Enterprise AI-Driven Security Automation for Network Professionals
You're not just managing a network. You're protecting a business-critical attack surface that grows more complex by the day. Legacy tools generate alerts, not answers. Manual triage slows response. Boardrooms demand measurable security ROI, not just compliance checklists. You’re expected to prevent breaches - yet lack the automation and AI clarity to act faster than attackers. Meanwhile, AI-powered threats are evolving in real time. Attackers use automation to exploit vulnerabilities within minutes. You're reacting, not anticipating. And if you’re still relying on traditional playbooks and static rules, you’re already behind. The gap isn’t your skills - it’s your systems. You need to shift from alert fatigue to intelligent, automated response - but knowing where to start with AI integration feels overwhelming, risky, and poorly documented. That’s why we built the Enterprise AI-Driven Security Automation for Network Professionals course. Not theory. Not buzzwords. This is a deployable, enterprise-grade framework that walks you step by step from fragmented security operations to a fully automated, AI-infused detection and response ecosystem - tailored for network infrastructure, firewalls, and traffic flows. By the end of this course, you’ll go from concept to a live, board-ready AI automation blueprint in under 30 days - complete with use case justification, ROI model, integration roadmap, and threat coverage metrics. One network security architect at a Fortune 500 financial services firm used this exact method to reduce false positives by 72% and cut mean time to respond from 4.2 hours to under 8 minutes. This isn’t about replacing your judgment. It’s about amplifying it. You’ll learn how to build AI models that learn your network’s behaviour, detect anomalies others miss, and initiate containment without human delay - all while maintaining full control, auditability, and compliance. We’ve removed the mystery, the vendor hype, and the trial-and-error. Every lesson is outcome-focused, grounded in real-world infrastructure patterns, and built for immediate application to your current environment. Here’s how this course is structured to help you get there.COURSE FORMAT & DELIVERY DETAILS Learn On Your Terms - No Deadlines, No Pressure
This course is self-paced, on-demand, and designed for busy network professionals. You set the schedule. Access all content instantly upon enrollment, with no fixed start or end dates, no live sessions, and no forced timelines. Most learners complete the core automation framework in 2–3 weeks with just 90 minutes per day, and report implementing their first AI-driven playbook within 14 days. Immediate, Lifetime Access - Anytime, Anywhere
Once enrolled, you gain immediate online access to all materials, with 24/7 global availability. All content is mobile-friendly and works seamlessly across tablets, laptops, and smartphones - perfect for reviewing during downtime, commutes, or incident response planning between shifts. Continuously Updated - Always Enterprise-Ready
You receive lifetime access to the course, including all future updates at no extra cost. As new AI models emerge, threat tactics evolve, and vendor integrations expand, your training evolves with them. This isn’t a one-time download - it’s a living, growing resource built for long-term relevance in high-stakes environments. Hands-On Learning with Expert Guidance
Each module includes guided implementation exercises, real-world case templates, and decision matrices used by enterprise teams. You’re not alone - you’ll have direct access to instructor-led support through a dedicated Q&A portal, where certified security automation engineers respond to technical queries with industry-specific context and best practice guidance. Certificate of Completion Issued by The Art of Service
Upon finishing the course, you earn a Certificate of Completion issued by The Art of Service - a globally recognised credential in IT governance and operational excellence, trusted by over 250,000 professionals across 140 countries. This certification demonstrates mastery of enterprise AI security automation and strengthens your credibility in job applications, promotions, and board-level discussions. Transparent Pricing - No Hidden Fees
The price you see is the price you pay. There are no upsells, no recurring charges, and no hidden fees. Access includes all materials, tools, templates, and certification - one time, forever. Accepted Payment Methods
We accept all major payment methods, including Visa, Mastercard, and PayPal, ensuring secure and flexible checkout no matter your location or preference. Zero-Risk Enrollment - 100% Satisfied or Refunded
We stand behind this course with an unconditional money-back guarantee. If you complete the first two modules and don’t believe this course will deliver measurable value to your career and organisation, simply request a full refund. No questions asked. Your investment is protected - so you can learn with confidence. Secure Enrollment Process
After enrollment, you will receive a confirmation email. Your access details and login instructions will be sent separately once your course materials are ready. This ensures a seamless, secure onboarding experience tailored to enterprise-grade account provisioning. This Works Even If…
- You’ve never worked with AI models or machine learning before
- Your organisation uses a mix of legacy and modern network infrastructure
- You’re not the decision-maker but need to build a compelling case for automation
- You’ve been burned by overhyped vendor solutions that failed to deliver
- Your team resists change and needs clear, low-risk implementation steps
You’ll learn how to start small, prove value fast, and scale securely - using frameworks already deployed in financial, healthcare, and critical infrastructure environments. Real practitioners in roles like yours - network security engineers, NOC managers, and infrastructure architects - have used this course to gain budget approval, lead automation projects, and position themselves as indispensable in their organisations.
