Mastering Cyber Threat Hunting with AI-Powered Detection
Your organization is under constant siege. Threat actors move faster, exploit blind spots, and evolve quicker than ever. Even with layered defenses, you know the truth: breaches are inevitable - and undetected threats are already inside. The pressure to find them before they detonate is relentless. You're expected to stay ahead, but traditional tools flag noise, not needles in haystacks. You're drowning in alerts, not insights. Without a proactive threat hunting strategy powered by intelligent automation, you’re reacting - not preventing. That uncertainty puts your job, your budget, and your team’s credibility at risk. Mastering Cyber Threat Hunting with AI-Powered Detection isn’t another theory course. It’s the battle-tested system that shifts you from reactive responder to predictive hunter. This program equips you to build and deploy AI-enhanced detection workflows that uncover stealthy threats in real networks - exactly as elite red and blue teams do. One graduate, Sarah M., Senior SOC Analyst at a global financial firm, used this methodology to redesign their detection pipeline and identified a previously undetected lateral movement sequence - reducing mean time to detection from 47 days to under 3 hours. Her initiative earned her a promotion and her team a $750K budget increase for AI integration. This is your blueprint to go from uncertain and overwhelmed to confident, funded, and future-proof. You’ll leave with a fully documented, board-ready AI threat hunting framework - deployable within 30 days, validated by real techniques, and aligned with global security standards. Here’s how this course is structured to help you get there.Course Format & Delivery Details Designed for security professionals who operate in high-stakes, fast-moving environments, Mastering Cyber Threat Hunting with AI-Powered Detection provides a no-fluff, high-precision learning experience built for results, not just completion. Self-Paced, On-Demand, Always Accessible
This course is self-paced with on-demand access. There are no fixed schedules or live sessions. You determine when and where you learn. Typical completion time is 12 to 18 hours, but many practitioners report implementing core detection models within the first 48 hours of enrollment. You’ll start seeing actionable improvements in your threat detection accuracy within the first module - often after just one focused session. The curriculum is structured to deliver immediate value at every stage, not just at the end. Lifetime Access, Zero Expiry
Enroll once and gain lifetime access to all course materials. You’ll also receive all future updates - including new detection logic, evolving AI models, and updated tool integrations - at no additional cost. As the threat landscape shifts, your knowledge stays current. Access is 24/7 and fully mobile-friendly. Whether you’re on-site at a client, traveling, or working night shifts, you can engage with the material securely from any device with a browser. Expert-Led Guidance & Support
You’re not learning in isolation. Each module includes direct access to expert-curated guidance, annotated analysis templates, and evaluation criteria used by top-tier security teams. You’ll also receive structured feedback pathways to validate your implementation approach. Instructor insights are embedded directly into frameworks, ensuring you interpret AI outputs correctly, avoid common misconfigurations, and build detection logic grounded in real adversary behavior. Certificate of Completion - Globally Recognized Credential
Upon successful completion, you’ll earn a verified Certificate of Completion issued by The Art of Service. This credential is recognized across industries and jurisdictions, signaling to employers, auditors, and leadership that you possess advanced, actionable expertise in AI-driven cybersecurity operations. The certificate includes a unique verification ID, links to the official registry, and is formatted for immediate integration into LinkedIn profiles, résumés, and internal promotion dossiers. Transparent Pricing, No Hidden Fees
The full investment is straightforward and includes every component: all modules, templates, frameworks, detection playbooks, and the issued certificate. There are no upsells, no subscription traps, and no recurring charges. Payment is accepted via Visa, Mastercard, and PayPal. All transactions are processed through a PCI-compliant gateway with end-to-end encryption, ensuring complete financial security. Zero-Risk Enrollment: Satisfied or Refunded
We stand behind the results. If you complete the first three modules and do not find measurable value in the detection frameworks or practical utility of the AI integration strategies, simply request a full refund. No questions, no delays. Your access remains active throughout the refund period, so you can continue learning while you evaluate - because we’re confident you won’t want to leave. Immediate Post-Enrollment Process
After enrollment, you’ll receive a confirmation email. Your access details, including login instructions and resource links, will be sent separately once your course materials are prepared. This ensures all components are correctly configured and ready for your secure, uninterrupted use. “Will This Work for Me?” - Addressing the Real Concern
This course works even if you’re not a data scientist, have limited AI experience, or work in a resource-constrained SOC. The frameworks are designed to be modular, language-agnostic, and integrable with existing SIEM, EDR, and XDR platforms - no PhD required. Multiple graduates from mid-tier enterprises, government contractors, and MSPs have successfully deployed these models using existing tooling - including Splunk, Sentinel, Elastic, and open-source stacks. The AI components focus on practical pattern recognition, not complex math. A network security engineer with only six months of hunting experience reported reducing false positives by 68% within three weeks of applying the anomaly scoring model taught in Module 5. That’s the real-world ROI this course delivers. You’re protected by design: clear structure, step-by-step blueprints, risk-reversal guarantees, and real techniques used in modern SOCs. This isn’t speculation - it’s execution, refined and proven.
Module 1: Foundations of Proactive Threat Hunting - Understanding the limitations of reactive security models
- Defining threat hunting: hypothesis-driven vs data-driven approaches
- The kill chain vs MITRE ATT&CK framework evolution
- Core principles of adversary emulation and behavioral modeling
- Mapping enterprise assets to attack surfaces
- Establishing baseline network and user behavior
- Identifying high-value targets within hybrid environments
- Creating asset criticality and exposure matrices
- Introducing the concept of hunting hypotheses
- Developing threat-informed hunting strategies
- Quantifying risk exposure using likelihood and impact scoring
- Aligning threat hunting with organizational risk posture
- Setting clear success metrics for hunting operations
- Integrating threat hunting into incident response workflows
- Building your first simple hunting hypothesis statement
Module 2: AI in Cybersecurity - Principles, Myths, and Realities - Differentiating AI, ML, and automation in security contexts
- Understanding supervised vs unsupervised learning applications
- Debunking the myth that AI replaces human analysts
- How AI augments decision-making speed and accuracy
- Core machine learning concepts: features, labels, models, and inference
- Temporal analysis and session-based anomaly detection
- Clustering techniques for identifying unknown threat patterns
- Using dimensionality reduction to simplify complex logs
- Evaluating model performance: precision, recall, F1 score
- Avoiding overfitting and false confidence in AI outputs
- Interpreting confidence scores and probability thresholds
- Understanding model drift and concept shift in live environments
- Ensuring AI systems remain aligned with evolving tactics
- Building trust in AI through explainable detection logic
- Integrating AI insights with analyst judgment and context
Module 3: Building Your AI-Powered Detection Framework - Designing a modular, reusable detection framework architecture
- Establishing data ingestion standards across sources
- Normalizing logs from firewalls, endpoints, cloud, and identity systems
- Defining atomic detection units and chained logic
- Developing scoring mechanisms for risk prioritization
- Creating weighted alert escalation tiers
- Mapping detections to MITRE ATT&CK techniques and sub-techniques
- Integrating ATT&CK Navigator for visual mapping
- Using YAML for structured detection rule definition
- Validating detection logic against real-world scenarios
- Version controlling rules with Git-based workflows
- Automating rule testing with synthetic attack simulations
- Creating detection playbooks for common adversary behaviors
- Defining false positive mitigation strategies
- Building feedback loops to refine detection performance
Module 4: Data Engineering for Threat Detection - Identifying high-signal data sources across enterprise layers
- Collecting and enriching Windows Event Logs (4624, 4688, 4670, etc.)
