behavioural shifts
- Device fingerprinting and anomaly detection in IoT environments
- Network traffic anomaly detection using flow metadata
- DNS tunneling detection using entropy analysis
- Cryptomining detection through process and resource profiling
- API abuse detection using rate, sequence, and payload analysis
- Phishing detection using NLP and sender reputation models
- Dark web monitoring integration for proactive threat detection
Module 8: Model Evaluation and Performance Tuning - Establishing detection baselines before AI deployment
- Splitting data into training, validation, and test sets
- Confusion matrices and their role in tuning threshold sensitivity
- ROC curves and AUC analysis for comparing model performance
- Setting optimal thresholds for reducing false positives
- Calculating mean time to detect (MTTD) improvements
- Measuring reduction in analyst workload post-deployment
- Running controlled A/B tests of AI vs. non-AI detection
- Creating feedback loops for analyst validation of AI alerts
- Continuous monitoring of model performance and retraining cycles
Module 9: Secure Deployment and Operationalisation - Secure model hosting: containers, isolation, access controls
- Model versioning and rollback strategies
- Infrastructure as code for reproducible AI deployments
- Monitoring model inference latency and system load
- Role-based access control for AI system administration
- Encrypting model parameters and input data in transit and at rest
- Audit logging for every detection and model update
- Zero-trust integration of AI components into security architecture
- Handling adversarial attacks on AI models (evasion, poisoning)
- Red teaming your own AI detection system for resilience
Module 10: Cloud-Native AI Threat Detection - Threat detection in AWS, Azure, and GCP environments
- Analysing cloudTrail, Azure Activity Log, and Audit Logs
- Detecting misconfigurations using AI-assisted policy analysis
- Spotting credential misuse in federated identity systems
- Serverless attack detection: identifying function hijacking
- Container escape and Kubernetes API abuse detection
- Monitoring for unauthorised egress data transfers
- AI-powered detection of cryptojacking in cloud workloads
- Scaling detection models across multi-cloud environments
- Automated cost anomaly detection linked to security events
Module 11: Adversarial Machine Learning and Defence - Understanding adversarial examples in cyber security
- Poisoning attacks: how attackers corrupt training data
- Evasion techniques: manipulating inputs to bypass detection
- Model inversion attacks and privacy leakage risks
- Defensive distillation and robust model training
- Adversarial training: preparing models for attack scenarios
- Input sanitisation and anomaly rejection at inference time
- Detecting model probing and enumeration attempts
- Building resilience into AI detection pipelines
- Monitoring for indicators of adversarial model testing
Module 12: Practical Implementation Projects - Project 1: Building a login anomaly detector using SSH logs
- Feature extraction from authentication event logs
- Training a model to detect brute force and credential stuffing
- Evaluating detection accuracy across different user types
- Project 2: Detecting lateral movement in enterprise networks
- Analysing Windows event logs for SMB and WMI activity
- Clustering unusual connection patterns using DBSCAN
- Visualising detection results for SOC team review
- Project 3: Real-time phishing URL classifier
- Implementing NLP-based detection of malicious domains
- Project 4: AI-assisted insider threat scenario modelling
- Simulating data exfiltration patterns using synthetic data
- Detecting abnormal file access sequences
- Generating a board-ready threat assessment report
- Deploying a containerised model for production testing
Module 13: Governance, Ethics, and Compliance - Establishing AI governance policies for security teams
- Ensuring fairness and avoiding bias in detection models
- Audit readiness: documenting model decisions and data sources
- Regulatory requirements for AI use in financial and healthcare sectors
- Transparency and explainability requirements for AI alerts
- Handling consent and notification in UEBA systems
- Third-party AI vendor risk assessment frameworks
- Internal review boards for AI deployment approval
- Incident response planning for AI system failures
- Creating an AI ethics checklist for detection engineering
Module 14: Career Advancement and Professional Certification - Positioning your AI detection expertise in performance reviews
- Building a portfolio of implemented AI detection use cases
- Presenting technical work to non-technical stakeholders
- Preparing for interviews focused on AI and automation skills
- Networking with AI security professionals and communities
- Contributing to open-source AI security tools and frameworks
- Speaking at conferences and writing technical blog posts
- Earning recognition as a domain expert in your organisation
- Mapping skills to industry certifications and career paths
- Receiving your Certificate of Completion from The Art of Service
- Sharing your credential on LinkedIn and professional networks
- Accessing alumni resources and exclusive industry updates
- Invitations to advanced peer discussion groups
- Continued support for implementing new AI techniques
- Lifetime access to updated curriculum modules as threats evolve
- Secure model hosting: containers, isolation, access controls
- Model versioning and rollback strategies
- Infrastructure as code for reproducible AI deployments
- Monitoring model inference latency and system load
- Role-based access control for AI system administration
- Encrypting model parameters and input data in transit and at rest
- Audit logging for every detection and model update
- Zero-trust integration of AI components into security architecture
- Handling adversarial attacks on AI models (evasion, poisoning)
- Red teaming your own AI detection system for resilience
Module 10: Cloud-Native AI Threat Detection - Threat detection in AWS, Azure, and GCP environments
- Analysing cloudTrail, Azure Activity Log, and Audit Logs
- Detecting misconfigurations using AI-assisted policy analysis
- Spotting credential misuse in federated identity systems
- Serverless attack detection: identifying function hijacking
- Container escape and Kubernetes API abuse detection
- Monitoring for unauthorised egress data transfers
- AI-powered detection of cryptojacking in cloud workloads
- Scaling detection models across multi-cloud environments
- Automated cost anomaly detection linked to security events