EXTENSIVE and DETAILED COURSE CURRICULUM
Module 1: Foundations of AI-Driven Security in Enterprise Networks - Understanding the evolution of network threats and the AI advantage
- Key differences between traditional SIEM and AI-powered detection
- Core principles of autonomous security response
- The role of network telemetry in AI training pipelines
- Identifying high-impact areas for automation in network operations
- Mapping real-world attack chains to automation opportunities
- Evaluating organisational readiness for AI integration
- Establishing security, compliance, and audit boundaries
- Defining success metrics for AI-driven security initiatives
- Building stakeholder alignment across IT, security, and leadership
Module 2: Enterprise Threat Intelligence and AI Integration Frameworks - Architecting an AI-ready threat intelligence pipeline
- Classifying internal and external threat data sources
- Automated enrichment of IP, domain, and URL reputation feeds
- Integrating MITRE ATT&CK with AI model labelling
- Creating custom detection rules based on adversary behaviour
- Using AI to prioritise threat intelligence by relevance and risk
- Real-time correlation of threat feeds with network events
- Designing feedback loops for continuous intelligence refinement
- Evaluating commercial vs open-source threat intelligence platforms
- Building a centralised threat knowledge graph for AI consumption
Module 3: AI Model Fundamentals for Network Professionals - Demystifying machine learning for non-data scientists
- Understanding supervised, unsupervised, and reinforcement learning
- Selecting the right AI model type for network anomaly detection
- Feature engineering for network flow, packet, and log data
- Training data curation and bias mitigation strategies
- Model performance metrics: precision, recall, F1-score, AUC
- Interpreting model outputs in operational contexts
- Monitoring for model drift and performance decay
- Implementing retraining triggers based on network changes
- Validating model accuracy using historical breach data
Module 4: Network Data Acquisition and Preprocessing for AI - Collecting NetFlow, IPFIX, and sFlow at scale
- Extracting metadata from full packet captures without storage overload
- Normalising firewall, IDS, and proxy logs for AI ingestion
- Building unified event schemas across heterogeneous vendors
- Configuring real-time data pipelines using syslog, Kafka, or API feeds
- Applying data compression and retention policies for efficiency
- Using hashing and anonymisation for privacy compliance
- Automated data quality checks and pipeline monitoring
- Handling missing or corrupted data in AI training sets
- Creating synthetic data for rare attack scenarios
Module 5: Building AI-Powered Anomaly Detection Systems - Designing baselines for normal network behaviour
- Detecting deviations in traffic volume, protocol usage, and timing
- Identifying lateral movement through user and host clustering
- Spotting encrypted tunneling and covert exfiltration patterns
- Monitoring DNS tunneling and domain generation algorithms
- Using sequence modelling to detect multi-stage attacks
- Implementing unsupervised clustering for zero-day anomaly spotting
- Reducing false positives with contextual correlation filters
- Generating actionable alerts with confidence scoring
- Visualising anomaly trends for operational reporting
Module 6: Automated Playbook Design and Orchestration - Mapping detection outcomes to automated response actions
- Creating decision trees for conditional escalation and containment
- Designing human-in-the-loop controls for high-risk actions
- Integrating SOAR platforms with custom AI detection engines
- Automating firewall rule creation and revocation
- Scripting endpoint isolation via EDR and NAC integrations
- Blocking malicious IPs at the edge using BGP or DNS sinkholes
- Sending auto-generated incident reports to ticketing systems
- Validating automation effectiveness through red team emulation
- Building rollback procedures for failed automation steps
Module 7: AI-Driven Firewall and Routing Automation - Dynamic firewall policy updates based on real-time threat feeds
- Automated geoblocking of traffic from high-risk regions
- Detecting and blocking port scanning and brute force attacks
- Adaptive segmentation using user and device risk scores
- AI-based QoS adjustments during DDoS mitigation
- Automated failover and routing changes during network attacks
- Monitoring for BGP hijacking and route leaks
- Using AI to predict and prevent policy misconfigurations