- Extracting actionable telemetry from EDR platforms
- Processing cloud audit logs from AWS CloudTrail, Azure Activity Log
- Harvesting PowerShell command-line and script block logging
- Using Sysmon effectively without performance degradation
- Normalizing timestamps, hostnames, and user identifiers
- Enriching logs with asset inventory and ownership data
- Correlating user identities across on-prem and cloud systems
- Handling data at scale using efficient indexing strategies
- Reducing noise through intelligent filtering and aggregation
- Building data pipelines with lightweight ETL principles
- Securing data transmission and storage in transit and at rest
- Evaluating data retention policies for forensic readiness
- Designing scalable storage architectures for heterogeneous data
Module 5: AI-Driven Anomaly Detection Models - Designing user and entity behavior analytics (UEBA) systems
- Modeling normal user activity: login times, geolocation, resource access
- Detecting credential misuse through access pattern deviations
- Using z-scores and moving averages for deviation detection
- Implementing Isolation Forest for outlier identification
- Applying DBSCAN for clustering suspicious event groups
- Building time-based anomaly detection windows
- Detecting sudden spikes in failed authentication attempts
- Identifying beaconing behavior using inter-arrival time analysis
- Scoring connection frequency and destination entropy
- Detecting process creation anomalies via command-line parsing
- Monitoring registry modifications for persistence indicators
- Applying natural language processing to detect malicious scripts
- Using n-gram analysis on PowerShell and command shells
- Flagging obfuscated or encoded commands with entropy scoring
Module 6: AI-Enhanced Lateral Movement Detection - Mapping internal network connectivity and trust relationships
- Detecting suspicious SMB and WMI connection patterns
- Identifying Pass-the-Hash and Pass-the-Ticket behaviors
- Modeling domain admin access to non-owned assets
- Using graph analysis to visualize access anomalies
- Building service account usage profiles and baselines
- Flagging unusual DCOM and remote service creation
- Detecting RDP connection chaining across workstations
- Monitoring replication traffic for abnormal DC access
- Identifying Golden and Silver Ticket abuse through Kerberos anomalies
- Detecting time-based authentication storms from brute force
- Scoring lateral movement likelihood using behavioral features
- Integrating endpoint telemetry with directory service logs
- Creating machine-level trust relationship maps
- Automating detection of unusual service principal name (SPN) requests
Module 7: Detecting Privilege Escalation with AI Logic - Identifying known privilege escalation paths in Windows and Linux
- Detecting token manipulation and impersonation attempts
- Monitoring for SeDebugPrivilege abuse
- Flagging unexpected group membership changes in Active Directory
- Using AI to detect low-and-slow privilege accumulation
- Scoring deviations in command elevation frequency
- Mapping process ancestry to identify rogue executors
- Detecting suspicious use of runas, sudo, and psexec
- Identifying registry-based privilege escalation methods
- Building file permission anomaly detectors
- Monitoring for unexpected kernel module loading
- Detecting exploit abuse of unpatched local vulnerabilities
- Correlating privilege changes with access pattern shifts
- Establishing time-bound privilege windows for auditing
- Creating escalation risk scores based on user and context
Module 8: Cloud-Native Threat Detection and AI Correlation - Establishing cloud detection baselines for AWS, Azure, GCP
- Monitoring for unauthorized IAM role assumption
- Detecting excessive API call volumes in cloud environments
- Identifying unusual S3 bucket access or configuration changes
- Monitoring for public exposure of storage resources
- Tracking anomalous cross-account role usage
- Detecting VM instance spawning in unexpected regions
- Flagging unauthorized Secret Manager or Key Vault access
- Using AI to detect compromised service identities
- Correlating cloudtrail logs with VPC flow logs
- Identifying API gateway abuse for data exfiltration
- Detecting container escape attempts in Kubernetes
- Monitoring for unusual pod-to-pod communication
- Building risk scores for ephemeral cloud assets
- Automating detection of rogue cloud deployments
Module 9: Detection Engineering with AI Feedback Loops - Designing detection rules that learn from analyst validation
- Implementing feedback channels for true positive confirmation
- Automatically adjusting detection thresholds based on feedback
- Using reinforcement learning principles for rule tuning
- Reducing alert fatigue through intelligent suppression rules
- Creating dynamic baselines that adapt to business cycles
- Incorporating seasonal access pattern adjustments
- Using analyst tagging to improve model training
- Building confidence-weighted alert escalation paths
- Implementing A/B testing for rule effectiveness
- Tracking detection efficacy over time with metrics dashboards
- Measuring mean time to detection improvement
- Calculating prevention-to-detection ratio pre and post deployment
- Reporting on hunting coverage across ATT&CK matrix
- Linking detection success to risk reduction benchmarks
Module 10: Practical Threat Hunting Workflows - Structuring a weekly threat hunting cycle
- Selecting high-impact hypothesis targets based on threat intel
- Tactical vs strategic hunting: when to use each
- Using threat intelligence to inform hunting priorities
- Integrating open-source, commercial, and internal intel feeds
- Conducting environment-specific adversary simulations
- Building custom detection rules for active threat campaigns
- Running hypothesis-driven queries across data lakes
- Documenting investigation findings with standardized templates
- Presenting hunting results to technical and executive audiences
- Automating repetitive hunting tasks with scripting
- Using Jupyter notebooks for interactive data exploration
- Developing repeatable hunting playbooks
- Transitioning findings into permanent detection rules
- Measuring hunting program maturity over time
Module 11: AI-Powered Malware and Ransomware Detection - Identifying pre-attack indicators before execution
- Monitoring for suspicious fileless execution paths
- Detecting living-off-the-land (LOLBin) abuse
- Flagging unusual WMI persistence mechanisms
- Using AI to detect anomalous PowerShell script behavior
- Monitoring for certutil, bitsadmin, or mshta abuse
- Scoring process injection techniques: APC, hollowing, etc.
- Detecting reflective DLL loading through API call sequences
- Identifying ransomware file encryption patterns early
- Monitoring for mass file attribute changes or renames
- Flagging sudden spikes in disk I/O or file deletion
- Using machine learning to classify benign vs malicious binaries
- Integrating VirusTotal and hybrid analysis data
- Detecting C2 beaconing via DNS tunneling patterns
- Building behavioral sandboxes with AI-driven analysis
Module 12: Data Exfiltration and Staged Exfiltration Detection - Identifying data staging behaviors before exfiltration
- Detecting archive creation in temporary folders
- Monitoring for base64, gzip, or rar encoding indicators
- Scoring unauthorized cloud upload activity
- Identifying data transfer to external USB devices
- Tracking unusual SMB shares or NFS mounts
- Detecting exfiltration over DNS, HTTP, or ICMP covert channels
- Using entropy analysis to detect encrypted payloads
- Monitoring for unusual egress traffic to foreign jurisdictions
- Correlating user access with data access volume
- Building data movement risk scores per user
- Flagging large data exports from databases or cloud buckets
- Integrating DLP telemetry with behavioral analytics
- Detecting low-volume, long-duration exfiltration (slow drip)
- Automating alerts for threshold breaches based on role
Module 13: Integrating AI with SIEM, EDR, and SOAR - Connecting AI detection models to Splunk, Sentinel, QRadar
- Building real-time detection pipelines with Kafka and Spark
- Streaming AI outputs into existing alert consoles
- Automating incident creation in SOAR platforms upon high-score detection
- Using Phantom, Demisto, or Cortex XSOAR playbooks
- Enriching tickets with AI-generated context and confidence
- Creating feedback mechanisms from analyst resolution
- Using bidirectional integration to update models from case outcomes
- Orchestrating automated containment for high-confidence threats
- Implementing AI-scoring within incident triage workflows
- Reducing manual triage time by up to 75% using AI prioritization
- Building dashboards that visualize AI detection efficacy
- Integrating AI outputs into executive risk reporting
- Ensuring compliance with auditing and logging standards
- Documenting integration designs for operational handover
Module 14: Implementing and Scaling Your AI Hunting Program - Building a phased rollout plan for AI detection
- Starting with pilot detection use cases
- Securing leadership buy-in with measurable KPIs
- Training analysts on interpreting AI-generated alerts
- Creating centralized rule repositories for team access
- Establishing peer review processes for new detections
- Developing version control and change management policies
- Onboarding new team members with standardized playbooks
- Conducting quarterly detection maturity assessments
- Integrating with purple team exercises for validation
- Scaling detection coverage across business units
- Measuring cost savings from reduced breach impact
- Documenting ROI for cybersecurity investment reviews
- Presenting success metrics to board and audit committees
- Continuously iterating based on threat landscape evolution
Module 15: Certification, Next Steps, and Career Advancement - Completing the final assessment: deploy a live detection model
- Documenting your AI threat hunting framework
- Submitting for review and receiving your Certificate of Completion
- Verification process and digital badge issuance
- Adding the credential to LinkedIn and professional portfolios