Module 11: Adversarial Machine Learning and Defence - Understanding adversarial examples in cyber security
- Poisoning attacks: how attackers corrupt training data
- Evasion techniques: manipulating inputs to bypass detection
- Model inversion attacks and privacy leakage risks
- Defensive distillation and robust model training
- Adversarial training: preparing models for attack scenarios
- Input sanitisation and anomaly rejection at inference time
- Detecting model probing and enumeration attempts
- Building resilience into AI detection pipelines
- Monitoring for indicators of adversarial model testing
Module 12: Practical Implementation Projects - Project 1: Building a login anomaly detector using SSH logs
- Feature extraction from authentication event logs
- Training a model to detect brute force and credential stuffing
- Evaluating detection accuracy across different user types
- Project 2: Detecting lateral movement in enterprise networks
- Analysing Windows event logs for SMB and WMI activity
- Clustering unusual connection patterns using DBSCAN
- Visualising detection results for SOC team review
- Project 3: Real-time phishing URL classifier
- Implementing NLP-based detection of malicious domains
- Project 4: AI-assisted insider threat scenario modelling
- Simulating data exfiltration patterns using synthetic data
- Detecting abnormal file access sequences
- Generating a board-ready threat assessment report
- Deploying a containerised model for production testing
Module 13: Governance, Ethics, and Compliance - Establishing AI governance policies for security teams
- Ensuring fairness and avoiding bias in detection models
- Audit readiness: documenting model decisions and data sources
- Regulatory requirements for AI use in financial and healthcare sectors
- Transparency and explainability requirements for AI alerts
- Handling consent and notification in UEBA systems
- Third-party AI vendor risk assessment frameworks
- Internal review boards for AI deployment approval
- Incident response planning for AI system failures
- Creating an AI ethics checklist for detection engineering
Module 14: Career Advancement and Professional Certification - Positioning your AI detection expertise in performance reviews
- Building a portfolio of implemented AI detection use cases
- Presenting technical work to non-technical stakeholders
- Preparing for interviews focused on AI and automation skills
- Networking with AI security professionals and communities
- Contributing to open-source AI security tools and frameworks
- Speaking at conferences and writing technical blog posts
- Earning recognition as a domain expert in your organisation
- Mapping skills to industry certifications and career paths
- Receiving your Certificate of Completion from The Art of Service
- Sharing your credential on LinkedIn and professional networks
- Accessing alumni resources and exclusive industry updates
- Invitations to advanced peer discussion groups
- Continued support for implementing new AI techniques
- Lifetime access to updated curriculum modules as threats evolve
- Understanding adversarial examples in cyber security
- Poisoning attacks: how attackers corrupt training data
- Evasion techniques: manipulating inputs to bypass detection
- Model inversion attacks and privacy leakage risks
- Defensive distillation and robust model training
- Adversarial training: preparing models for attack scenarios
- Input sanitisation and anomaly rejection at inference time
- Detecting model probing and enumeration attempts
- Building resilience into AI detection pipelines
- Monitoring for indicators of adversarial model testing
Module 12: Practical Implementation Projects - Project 1: Building a login anomaly detector using SSH logs
- Feature extraction from authentication event logs
- Training a model to detect brute force and credential stuffing
- Evaluating detection accuracy across different user types
- Project 2: Detecting lateral movement in enterprise networks
- Analysing Windows event logs for SMB and WMI activity
- Clustering unusual connection patterns using DBSCAN
- Visualising detection results for SOC team review
- Project 3: Real-time phishing URL classifier
- Implementing NLP-based detection of malicious domains
- Project 4: AI-assisted insider threat scenario modelling
- Simulating data exfiltration patterns using synthetic data
- Detecting abnormal file access sequences
- Generating a board-ready threat assessment report
- Deploying a containerised model for production testing
Module 13: Governance, Ethics, and Compliance - Establishing AI governance policies for security teams
- Ensuring fairness and avoiding bias in detection models
- Audit readiness: documenting model decisions and data sources
- Regulatory requirements for AI use in financial and healthcare sectors
- Transparency and explainability requirements for AI alerts
- Handling consent and notification in UEBA systems
- Third-party AI vendor risk assessment frameworks
- Internal review boards for AI deployment approval
- Incident response planning for AI system failures
- Creating an AI ethics checklist for detection engineering
Module 14: Career Advancement and Professional Certification - Positioning your AI detection expertise in performance reviews
- Building a portfolio of implemented AI detection use cases
- Presenting technical work to non-technical stakeholders
- Preparing for interviews focused on AI and automation skills
- Networking with AI security professionals and communities
- Contributing to open-source AI security tools and frameworks
- Speaking at conferences and writing technical blog posts
- Earning recognition as a domain expert in your organisation
- Mapping skills to industry certifications and career paths
- Receiving your Certificate of Completion from The Art of Service
- Sharing your credential on LinkedIn and professional networks
- Accessing alumni resources and exclusive industry updates
- Invitations to advanced peer discussion groups
- Continued support for implementing new AI techniques
- Lifetime access to updated curriculum modules as threats evolve
- Establishing AI governance policies for security teams
- Ensuring fairness and avoiding bias in detection models
- Audit readiness: documenting model decisions and data sources
- Regulatory requirements for AI use in financial and healthcare sectors
- Transparency and explainability requirements for AI alerts
- Handling consent and notification in UEBA systems
- Third-party AI vendor risk assessment frameworks
- Internal review boards for AI deployment approval
- Incident response planning for AI system failures
- Creating an AI ethics checklist for detection engineering