- Integrating cloud-native firewalls with on-prem automation
- Generating compliance reports for audit-ready firewall changes
Module 8: Zero Trust Integration with AI Security Automation - Aligning AI detection with Zero Trust policy enforcement
- Dynamic access control based on behavioural analytics
- Automating device trust scoring and continuous authentication
- Detecting compromised identities through access pattern anomalies
- Triggering MFA challenges in response to risky behaviour
- Integrating with identity providers like Okta and Azure AD
- Automated session termination for suspicious logins
- Enforcing least privilege using AI-identified roles
- Monitoring for API abuse and excessive data access
- Generating user activity summaries for policy refinement
Module 9: Cloud and Hybrid Network Security Automation - Extending AI automation across AWS, Azure, and GCP
- Monitoring VPC flow logs for policy violations
- Detecting public S3 bucket exposure and IAM misconfigurations
- Automating cloud security group updates based on threat context
- Responding to crypto-mining and unauthorised VM launches
- Integrating with cloud-native logging and monitoring tools
- Detecting workload impersonation and token theft
- Automating hybrid DNS and DHCP policy enforcement
- Orchestrating responses across multi-cloud and on-prem environments
- Building unified visibility dashboards for hybrid threat hunting
Module 10: AI for Encrypted Traffic Analysis - Performing TLS fingerprinting for application and threat identification
- Detecting malware communication using encrypted traffic patterns
- Analysing certificate anomalies and self-signed certificate abuse
- Using JA3 and JA3S hashes for client and server fingerprinting
- Identifying shadow IT through unapproved encrypted services
- Detecting data exfiltration via encrypted channels
- Monitoring for unusual cipher suite usage and downgrade attacks
- Correlating encrypted traffic with user and device context
- Automating inspection rules for high-risk encrypted sessions
- Integrating with SSL decryption gateways and forward proxies
Module 11: Automated Incident Response and Forensics - Building AI-assisted triage workflows for SOC teams
- Automatically classifying incidents by severity and scope
- Collecting and preserving digital evidence at first detection
- Running automated memory and disk acquisition scripts
- Correlating events across endpoints, network, and cloud
- Generating timeline reconstructions of attack sequences
- Using AI to predict attacker objectives and next steps
- Automating legal hold notifications for forensic data
- Creating standardised reporting templates for leadership
- Integrating with digital forensics platforms like Autopsy and FTK
Module 12: AI-Enhanced Vulnerability Management - Automating vulnerability scanning triggers based on asset changes
- Prioritising patching using AI-driven risk scoring
- Correlating CVE data with real-world exploitation trends
- Detecting unpatched systems through passive traffic observation
- Automating patch deployment workflows for critical assets
- Using AI to predict which vulnerabilities will be exploited next
- Integrating with vulnerability scanners like Nessus and Qualys
- Reducing scan impact on production networks with adaptive scheduling
- Generating compliance evidence for audit requirements
- Creating executive summaries of vulnerability exposure trends
Module 13: AI for Insider Threat Detection - Establishing baseline user access and data usage patterns
- Detecting unauthorised data downloads and bulk transfers
- Monitoring for after-hours access and off-network logins
- Correlating email, file share, and network activity logs
- Detecting credential dumping and privilege escalation attempts
- Identifying risky third-party vendor access patterns
- Using AI to detect policy violations without surveillance
- Integrating with HR systems for offboarding automation
- Automating alerts to data owners and compliance teams
- Conducting ethical monitoring with legal and privacy safeguards
Module 14: Infrastructure as Code and AI Security - Automating security checks in CI/CD pipelines
- Scanning Terraform and CloudFormation templates for risks
- Detecting hard-coded secrets and credentials in code
- Enforcing secure network configuration patterns
- Integrating AI with Git-based change management
- Automating drift detection and policy enforcement
- Generating real-time compliance reports for infrastructure
- Validating network designs before deployment