- Leveraging the certification in salary negotiations and promotions
- Using your project as a capstone for internal presentations
- Accessing exclusive alumni resources and updates
- Joining a network of certified AI threat hunting practitioners
- Receiving invitations to private working groups and briefings
- Building a personal brand as an AI-savvy security leader
- Transitioning into roles such as Threat Hunter, Detection Engineer, or SOC Architect
- Continuing education pathways with advanced modules
- Accessing updated playbooks and detection rules quarterly
- Maintaining your certification through ongoing learning credits
- Understanding the limitations of reactive security models
- Defining threat hunting: hypothesis-driven vs data-driven approaches
- The kill chain vs MITRE ATT&CK framework evolution
- Core principles of adversary emulation and behavioral modeling
- Mapping enterprise assets to attack surfaces
- Establishing baseline network and user behavior
- Identifying high-value targets within hybrid environments
- Creating asset criticality and exposure matrices
- Introducing the concept of hunting hypotheses
- Developing threat-informed hunting strategies
- Quantifying risk exposure using likelihood and impact scoring
- Aligning threat hunting with organizational risk posture
- Setting clear success metrics for hunting operations
- Integrating threat hunting into incident response workflows
- Building your first simple hunting hypothesis statement
Module 2: AI in Cybersecurity - Principles, Myths, and Realities - Differentiating AI, ML, and automation in security contexts
- Understanding supervised vs unsupervised learning applications
- Debunking the myth that AI replaces human analysts
- How AI augments decision-making speed and accuracy
- Core machine learning concepts: features, labels, models, and inference
- Temporal analysis and session-based anomaly detection
- Clustering techniques for identifying unknown threat patterns
- Using dimensionality reduction to simplify complex logs
- Evaluating model performance: precision, recall, F1 score
- Avoiding overfitting and false confidence in AI outputs
- Interpreting confidence scores and probability thresholds
- Understanding model drift and concept shift in live environments
- Ensuring AI systems remain aligned with evolving tactics
- Building trust in AI through explainable detection logic
- Integrating AI insights with analyst judgment and context
Module 3: Building Your AI-Powered Detection Framework - Designing a modular, reusable detection framework architecture
- Establishing data ingestion standards across sources
- Normalizing logs from firewalls, endpoints, cloud, and identity systems
- Defining atomic detection units and chained logic
- Developing scoring mechanisms for risk prioritization
- Creating weighted alert escalation tiers
- Mapping detections to MITRE ATT&CK techniques and sub-techniques
- Integrating ATT&CK Navigator for visual mapping
- Using YAML for structured detection rule definition
- Validating detection logic against real-world scenarios
- Version controlling rules with Git-based workflows
- Automating rule testing with synthetic attack simulations
- Creating detection playbooks for common adversary behaviors
- Defining false positive mitigation strategies
- Building feedback loops to refine detection performance
Module 4: Data Engineering for Threat Detection - Identifying high-signal data sources across enterprise layers
- Collecting and enriching Windows Event Logs (4624, 4688, 4670, etc.)
- Extracting actionable telemetry from EDR platforms
- Processing cloud audit logs from AWS CloudTrail, Azure Activity Log
- Harvesting PowerShell command-line and script block logging
- Using Sysmon effectively without performance degradation
- Normalizing timestamps, hostnames, and user identifiers
- Enriching logs with asset inventory and ownership data
- Correlating user identities across on-prem and cloud systems
- Handling data at scale using efficient indexing strategies
- Reducing noise through intelligent filtering and aggregation
- Building data pipelines with lightweight ETL principles
- Securing data transmission and storage in transit and at rest
- Evaluating data retention policies for forensic readiness
- Designing scalable storage architectures for heterogeneous data
Module 5: AI-Driven Anomaly Detection Models - Designing user and entity behavior analytics (UEBA) systems
- Modeling normal user activity: login times, geolocation, resource access
- Detecting credential misuse through access pattern deviations
- Using z-scores and moving averages for deviation detection
- Implementing Isolation Forest for outlier identification
- Applying DBSCAN for clustering suspicious event groups
- Building time-based anomaly detection windows
- Detecting sudden spikes in failed authentication attempts
- Identifying beaconing behavior using inter-arrival time analysis
- Scoring connection frequency and destination entropy
- Detecting process creation anomalies via command-line parsing
- Monitoring registry modifications for persistence indicators
- Applying natural language processing to detect malicious scripts
- Using n-gram analysis on PowerShell and command shells
- Flagging obfuscated or encoded commands with entropy scoring
Module 6: AI-Enhanced Lateral Movement Detection - Mapping internal network connectivity and trust relationships
- Detecting suspicious SMB and WMI connection patterns
- Identifying Pass-the-Hash and Pass-the-Ticket behaviors
- Modeling domain admin access to non-owned assets
- Using graph analysis to visualize access anomalies
- Building service account usage profiles and baselines
- Flagging unusual DCOM and remote service creation
- Detecting RDP connection chaining across workstations
- Monitoring replication traffic for abnormal DC access
- Identifying Golden and Silver Ticket abuse through Kerberos anomalies
- Detecting time-based authentication storms from brute force
- Scoring lateral movement likelihood using behavioral features
- Integrating endpoint telemetry with directory service logs
- Creating machine-level trust relationship maps
- Automating detection of unusual service principal name (SPN) requests
Module 7: Detecting Privilege Escalation with AI Logic - Identifying known privilege escalation paths in Windows and Linux
- Detecting token manipulation and impersonation attempts
- Monitoring for SeDebugPrivilege abuse
- Flagging unexpected group membership changes in Active Directory
- Using AI to detect low-and-slow privilege accumulation
- Scoring deviations in command elevation frequency
- Mapping process ancestry to identify rogue executors
- Detecting suspicious use of runas, sudo, and psexec
- Identifying registry-based privilege escalation methods
- Building file permission anomaly detectors
- Monitoring for unexpected kernel module loading
- Detecting exploit abuse of unpatched local vulnerabilities
- Correlating privilege changes with access pattern shifts
- Establishing time-bound privilege windows for auditing
- Creating escalation risk scores based on user and context
Module 8: Cloud-Native Threat Detection and AI Correlation - Establishing cloud detection baselines for AWS, Azure, GCP
- Monitoring for unauthorized IAM role assumption
- Detecting excessive API call volumes in cloud environments
- Identifying unusual S3 bucket access or configuration changes
- Monitoring for public exposure of storage resources
- Tracking anomalous cross-account role usage
- Detecting VM instance spawning in unexpected regions
- Flagging unauthorized Secret Manager or Key Vault access
- Using AI to detect compromised service identities
- Correlating cloudtrail logs with VPC flow logs
- Identifying API gateway abuse for data exfiltration
- Detecting container escape attempts in Kubernetes
- Monitoring for unusual pod-to-pod communication
- Building risk scores for ephemeral cloud assets
- Automating detection of rogue cloud deployments
Module 9: Detection Engineering with AI Feedback Loops - Designing detection rules that learn from analyst validation
- Implementing feedback channels for true positive confirmation
- Automatically adjusting detection thresholds based on feedback
- Using reinforcement learning principles for rule tuning
- Reducing alert fatigue through intelligent suppression rules
- Creating dynamic baselines that adapt to business cycles
- Incorporating seasonal access pattern adjustments
- Using analyst tagging to improve model training
- Building confidence-weighted alert escalation paths
- Implementing A/B testing for rule effectiveness
- Tracking detection efficacy over time with metrics dashboards
- Measuring mean time to detection improvement
- Calculating prevention-to-detection ratio pre and post deployment
- Reporting on hunting coverage across ATT&CK matrix
- Linking detection success to risk reduction benchmarks
Module 10: Practical Threat Hunting Workflows - Structuring a weekly threat hunting cycle
- Selecting high-impact hypothesis targets based on threat intel
- Tactical vs strategic hunting: when to use each
- Using threat intelligence to inform hunting priorities
- Integrating open-source, commercial, and internal intel feeds
- Conducting environment-specific adversary simulations
- Building custom detection rules for active threat campaigns
- Running hypothesis-driven queries across data lakes
- Documenting investigation findings with standardized templates
- Presenting hunting results to technical and executive audiences
- Automating repetitive hunting tasks with scripting
- Using Jupyter notebooks for interactive data exploration
- Developing repeatable hunting playbooks
- Transitioning findings into permanent detection rules
- Measuring hunting program maturity over time
Module 11: AI-Powered Malware and Ransomware Detection - Identifying pre-attack indicators before execution
- Monitoring for suspicious fileless execution paths
- Detecting living-off-the-land (LOLBin) abuse
- Flagging unusual WMI persistence mechanisms
- Using AI to detect anomalous PowerShell script behavior
- Monitoring for certutil, bitsadmin, or mshta abuse
- Scoring process injection techniques: APC, hollowing, etc.