- Using AI to recommend optimised security group rules
- Building self-healing infrastructure configurations
Module 15: AI-Driven DDoS Detection and Mitigation - Identifying volumetric, protocol, and application-layer DDoS attacks
- Using AI to distinguish attacks from legitimate traffic spikes
- Automating traffic diversion to scrubbing centres
- Implementing rate limiting based on real-time threat context
- Detecting slow-rate and low-and-slow DDoS variants
- Monitoring for amplification attacks using DNS and NTP
- Coordinating mitigation across cloud and on-prem networks
- Automating post-attack forensics and clean-up
- Integrating with cloud DDoS protection services
- Generating business impact reports for executive review
Module 16: AI for Wireless and IoT Network Security - Mapping AI detection to rogue access points and ad hoc networks
- Detecting device impersonation and MAC spoofing
- Monitoring for unauthorised IoT device onboarding
- Automating wireless client isolation based on risk
- Analysing beacon frame anomalies for wireless attacks
- Identifying sleep-deprivation attacks on smart devices
- Correlating IoT traffic with operational technology systems
- Detecting firmware update tampering and supply chain risks
- Automating device revocation and quarantine procedures
- Generating asset inventory reports for IoT governance
Module 17: Building Your Board-Ready AI Automation Proposal - Defining the business case for AI security automation
- Calculating ROI using reduced incident response times
- Quantifying risk reduction through faster containment
- Estimating cost savings from reduced SOC workload
- Aligning automation goals with business continuity objectives
- Creating visual dashboards for leadership communication
- Developing pilot project plans with clear KPIs
- Identifying quick wins to build internal momentum
- Addressing leadership concerns about AI reliability
- Presenting your final certification project as a strategic initiative
Module 1: Foundations of AI-Driven Security in Enterprise Networks - Understanding the evolution of network threats and the AI advantage
- Key differences between traditional SIEM and AI-powered detection
- Core principles of autonomous security response
- The role of network telemetry in AI training pipelines
- Identifying high-impact areas for automation in network operations
- Mapping real-world attack chains to automation opportunities
- Evaluating organisational readiness for AI integration
- Establishing security, compliance, and audit boundaries
- Defining success metrics for AI-driven security initiatives
- Building stakeholder alignment across IT, security, and leadership
Module 2: Enterprise Threat Intelligence and AI Integration Frameworks - Architecting an AI-ready threat intelligence pipeline
- Classifying internal and external threat data sources
- Automated enrichment of IP, domain, and URL reputation feeds
- Integrating MITRE ATT&CK with AI model labelling
- Creating custom detection rules based on adversary behaviour
- Using AI to prioritise threat intelligence by relevance and risk
- Real-time correlation of threat feeds with network events
- Designing feedback loops for continuous intelligence refinement
- Evaluating commercial vs open-source threat intelligence platforms
- Building a centralised threat knowledge graph for AI consumption
Module 3: AI Model Fundamentals for Network Professionals - Demystifying machine learning for non-data scientists
- Understanding supervised, unsupervised, and reinforcement learning
- Selecting the right AI model type for network anomaly detection
- Feature engineering for network flow, packet, and log data
- Training data curation and bias mitigation strategies
- Model performance metrics: precision, recall, F1-score, AUC
- Interpreting model outputs in operational contexts
- Monitoring for model drift and performance decay
- Implementing retraining triggers based on network changes
- Validating model accuracy using historical breach data
Module 4: Network Data Acquisition and Preprocessing for AI - Collecting NetFlow, IPFIX, and sFlow at scale
- Extracting metadata from full packet captures without storage overload
- Normalising firewall, IDS, and proxy logs for AI ingestion
- Building unified event schemas across heterogeneous vendors
- Configuring real-time data pipelines using syslog, Kafka, or API feeds
- Applying data compression and retention policies for efficiency
- Using hashing and anonymisation for privacy compliance
- Automated data quality checks and pipeline monitoring
- Handling missing or corrupted data in AI training sets
- Creating synthetic data for rare attack scenarios
Module 5: Building AI-Powered Anomaly Detection Systems - Designing baselines for normal network behaviour
- Detecting deviations in traffic volume, protocol usage, and timing
- Identifying lateral movement through user and host clustering
- Spotting encrypted tunneling and covert exfiltration patterns
- Monitoring DNS tunneling and domain generation algorithms
- Using sequence modelling to detect multi-stage attacks
- Implementing unsupervised clustering for zero-day anomaly spotting
- Reducing false positives with contextual correlation filters
- Generating actionable alerts with confidence scoring
- Visualising anomaly trends for operational reporting
Module 6: Automated Playbook Design and Orchestration - Mapping detection outcomes to automated response actions
- Creating decision trees for conditional escalation and containment
- Designing human-in-the-loop controls for high-risk actions
- Integrating SOAR platforms with custom AI detection engines
- Automating firewall rule creation and revocation
- Scripting endpoint isolation via EDR and NAC integrations
- Blocking malicious IPs at the edge using BGP or DNS sinkholes
- Sending auto-generated incident reports to ticketing systems
- Validating automation effectiveness through red team emulation
- Building rollback procedures for failed automation steps
Module 7: AI-Driven Firewall and Routing Automation - Dynamic firewall policy updates based on real-time threat feeds
- Automated geoblocking of traffic from high-risk regions
- Detecting and blocking port scanning and brute force attacks
- Adaptive segmentation using user and device risk scores
- AI-based QoS adjustments during DDoS mitigation
- Automated failover and routing changes during network attacks
- Monitoring for BGP hijacking and route leaks
- Using AI to predict and prevent policy misconfigurations
- Integrating cloud-native firewalls with on-prem automation
- Generating compliance reports for audit-ready firewall changes
Module 8: Zero Trust Integration with AI Security Automation - Aligning AI detection with Zero Trust policy enforcement
- Dynamic access control based on behavioural analytics
- Automating device trust scoring and continuous authentication
- Detecting compromised identities through access pattern anomalies
- Triggering MFA challenges in response to risky behaviour
- Integrating with identity providers like Okta and Azure AD
- Automated session termination for suspicious logins
- Enforcing least privilege using AI-identified roles
- Monitoring for API abuse and excessive data access
- Generating user activity summaries for policy refinement
Module 9: Cloud and Hybrid Network Security Automation - Extending AI automation across AWS, Azure, and GCP
- Monitoring VPC flow logs for policy violations
- Detecting public S3 bucket exposure and IAM misconfigurations
- Automating cloud security group updates based on threat context
- Responding to crypto-mining and unauthorised VM launches
- Integrating with cloud-native logging and monitoring tools
- Detecting workload impersonation and token theft
- Automating hybrid DNS and DHCP policy enforcement
- Orchestrating responses across multi-cloud and on-prem environments
- Building unified visibility dashboards for hybrid threat hunting
Module 10: AI for Encrypted Traffic Analysis - Performing TLS fingerprinting for application and threat identification
- Detecting malware communication using encrypted traffic patterns
- Analysing certificate anomalies and self-signed certificate abuse
- Using JA3 and JA3S hashes for client and server fingerprinting
- Identifying shadow IT through unapproved encrypted services
- Detecting data exfiltration via encrypted channels
- Monitoring for unusual cipher suite usage and downgrade attacks
- Correlating encrypted traffic with user and device context
- Automating inspection rules for high-risk encrypted sessions
- Integrating with SSL decryption gateways and forward proxies
Module 11: Automated Incident Response and Forensics - Building AI-assisted triage workflows for SOC teams
- Automatically classifying incidents by severity and scope
- Collecting and preserving digital evidence at first detection
- Running automated memory and disk acquisition scripts
- Correlating events across endpoints, network, and cloud
- Generating timeline reconstructions of attack sequences
- Using AI to predict attacker objectives and next steps
- Automating legal hold notifications for forensic data