- Detecting reflective DLL loading through API call sequences
- Identifying ransomware file encryption patterns early
- Monitoring for mass file attribute changes or renames
- Flagging sudden spikes in disk I/O or file deletion
- Using machine learning to classify benign vs malicious binaries
- Integrating VirusTotal and hybrid analysis data
- Detecting C2 beaconing via DNS tunneling patterns
- Building behavioral sandboxes with AI-driven analysis
Module 12: Data Exfiltration and Staged Exfiltration Detection - Identifying data staging behaviors before exfiltration
- Detecting archive creation in temporary folders
- Monitoring for base64, gzip, or rar encoding indicators
- Scoring unauthorized cloud upload activity
- Identifying data transfer to external USB devices
- Tracking unusual SMB shares or NFS mounts
- Detecting exfiltration over DNS, HTTP, or ICMP covert channels
- Using entropy analysis to detect encrypted payloads
- Monitoring for unusual egress traffic to foreign jurisdictions
- Correlating user access with data access volume
- Building data movement risk scores per user
- Flagging large data exports from databases or cloud buckets
- Integrating DLP telemetry with behavioral analytics
- Detecting low-volume, long-duration exfiltration (slow drip)
- Automating alerts for threshold breaches based on role
Module 13: Integrating AI with SIEM, EDR, and SOAR - Connecting AI detection models to Splunk, Sentinel, QRadar
- Building real-time detection pipelines with Kafka and Spark
- Streaming AI outputs into existing alert consoles
- Automating incident creation in SOAR platforms upon high-score detection
- Using Phantom, Demisto, or Cortex XSOAR playbooks
- Enriching tickets with AI-generated context and confidence
- Creating feedback mechanisms from analyst resolution
- Using bidirectional integration to update models from case outcomes
- Orchestrating automated containment for high-confidence threats
- Implementing AI-scoring within incident triage workflows
- Reducing manual triage time by up to 75% using AI prioritization
- Building dashboards that visualize AI detection efficacy
- Integrating AI outputs into executive risk reporting
- Ensuring compliance with auditing and logging standards
- Documenting integration designs for operational handover
Module 14: Implementing and Scaling Your AI Hunting Program - Building a phased rollout plan for AI detection
- Starting with pilot detection use cases
- Securing leadership buy-in with measurable KPIs
- Training analysts on interpreting AI-generated alerts
- Creating centralized rule repositories for team access
- Establishing peer review processes for new detections
- Developing version control and change management policies
- Onboarding new team members with standardized playbooks
- Conducting quarterly detection maturity assessments
- Integrating with purple team exercises for validation
- Scaling detection coverage across business units
- Measuring cost savings from reduced breach impact
- Documenting ROI for cybersecurity investment reviews
- Presenting success metrics to board and audit committees
- Continuously iterating based on threat landscape evolution
Module 15: Certification, Next Steps, and Career Advancement - Completing the final assessment: deploy a live detection model
- Documenting your AI threat hunting framework
- Submitting for review and receiving your Certificate of Completion
- Verification process and digital badge issuance
- Adding the credential to LinkedIn and professional portfolios
- Leveraging the certification in salary negotiations and promotions
- Using your project as a capstone for internal presentations
- Accessing exclusive alumni resources and updates
- Joining a network of certified AI threat hunting practitioners
- Receiving invitations to private working groups and briefings
- Building a personal brand as an AI-savvy security leader
- Transitioning into roles such as Threat Hunter, Detection Engineer, or SOC Architect
- Continuing education pathways with advanced modules
- Accessing updated playbooks and detection rules quarterly
- Maintaining your certification through ongoing learning credits
- Designing a modular, reusable detection framework architecture
- Establishing data ingestion standards across sources
- Normalizing logs from firewalls, endpoints, cloud, and identity systems
- Defining atomic detection units and chained logic
- Developing scoring mechanisms for risk prioritization
- Creating weighted alert escalation tiers
- Mapping detections to MITRE ATT&CK techniques and sub-techniques
- Integrating ATT&CK Navigator for visual mapping
- Using YAML for structured detection rule definition
- Validating detection logic against real-world scenarios
- Version controlling rules with Git-based workflows
- Automating rule testing with synthetic attack simulations
- Creating detection playbooks for common adversary behaviors
- Defining false positive mitigation strategies
- Building feedback loops to refine detection performance
Module 4: Data Engineering for Threat Detection - Identifying high-signal data sources across enterprise layers
- Collecting and enriching Windows Event Logs (4624, 4688, 4670, etc.)
- Extracting actionable telemetry from EDR platforms
- Processing cloud audit logs from AWS CloudTrail, Azure Activity Log
- Harvesting PowerShell command-line and script block logging
- Using Sysmon effectively without performance degradation
- Normalizing timestamps, hostnames, and user identifiers
- Enriching logs with asset inventory and ownership data
- Correlating user identities across on-prem and cloud systems
- Handling data at scale using efficient indexing strategies
- Reducing noise through intelligent filtering and aggregation
- Building data pipelines with lightweight ETL principles
- Securing data transmission and storage in transit and at rest
- Evaluating data retention policies for forensic readiness
- Designing scalable storage architectures for heterogeneous data
Module 5: AI-Driven Anomaly Detection Models - Designing user and entity behavior analytics (UEBA) systems
- Modeling normal user activity: login times, geolocation, resource access
- Detecting credential misuse through access pattern deviations
- Using z-scores and moving averages for deviation detection
- Implementing Isolation Forest for outlier identification
- Applying DBSCAN for clustering suspicious event groups
- Building time-based anomaly detection windows
- Detecting sudden spikes in failed authentication attempts
- Identifying beaconing behavior using inter-arrival time analysis
- Scoring connection frequency and destination entropy
- Detecting process creation anomalies via command-line parsing
- Monitoring registry modifications for persistence indicators
- Applying natural language processing to detect malicious scripts
- Using n-gram analysis on PowerShell and command shells
- Flagging obfuscated or encoded commands with entropy scoring
Module 6: AI-Enhanced Lateral Movement Detection - Mapping internal network connectivity and trust relationships
- Detecting suspicious SMB and WMI connection patterns
- Identifying Pass-the-Hash and Pass-the-Ticket behaviors
- Modeling domain admin access to non-owned assets
- Using graph analysis to visualize access anomalies
- Building service account usage profiles and baselines
- Flagging unusual DCOM and remote service creation
- Detecting RDP connection chaining across workstations
- Monitoring replication traffic for abnormal DC access
- Identifying Golden and Silver Ticket abuse through Kerberos anomalies
- Detecting time-based authentication storms from brute force
- Scoring lateral movement likelihood using behavioral features
- Integrating endpoint telemetry with directory service logs
- Creating machine-level trust relationship maps
- Automating detection of unusual service principal name (SPN) requests
Module 7: Detecting Privilege Escalation with AI Logic - Identifying known privilege escalation paths in Windows and Linux
- Detecting token manipulation and impersonation attempts
- Monitoring for SeDebugPrivilege abuse
- Flagging unexpected group membership changes in Active Directory
- Using AI to detect low-and-slow privilege accumulation
- Scoring deviations in command elevation frequency
- Mapping process ancestry to identify rogue executors
- Detecting suspicious use of runas, sudo, and psexec
- Identifying registry-based privilege escalation methods
- Building file permission anomaly detectors
- Monitoring for unexpected kernel module loading
- Detecting exploit abuse of unpatched local vulnerabilities
- Correlating privilege changes with access pattern shifts
- Establishing time-bound privilege windows for auditing
- Creating escalation risk scores based on user and context
Module 8: Cloud-Native Threat Detection and AI Correlation - Establishing cloud detection baselines for AWS, Azure, GCP
- Monitoring for unauthorized IAM role assumption
- Detecting excessive API call volumes in cloud environments
- Identifying unusual S3 bucket access or configuration changes
- Monitoring for public exposure of storage resources
- Tracking anomalous cross-account role usage
- Detecting VM instance spawning in unexpected regions
- Flagging unauthorized Secret Manager or Key Vault access
- Using AI to detect compromised service identities
- Correlating cloudtrail logs with VPC flow logs
- Identifying API gateway abuse for data exfiltration
- Detecting container escape attempts in Kubernetes
- Monitoring for unusual pod-to-pod communication
- Building risk scores for ephemeral cloud assets
- Automating detection of rogue cloud deployments
Module 9: Detection Engineering with AI Feedback Loops - Designing detection rules that learn from analyst validation
- Implementing feedback channels for true positive confirmation
- Automatically adjusting detection thresholds based on feedback
- Using reinforcement learning principles for rule tuning
- Reducing alert fatigue through intelligent suppression rules
- Creating dynamic baselines that adapt to business cycles
- Incorporating seasonal access pattern adjustments
- Using analyst tagging to improve model training
- Building confidence-weighted alert escalation paths
- Implementing A/B testing for rule effectiveness
- Tracking detection efficacy over time with metrics dashboards
- Measuring mean time to detection improvement
- Calculating prevention-to-detection ratio pre and post deployment
- Reporting on hunting coverage across ATT&CK matrix
- Linking detection success to risk reduction benchmarks
Module 10: Practical Threat Hunting Workflows - Structuring a weekly threat hunting cycle
- Selecting high-impact hypothesis targets based on threat intel
- Tactical vs strategic hunting: when to use each
- Using threat intelligence to inform hunting priorities
- Integrating open-source, commercial, and internal intel feeds
- Conducting environment-specific adversary simulations
- Building custom detection rules for active threat campaigns
- Running hypothesis-driven queries across data lakes
- Documenting investigation findings with standardized templates
- Presenting hunting results to technical and executive audiences
- Automating repetitive hunting tasks with scripting
- Using Jupyter notebooks for interactive data exploration
- Developing repeatable hunting playbooks
- Transitioning findings into permanent detection rules
- Measuring hunting program maturity over time
Module 11: AI-Powered Malware and Ransomware Detection - Identifying pre-attack indicators before execution
- Monitoring for suspicious fileless execution paths
- Detecting living-off-the-land (LOLBin) abuse
- Flagging unusual WMI persistence mechanisms
- Using AI to detect anomalous PowerShell script behavior
- Monitoring for certutil, bitsadmin, or mshta abuse
- Scoring process injection techniques: APC, hollowing, etc.