- Creating standardised reporting templates for leadership
- Integrating with digital forensics platforms like Autopsy and FTK
Module 12: AI-Enhanced Vulnerability Management - Automating vulnerability scanning triggers based on asset changes
- Prioritising patching using AI-driven risk scoring
- Correlating CVE data with real-world exploitation trends
- Detecting unpatched systems through passive traffic observation
- Automating patch deployment workflows for critical assets
- Using AI to predict which vulnerabilities will be exploited next
- Integrating with vulnerability scanners like Nessus and Qualys
- Reducing scan impact on production networks with adaptive scheduling
- Generating compliance evidence for audit requirements
- Creating executive summaries of vulnerability exposure trends
Module 13: AI for Insider Threat Detection - Establishing baseline user access and data usage patterns
- Detecting unauthorised data downloads and bulk transfers
- Monitoring for after-hours access and off-network logins
- Correlating email, file share, and network activity logs
- Detecting credential dumping and privilege escalation attempts
- Identifying risky third-party vendor access patterns
- Using AI to detect policy violations without surveillance
- Integrating with HR systems for offboarding automation
- Automating alerts to data owners and compliance teams
- Conducting ethical monitoring with legal and privacy safeguards
Module 14: Infrastructure as Code and AI Security - Automating security checks in CI/CD pipelines
- Scanning Terraform and CloudFormation templates for risks
- Detecting hard-coded secrets and credentials in code
- Enforcing secure network configuration patterns
- Integrating AI with Git-based change management
- Automating drift detection and policy enforcement
- Generating real-time compliance reports for infrastructure
- Validating network designs before deployment
- Using AI to recommend optimised security group rules
- Building self-healing infrastructure configurations
Module 15: AI-Driven DDoS Detection and Mitigation - Identifying volumetric, protocol, and application-layer DDoS attacks
- Using AI to distinguish attacks from legitimate traffic spikes
- Automating traffic diversion to scrubbing centres
- Implementing rate limiting based on real-time threat context
- Detecting slow-rate and low-and-slow DDoS variants
- Monitoring for amplification attacks using DNS and NTP
- Coordinating mitigation across cloud and on-prem networks
- Automating post-attack forensics and clean-up
- Integrating with cloud DDoS protection services
- Generating business impact reports for executive review
Module 16: AI for Wireless and IoT Network Security - Mapping AI detection to rogue access points and ad hoc networks
- Detecting device impersonation and MAC spoofing
- Monitoring for unauthorised IoT device onboarding
- Automating wireless client isolation based on risk
- Analysing beacon frame anomalies for wireless attacks
- Identifying sleep-deprivation attacks on smart devices
- Correlating IoT traffic with operational technology systems
- Detecting firmware update tampering and supply chain risks
- Automating device revocation and quarantine procedures
- Generating asset inventory reports for IoT governance
Module 17: Building Your Board-Ready AI Automation Proposal - Defining the business case for AI security automation
- Calculating ROI using reduced incident response times
- Quantifying risk reduction through faster containment
- Estimating cost savings from reduced SOC workload
- Aligning automation goals with business continuity objectives
- Creating visual dashboards for leadership communication
- Developing pilot project plans with clear KPIs
- Identifying quick wins to build internal momentum
- Addressing leadership concerns about AI reliability
- Presenting your final certification project as a strategic initiative
- Architecting an AI-ready threat intelligence pipeline
- Classifying internal and external threat data sources
- Automated enrichment of IP, domain, and URL reputation feeds
- Integrating MITRE ATT&CK with AI model labelling
- Creating custom detection rules based on adversary behaviour
- Using AI to prioritise threat intelligence by relevance and risk
- Real-time correlation of threat feeds with network events
- Designing feedback loops for continuous intelligence refinement
- Evaluating commercial vs open-source threat intelligence platforms
- Building a centralised threat knowledge graph for AI consumption
Module 3: AI Model Fundamentals for Network Professionals - Demystifying machine learning for non-data scientists