- Detecting reflective DLL loading through API call sequences
- Identifying ransomware file encryption patterns early
- Monitoring for mass file attribute changes or renames
- Flagging sudden spikes in disk I/O or file deletion
- Using machine learning to classify benign vs malicious binaries
- Integrating VirusTotal and hybrid analysis data
- Detecting C2 beaconing via DNS tunneling patterns
- Building behavioral sandboxes with AI-driven analysis
Module 12: Data Exfiltration and Staged Exfiltration Detection - Identifying data staging behaviors before exfiltration
- Detecting archive creation in temporary folders
- Monitoring for base64, gzip, or rar encoding indicators
- Scoring unauthorized cloud upload activity
- Identifying data transfer to external USB devices
- Tracking unusual SMB shares or NFS mounts
- Detecting exfiltration over DNS, HTTP, or ICMP covert channels
- Using entropy analysis to detect encrypted payloads
- Monitoring for unusual egress traffic to foreign jurisdictions
- Correlating user access with data access volume
- Building data movement risk scores per user
- Flagging large data exports from databases or cloud buckets
- Integrating DLP telemetry with behavioral analytics
- Detecting low-volume, long-duration exfiltration (slow drip)
- Automating alerts for threshold breaches based on role
Module 13: Integrating AI with SIEM, EDR, and SOAR - Connecting AI detection models to Splunk, Sentinel, QRadar
- Building real-time detection pipelines with Kafka and Spark
- Streaming AI outputs into existing alert consoles
- Automating incident creation in SOAR platforms upon high-score detection
- Using Phantom, Demisto, or Cortex XSOAR playbooks
- Enriching tickets with AI-generated context and confidence
- Creating feedback mechanisms from analyst resolution
- Using bidirectional integration to update models from case outcomes
- Orchestrating automated containment for high-confidence threats
- Implementing AI-scoring within incident triage workflows
- Reducing manual triage time by up to 75% using AI prioritization
- Building dashboards that visualize AI detection efficacy
- Integrating AI outputs into executive risk reporting
- Ensuring compliance with auditing and logging standards
- Documenting integration designs for operational handover
Module 14: Implementing and Scaling Your AI Hunting Program - Building a phased rollout plan for AI detection
- Starting with pilot detection use cases
- Securing leadership buy-in with measurable KPIs
- Training analysts on interpreting AI-generated alerts
- Creating centralized rule repositories for team access
- Establishing peer review processes for new detections
- Developing version control and change management policies
- Onboarding new team members with standardized playbooks
- Conducting quarterly detection maturity assessments
- Integrating with purple team exercises for validation
- Scaling detection coverage across business units
- Measuring cost savings from reduced breach impact
- Documenting ROI for cybersecurity investment reviews
- Presenting success metrics to board and audit committees
- Continuously iterating based on threat landscape evolution
Module 15: Certification, Next Steps, and Career Advancement - Completing the final assessment: deploy a live detection model
- Documenting your AI threat hunting framework
- Submitting for review and receiving your Certificate of Completion
- Verification process and digital badge issuance
- Adding the credential to LinkedIn and professional portfolios
- Leveraging the certification in salary negotiations and promotions
- Using your project as a capstone for internal presentations
- Accessing exclusive alumni resources and updates
- Joining a network of certified AI threat hunting practitioners
- Receiving invitations to private working groups and briefings
- Building a personal brand as an AI-savvy security leader
- Transitioning into roles such as Threat Hunter, Detection Engineer, or SOC Architect
- Continuing education pathways with advanced modules
- Accessing updated playbooks and detection rules quarterly
- Maintaining your certification through ongoing learning credits
- Designing user and entity behavior analytics (UEBA) systems
- Modeling normal user activity: login times, geolocation, resource access
- Detecting credential misuse through access pattern deviations
- Using z-scores and moving averages for deviation detection
- Implementing Isolation Forest for outlier identification
- Applying DBSCAN for clustering suspicious event groups
- Building time-based anomaly detection windows
- Detecting sudden spikes in failed authentication attempts
- Identifying beaconing behavior using inter-arrival time analysis
- Scoring connection frequency and destination entropy
- Detecting process creation anomalies via command-line parsing
- Monitoring registry modifications for persistence indicators
- Applying natural language processing to detect malicious scripts
- Using n-gram analysis on PowerShell and command shells
- Flagging obfuscated or encoded commands with entropy scoring
Module 6: AI-Enhanced Lateral Movement Detection - Mapping internal network connectivity and trust relationships
- Detecting suspicious SMB and WMI connection patterns
- Identifying Pass-the-Hash and Pass-the-Ticket behaviors
- Modeling domain admin access to non-owned assets
- Using graph analysis to visualize access anomalies
- Building service account usage profiles and baselines
- Flagging unusual DCOM and remote service creation
- Detecting RDP connection chaining across workstations
- Monitoring replication traffic for abnormal DC access
- Identifying Golden and Silver Ticket abuse through Kerberos anomalies
- Detecting time-based authentication storms from brute force
- Scoring lateral movement likelihood using behavioral features
- Integrating endpoint telemetry with directory service logs
- Creating machine-level trust relationship maps
- Automating detection of unusual service principal name (SPN) requests
Module 7: Detecting Privilege Escalation with AI Logic - Identifying known privilege escalation paths in Windows and Linux
- Detecting token manipulation and impersonation attempts
- Monitoring for SeDebugPrivilege abuse
- Flagging unexpected group membership changes in Active Directory
- Using AI to detect low-and-slow privilege accumulation
- Scoring deviations in command elevation frequency
- Mapping process ancestry to identify rogue executors
- Detecting suspicious use of runas, sudo, and psexec
- Identifying registry-based privilege escalation methods
- Building file permission anomaly detectors
- Monitoring for unexpected kernel module loading
- Detecting exploit abuse of unpatched local vulnerabilities
- Correlating privilege changes with access pattern shifts
- Establishing time-bound privilege windows for auditing
- Creating escalation risk scores based on user and context
Module 8: Cloud-Native Threat Detection and AI Correlation - Establishing cloud detection baselines for AWS, Azure, GCP
- Monitoring for unauthorized IAM role assumption
- Detecting excessive API call volumes in cloud environments
- Identifying unusual S3 bucket access or configuration changes
- Monitoring for public exposure of storage resources
- Tracking anomalous cross-account role usage
- Detecting VM instance spawning in unexpected regions
- Flagging unauthorized Secret Manager or Key Vault access
- Using AI to detect compromised service identities
- Correlating cloudtrail logs with VPC flow logs
- Identifying API gateway abuse for data exfiltration
- Detecting container escape attempts in Kubernetes
- Monitoring for unusual pod-to-pod communication
- Building risk scores for ephemeral cloud assets
- Automating detection of rogue cloud deployments
Module 9: Detection Engineering with AI Feedback Loops - Designing detection rules that learn from analyst validation
- Implementing feedback channels for true positive confirmation
- Automatically adjusting detection thresholds based on feedback
- Using reinforcement learning principles for rule tuning
- Reducing alert fatigue through intelligent suppression rules
- Creating dynamic baselines that adapt to business cycles
- Incorporating seasonal access pattern adjustments
- Using analyst tagging to improve model training
- Building confidence-weighted alert escalation paths
- Implementing A/B testing for rule effectiveness
- Tracking detection efficacy over time with metrics dashboards
- Measuring mean time to detection improvement
- Calculating prevention-to-detection ratio pre and post deployment
- Reporting on hunting coverage across ATT&CK matrix
- Linking detection success to risk reduction benchmarks
Module 10: Practical Threat Hunting Workflows - Structuring a weekly threat hunting cycle
- Selecting high-impact hypothesis targets based on threat intel
- Tactical vs strategic hunting: when to use each
- Using threat intelligence to inform hunting priorities
- Integrating open-source, commercial, and internal intel feeds
- Conducting environment-specific adversary simulations
- Building custom detection rules for active threat campaigns
- Running hypothesis-driven queries across data lakes
- Documenting investigation findings with standardized templates
- Presenting hunting results to technical and executive audiences
- Automating repetitive hunting tasks with scripting
- Using Jupyter notebooks for interactive data exploration
- Developing repeatable hunting playbooks
- Transitioning findings into permanent detection rules
- Measuring hunting program maturity over time
Module 11: AI-Powered Malware and Ransomware Detection - Identifying pre-attack indicators before execution
- Monitoring for suspicious fileless execution paths
- Detecting living-off-the-land (LOLBin) abuse
- Flagging unusual WMI persistence mechanisms
- Using AI to detect anomalous PowerShell script behavior
- Monitoring for certutil, bitsadmin, or mshta abuse
- Scoring process injection techniques: APC, hollowing, etc.
- Detecting reflective DLL loading through API call sequences
- Identifying ransomware file encryption patterns early
- Monitoring for mass file attribute changes or renames
- Flagging sudden spikes in disk I/O or file deletion
- Using machine learning to classify benign vs malicious binaries
- Integrating VirusTotal and hybrid analysis data
- Detecting C2 beaconing via DNS tunneling patterns
- Building behavioral sandboxes with AI-driven analysis
Module 12: Data Exfiltration and Staged Exfiltration Detection - Identifying data staging behaviors before exfiltration
- Detecting archive creation in temporary folders
- Monitoring for base64, gzip, or rar encoding indicators
- Scoring unauthorized cloud upload activity
- Identifying data transfer to external USB devices
- Tracking unusual SMB shares or NFS mounts
- Detecting exfiltration over DNS, HTTP, or ICMP covert channels
- Using entropy analysis to detect encrypted payloads
- Monitoring for unusual egress traffic to foreign jurisdictions
- Correlating user access with data access volume
- Building data movement risk scores per user
- Flagging large data exports from databases or cloud buckets
- Integrating DLP telemetry with behavioral analytics
- Detecting low-volume, long-duration exfiltration (slow drip)
- Automating alerts for threshold breaches based on role
Module 13: Integrating AI with SIEM, EDR, and SOAR - Connecting AI detection models to Splunk, Sentinel, QRadar
- Building real-time detection pipelines with Kafka and Spark
- Streaming AI outputs into existing alert consoles
- Automating incident creation in SOAR platforms upon high-score detection
- Using Phantom, Demisto, or Cortex XSOAR playbooks
- Enriching tickets with AI-generated context and confidence
- Creating feedback mechanisms from analyst resolution
- Using bidirectional integration to update models from case outcomes
- Orchestrating automated containment for high-confidence threats
- Implementing AI-scoring within incident triage workflows
- Reducing manual triage time by up to 75% using AI prioritization
- Building dashboards that visualize AI detection efficacy
- Integrating AI outputs into executive risk reporting
- Ensuring compliance with auditing and logging standards
- Documenting integration designs for operational handover
Module 14: Implementing and Scaling Your AI Hunting Program - Building a phased rollout plan for AI detection
- Starting with pilot detection use cases
- Securing leadership buy-in with measurable KPIs
- Training analysts on interpreting AI-generated alerts
- Creating centralized rule repositories for team access
- Establishing peer review processes for new detections
- Developing version control and change management policies
- Onboarding new team members with standardized playbooks
- Conducting quarterly detection maturity assessments
- Integrating with purple team exercises for validation
- Scaling detection coverage across business units
- Measuring cost savings from reduced breach impact
- Documenting ROI for cybersecurity investment reviews
- Presenting success metrics to board and audit committees
- Continuously iterating based on threat landscape evolution
Module 15: Certification, Next Steps, and Career Advancement - Completing the final assessment: deploy a live detection model
- Documenting your AI threat hunting framework
- Submitting for review and receiving your Certificate of Completion
- Verification process and digital badge issuance
- Adding the credential to LinkedIn and professional portfolios
- Leveraging the certification in salary negotiations and promotions
- Using your project as a capstone for internal presentations
- Accessing exclusive alumni resources and updates
- Joining a network of certified AI threat hunting practitioners
- Receiving invitations to private working groups and briefings
- Building a personal brand as an AI-savvy security leader
- Transitioning into roles such as Threat Hunter, Detection Engineer, or SOC Architect
- Continuing education pathways with advanced modules
- Accessing updated playbooks and detection rules quarterly
- Maintaining your certification through ongoing learning credits
- Identifying known privilege escalation paths in Windows and Linux
- Detecting token manipulation and impersonation attempts
- Monitoring for SeDebugPrivilege abuse
- Flagging unexpected group membership changes in Active Directory
- Using AI to detect low-and-slow privilege accumulation
- Scoring deviations in command elevation frequency
- Mapping process ancestry to identify rogue executors
- Detecting suspicious use of runas, sudo, and psexec
- Identifying registry-based privilege escalation methods
- Building file permission anomaly detectors
- Monitoring for unexpected kernel module loading
- Detecting exploit abuse of unpatched local vulnerabilities
- Correlating privilege changes with access pattern shifts
- Establishing time-bound privilege windows for auditing
- Creating escalation risk scores based on user and context
Module 8: Cloud-Native Threat Detection and AI Correlation - Establishing cloud detection baselines for AWS, Azure, GCP
- Monitoring for unauthorized IAM role assumption
- Detecting excessive API call volumes in cloud environments
- Identifying unusual S3 bucket access or configuration changes
- Monitoring for public exposure of storage resources
- Tracking anomalous cross-account role usage
- Detecting VM instance spawning in unexpected regions
- Flagging unauthorized Secret Manager or Key Vault access
- Using AI to detect compromised service identities
- Correlating cloudtrail logs with VPC flow logs
- Identifying API gateway abuse for data exfiltration
- Detecting container escape attempts in Kubernetes
- Monitoring for unusual pod-to-pod communication
- Building risk scores for ephemeral cloud assets
- Automating detection of rogue cloud deployments
Module 9: Detection Engineering with AI Feedback Loops - Designing detection rules that learn from analyst validation
- Implementing feedback channels for true positive confirmation
- Automatically adjusting detection thresholds based on feedback
- Using reinforcement learning principles for rule tuning
- Reducing alert fatigue through intelligent suppression rules
- Creating dynamic baselines that adapt to business cycles
- Incorporating seasonal access pattern adjustments
- Using analyst tagging to improve model training
- Building confidence-weighted alert escalation paths
- Implementing A/B testing for rule effectiveness
- Tracking detection efficacy over time with metrics dashboards
- Measuring mean time to detection improvement
- Calculating prevention-to-detection ratio pre and post deployment
- Reporting on hunting coverage across ATT&CK matrix
- Linking detection success to risk reduction benchmarks
Module 10: Practical Threat Hunting Workflows - Structuring a weekly threat hunting cycle
- Selecting high-impact hypothesis targets based on threat intel
- Tactical vs strategic hunting: when to use each
- Using threat intelligence to inform hunting priorities
- Integrating open-source, commercial, and internal intel feeds
- Conducting environment-specific adversary simulations
- Building custom detection rules for active threat campaigns
- Running hypothesis-driven queries across data lakes
- Documenting investigation findings with standardized templates
- Presenting hunting results to technical and executive audiences
- Automating repetitive hunting tasks with scripting
- Using Jupyter notebooks for interactive data exploration
- Developing repeatable hunting playbooks
- Transitioning findings into permanent detection rules
- Measuring hunting program maturity over time
Module 11: AI-Powered Malware and Ransomware Detection - Identifying pre-attack indicators before execution
- Monitoring for suspicious fileless execution paths
- Detecting living-off-the-land (LOLBin) abuse
- Flagging unusual WMI persistence mechanisms
- Using AI to detect anomalous PowerShell script behavior
- Monitoring for certutil, bitsadmin, or mshta abuse
- Scoring process injection techniques: APC, hollowing, etc.
- Detecting reflective DLL loading through API call sequences
- Identifying ransomware file encryption patterns early
- Monitoring for mass file attribute changes or renames
- Flagging sudden spikes in disk I/O or file deletion
- Using machine learning to classify benign vs malicious binaries
- Integrating VirusTotal and hybrid analysis data
- Detecting C2 beaconing via DNS tunneling patterns
- Building behavioral sandboxes with AI-driven analysis
Module 12: Data Exfiltration and Staged Exfiltration Detection - Identifying data staging behaviors before exfiltration
- Detecting archive creation in temporary folders
- Monitoring for base64, gzip, or rar encoding indicators
- Scoring unauthorized cloud upload activity
- Identifying data transfer to external USB devices
- Tracking unusual SMB shares or NFS mounts
- Detecting exfiltration over DNS, HTTP, or ICMP covert channels
- Using entropy analysis to detect encrypted payloads
- Monitoring for unusual egress traffic to foreign jurisdictions
- Correlating user access with data access volume
- Building data movement risk scores per user
- Flagging large data exports from databases or cloud buckets
- Integrating DLP telemetry with behavioral analytics
- Detecting low-volume, long-duration exfiltration (slow drip)
- Automating alerts for threshold breaches based on role
Module 13: Integrating AI with SIEM, EDR, and SOAR - Connecting AI detection models to Splunk, Sentinel, QRadar
- Building real-time detection pipelines with Kafka and Spark
- Streaming AI outputs into existing alert consoles
- Automating incident creation in SOAR platforms upon high-score detection
- Using Phantom, Demisto, or Cortex XSOAR playbooks
- Enriching tickets with AI-generated context and confidence
- Creating feedback mechanisms from analyst resolution
- Using bidirectional integration to update models from case outcomes
- Orchestrating automated containment for high-confidence threats
- Implementing AI-scoring within incident triage workflows
- Reducing manual triage time by up to 75% using AI prioritization
- Building dashboards that visualize AI detection efficacy
- Integrating AI outputs into executive risk reporting
- Ensuring compliance with auditing and logging standards
- Documenting integration designs for operational handover
Module 14: Implementing and Scaling Your AI Hunting Program - Building a phased rollout plan for AI detection
- Starting with pilot detection use cases
- Securing leadership buy-in with measurable KPIs
- Training analysts on interpreting AI-generated alerts
- Creating centralized rule repositories for team access
- Establishing peer review processes for new detections
- Developing version control and change management policies
- Onboarding new team members with standardized playbooks
- Conducting quarterly detection maturity assessments
- Integrating with purple team exercises for validation
- Scaling detection coverage across business units
- Measuring cost savings from reduced breach impact
- Documenting ROI for cybersecurity investment reviews
- Presenting success metrics to board and audit committees
- Continuously iterating based on threat landscape evolution
Module 15: Certification, Next Steps, and Career Advancement - Completing the final assessment: deploy a live detection model
- Documenting your AI threat hunting framework
- Submitting for review and receiving your Certificate of Completion
- Verification process and digital badge issuance
- Adding the credential to LinkedIn and professional portfolios
- Leveraging the certification in salary negotiations and promotions
- Using your project as a capstone for internal presentations
- Accessing exclusive alumni resources and updates
- Joining a network of certified AI threat hunting practitioners
- Receiving invitations to private working groups and briefings
- Building a personal brand as an AI-savvy security leader
- Transitioning into roles such as Threat Hunter, Detection Engineer, or SOC Architect
- Continuing education pathways with advanced modules
- Accessing updated playbooks and detection rules quarterly
- Maintaining your certification through ongoing learning credits
- Designing detection rules that learn from analyst validation
- Implementing feedback channels for true positive confirmation
- Automatically adjusting detection thresholds based on feedback
- Using reinforcement learning principles for rule tuning
- Reducing alert fatigue through intelligent suppression rules
- Creating dynamic baselines that adapt to business cycles
- Incorporating seasonal access pattern adjustments
- Using analyst tagging to improve model training
- Building confidence-weighted alert escalation paths
- Implementing A/B testing for rule effectiveness
- Tracking detection efficacy over time with metrics dashboards
- Measuring mean time to detection improvement
- Calculating prevention-to-detection ratio pre and post deployment
- Reporting on hunting coverage across ATT&CK matrix
- Linking detection success to risk reduction benchmarks
Module 10: Practical Threat Hunting Workflows - Structuring a weekly threat hunting cycle
- Selecting high-impact hypothesis targets based on threat intel
- Tactical vs strategic hunting: when to use each
- Using threat intelligence to inform hunting priorities
- Integrating open-source, commercial, and internal intel feeds
- Conducting environment-specific adversary simulations
- Building custom detection rules for active threat campaigns
- Running hypothesis-driven queries across data lakes
- Documenting investigation findings with standardized templates
- Presenting hunting results to technical and executive audiences
- Automating repetitive hunting tasks with scripting
- Using Jupyter notebooks for interactive data exploration
- Developing repeatable hunting playbooks
- Transitioning findings into permanent detection rules
- Measuring hunting program maturity over time
Module 11: AI-Powered Malware and Ransomware Detection - Identifying pre-attack indicators before execution
- Monitoring for suspicious fileless execution paths
- Detecting living-off-the-land (LOLBin) abuse
- Flagging unusual WMI persistence mechanisms
- Using AI to detect anomalous PowerShell script behavior
- Monitoring for certutil, bitsadmin, or mshta abuse
- Scoring process injection techniques: APC, hollowing, etc.
- Detecting reflective DLL loading through API call sequences
- Identifying ransomware file encryption patterns early
- Monitoring for mass file attribute changes or renames
- Flagging sudden spikes in disk I/O or file deletion
- Using machine learning to classify benign vs malicious binaries
- Integrating VirusTotal and hybrid analysis data
- Detecting C2 beaconing via DNS tunneling patterns
- Building behavioral sandboxes with AI-driven analysis
Module 12: Data Exfiltration and Staged Exfiltration Detection - Identifying data staging behaviors before exfiltration
- Detecting archive creation in temporary folders
- Monitoring for base64, gzip, or rar encoding indicators
- Scoring unauthorized cloud upload activity
- Identifying data transfer to external USB devices
- Tracking unusual SMB shares or NFS mounts
- Detecting exfiltration over DNS, HTTP, or ICMP covert channels
- Using entropy analysis to detect encrypted payloads
- Monitoring for unusual egress traffic to foreign jurisdictions
- Correlating user access with data access volume
- Building data movement risk scores per user
- Flagging large data exports from databases or cloud buckets
- Integrating DLP telemetry with behavioral analytics
- Detecting low-volume, long-duration exfiltration (slow drip)
- Automating alerts for threshold breaches based on role
Module 13: Integrating AI with SIEM, EDR, and SOAR - Connecting AI detection models to Splunk, Sentinel, QRadar
- Building real-time detection pipelines with Kafka and Spark
- Streaming AI outputs into existing alert consoles
- Automating incident creation in SOAR platforms upon high-score detection
- Using Phantom, Demisto, or Cortex XSOAR playbooks
- Enriching tickets with AI-generated context and confidence
- Creating feedback mechanisms from analyst resolution
- Using bidirectional integration to update models from case outcomes
- Orchestrating automated containment for high-confidence threats
- Implementing AI-scoring within incident triage workflows
- Reducing manual triage time by up to 75% using AI prioritization
- Building dashboards that visualize AI detection efficacy
- Integrating AI outputs into executive risk reporting
- Ensuring compliance with auditing and logging standards
- Documenting integration designs for operational handover
Module 14: Implementing and Scaling Your AI Hunting Program - Building a phased rollout plan for AI detection
- Starting with pilot detection use cases
- Securing leadership buy-in with measurable KPIs
- Training analysts on interpreting AI-generated alerts
- Creating centralized rule repositories for team access
- Establishing peer review processes for new detections
- Developing version control and change management policies
- Onboarding new team members with standardized playbooks
- Conducting quarterly detection maturity assessments
- Integrating with purple team exercises for validation
- Scaling detection coverage across business units
- Measuring cost savings from reduced breach impact
- Documenting ROI for cybersecurity investment reviews
- Presenting success metrics to board and audit committees
- Continuously iterating based on threat landscape evolution
Module 15: Certification, Next Steps, and Career Advancement - Completing the final assessment: deploy a live detection model
- Documenting your AI threat hunting framework
- Submitting for review and receiving your Certificate of Completion
- Verification process and digital badge issuance
- Adding the credential to LinkedIn and professional portfolios
- Leveraging the certification in salary negotiations and promotions
- Using your project as a capstone for internal presentations
- Accessing exclusive alumni resources and updates
- Joining a network of certified AI threat hunting practitioners
- Receiving invitations to private working groups and briefings
- Building a personal brand as an AI-savvy security leader
- Transitioning into roles such as Threat Hunter, Detection Engineer, or SOC Architect
- Continuing education pathways with advanced modules
- Accessing updated playbooks and detection rules quarterly
- Maintaining your certification through ongoing learning credits
- Identifying pre-attack indicators before execution
- Monitoring for suspicious fileless execution paths
- Detecting living-off-the-land (LOLBin) abuse
- Flagging unusual WMI persistence mechanisms
- Using AI to detect anomalous PowerShell script behavior
- Monitoring for certutil, bitsadmin, or mshta abuse
- Scoring process injection techniques: APC, hollowing, etc.
- Detecting reflective DLL loading through API call sequences
- Identifying ransomware file encryption patterns early
- Monitoring for mass file attribute changes or renames
- Flagging sudden spikes in disk I/O or file deletion
- Using machine learning to classify benign vs malicious binaries
- Integrating VirusTotal and hybrid analysis data
- Detecting C2 beaconing via DNS tunneling patterns
- Building behavioral sandboxes with AI-driven analysis
Module 12: Data Exfiltration and Staged Exfiltration Detection - Identifying data staging behaviors before exfiltration
- Detecting archive creation in temporary folders
- Monitoring for base64, gzip, or rar encoding indicators
- Scoring unauthorized cloud upload activity
- Identifying data transfer to external USB devices
- Tracking unusual SMB shares or NFS mounts
- Detecting exfiltration over DNS, HTTP, or ICMP covert channels
- Using entropy analysis to detect encrypted payloads
- Monitoring for unusual egress traffic to foreign jurisdictions
- Correlating user access with data access volume
- Building data movement risk scores per user
- Flagging large data exports from databases or cloud buckets
- Integrating DLP telemetry with behavioral analytics
- Detecting low-volume, long-duration exfiltration (slow drip)
- Automating alerts for threshold breaches based on role
Module 13: Integrating AI with SIEM, EDR, and SOAR - Connecting AI detection models to Splunk, Sentinel, QRadar
- Building real-time detection pipelines with Kafka and Spark
- Streaming AI outputs into existing alert consoles
- Automating incident creation in SOAR platforms upon high-score detection
- Using Phantom, Demisto, or Cortex XSOAR playbooks
- Enriching tickets with AI-generated context and confidence
- Creating feedback mechanisms from analyst resolution
- Using bidirectional integration to update models from case outcomes
- Orchestrating automated containment for high-confidence threats
- Implementing AI-scoring within incident triage workflows
- Reducing manual triage time by up to 75% using AI prioritization
- Building dashboards that visualize AI detection efficacy
- Integrating AI outputs into executive risk reporting
- Ensuring compliance with auditing and logging standards
- Documenting integration designs for operational handover
Module 14: Implementing and Scaling Your AI Hunting Program - Building a phased rollout plan for AI detection
- Starting with pilot detection use cases
- Securing leadership buy-in with measurable KPIs
- Training analysts on interpreting AI-generated alerts
- Creating centralized rule repositories for team access
- Establishing peer review processes for new detections
- Developing version control and change management policies
- Onboarding new team members with standardized playbooks
- Conducting quarterly detection maturity assessments
- Integrating with purple team exercises for validation
- Scaling detection coverage across business units
- Measuring cost savings from reduced breach impact
- Documenting ROI for cybersecurity investment reviews
- Presenting success metrics to board and audit committees
- Continuously iterating based on threat landscape evolution
Module 15: Certification, Next Steps, and Career Advancement - Completing the final assessment: deploy a live detection model
- Documenting your AI threat hunting framework
- Submitting for review and receiving your Certificate of Completion
- Verification process and digital badge issuance
- Adding the credential to LinkedIn and professional portfolios
- Leveraging the certification in salary negotiations and promotions
- Using your project as a capstone for internal presentations
- Accessing exclusive alumni resources and updates
- Joining a network of certified AI threat hunting practitioners
- Receiving invitations to private working groups and briefings
- Building a personal brand as an AI-savvy security leader
- Transitioning into roles such as Threat Hunter, Detection Engineer, or SOC Architect
- Continuing education pathways with advanced modules
- Accessing updated playbooks and detection rules quarterly
- Maintaining your certification through ongoing learning credits
- Connecting AI detection models to Splunk, Sentinel, QRadar
- Building real-time detection pipelines with Kafka and Spark
- Streaming AI outputs into existing alert consoles
- Automating incident creation in SOAR platforms upon high-score detection
- Using Phantom, Demisto, or Cortex XSOAR playbooks
- Enriching tickets with AI-generated context and confidence
- Creating feedback mechanisms from analyst resolution
- Using bidirectional integration to update models from case outcomes
- Orchestrating automated containment for high-confidence threats
- Implementing AI-scoring within incident triage workflows
- Reducing manual triage time by up to 75% using AI prioritization
- Building dashboards that visualize AI detection efficacy
- Integrating AI outputs into executive risk reporting
- Ensuring compliance with auditing and logging standards
- Documenting integration designs for operational handover
Module 14: Implementing and Scaling Your AI Hunting Program - Building a phased rollout plan for AI detection
- Starting with pilot detection use cases
- Securing leadership buy-in with measurable KPIs
- Training analysts on interpreting AI-generated alerts
- Creating centralized rule repositories for team access
- Establishing peer review processes for new detections
- Developing version control and change management policies
- Onboarding new team members with standardized playbooks
- Conducting quarterly detection maturity assessments
- Integrating with purple team exercises for validation
- Scaling detection coverage across business units
- Measuring cost savings from reduced breach impact
- Documenting ROI for cybersecurity investment reviews
- Presenting success metrics to board and audit committees
- Continuously iterating based on threat landscape evolution
Module 15: Certification, Next Steps, and Career Advancement - Completing the final assessment: deploy a live detection model
- Documenting your AI threat hunting framework
- Submitting for review and receiving your Certificate of Completion
- Verification process and digital badge issuance
- Adding the credential to LinkedIn and professional portfolios
- Leveraging the certification in salary negotiations and promotions
- Using your project as a capstone for internal presentations
- Accessing exclusive alumni resources and updates
- Joining a network of certified AI threat hunting practitioners
- Receiving invitations to private working groups and briefings
- Building a personal brand as an AI-savvy security leader
- Transitioning into roles such as Threat Hunter, Detection Engineer, or SOC Architect
- Continuing education pathways with advanced modules
- Accessing updated playbooks and detection rules quarterly
- Maintaining your certification through ongoing learning credits
- Completing the final assessment: deploy a live detection model
- Documenting your AI threat hunting framework
- Submitting for review and receiving your Certificate of Completion
- Verification process and digital badge issuance
- Adding the credential to LinkedIn and professional portfolios
- Leveraging the certification in salary negotiations and promotions
- Using your project as a capstone for internal presentations
- Accessing exclusive alumni resources and updates
- Joining a network of certified AI threat hunting practitioners
- Receiving invitations to private working groups and briefings
- Building a personal brand as an AI-savvy security leader
- Transitioning into roles such as Threat Hunter, Detection Engineer, or SOC Architect
- Continuing education pathways with advanced modules
- Accessing updated playbooks and detection rules quarterly
- Maintaining your certification through ongoing learning credits