- Understanding supervised, unsupervised, and reinforcement learning
- Selecting the right AI model type for network anomaly detection
- Feature engineering for network flow, packet, and log data
- Training data curation and bias mitigation strategies
- Model performance metrics: precision, recall, F1-score, AUC
- Interpreting model outputs in operational contexts
- Monitoring for model drift and performance decay
- Implementing retraining triggers based on network changes
- Validating model accuracy using historical breach data
Module 4: Network Data Acquisition and Preprocessing for AI - Collecting NetFlow, IPFIX, and sFlow at scale
- Extracting metadata from full packet captures without storage overload
- Normalising firewall, IDS, and proxy logs for AI ingestion
- Building unified event schemas across heterogeneous vendors
- Configuring real-time data pipelines using syslog, Kafka, or API feeds
- Applying data compression and retention policies for efficiency
- Using hashing and anonymisation for privacy compliance
- Automated data quality checks and pipeline monitoring
- Handling missing or corrupted data in AI training sets
- Creating synthetic data for rare attack scenarios
Module 5: Building AI-Powered Anomaly Detection Systems - Designing baselines for normal network behaviour
- Detecting deviations in traffic volume, protocol usage, and timing
- Identifying lateral movement through user and host clustering
- Spotting encrypted tunneling and covert exfiltration patterns
- Monitoring DNS tunneling and domain generation algorithms
- Using sequence modelling to detect multi-stage attacks
- Implementing unsupervised clustering for zero-day anomaly spotting
- Reducing false positives with contextual correlation filters
- Generating actionable alerts with confidence scoring
- Visualising anomaly trends for operational reporting
Module 6: Automated Playbook Design and Orchestration - Mapping detection outcomes to automated response actions
- Creating decision trees for conditional escalation and containment
- Designing human-in-the-loop controls for high-risk actions
- Integrating SOAR platforms with custom AI detection engines
- Automating firewall rule creation and revocation
- Scripting endpoint isolation via EDR and NAC integrations
- Blocking malicious IPs at the edge using BGP or DNS sinkholes
- Sending auto-generated incident reports to ticketing systems
- Validating automation effectiveness through red team emulation
- Building rollback procedures for failed automation steps
Module 7: AI-Driven Firewall and Routing Automation - Dynamic firewall policy updates based on real-time threat feeds
- Automated geoblocking of traffic from high-risk regions
- Detecting and blocking port scanning and brute force attacks
- Adaptive segmentation using user and device risk scores
- AI-based QoS adjustments during DDoS mitigation
- Automated failover and routing changes during network attacks
- Monitoring for BGP hijacking and route leaks
- Using AI to predict and prevent policy misconfigurations
- Integrating cloud-native firewalls with on-prem automation
- Generating compliance reports for audit-ready firewall changes
Module 8: Zero Trust Integration with AI Security Automation - Aligning AI detection with Zero Trust policy enforcement
- Dynamic access control based on behavioural analytics
- Automating device trust scoring and continuous authentication
- Detecting compromised identities through access pattern anomalies
- Triggering MFA challenges in response to risky behaviour
- Integrating with identity providers like Okta and Azure AD
- Automated session termination for suspicious logins
- Enforcing least privilege using AI-identified roles
- Monitoring for API abuse and excessive data access
- Generating user activity summaries for policy refinement
Module 9: Cloud and Hybrid Network Security Automation - Extending AI automation across AWS, Azure, and GCP
- Monitoring VPC flow logs for policy violations
- Detecting public S3 bucket exposure and IAM misconfigurations
- Automating cloud security group updates based on threat context
- Responding to crypto-mining and unauthorised VM launches
- Integrating with cloud-native logging and monitoring tools
- Detecting workload impersonation and token theft
- Automating hybrid DNS and DHCP policy enforcement
- Orchestrating responses across multi-cloud and on-prem environments
- Building unified visibility dashboards for hybrid threat hunting
Module 10: AI for Encrypted Traffic Analysis - Performing TLS fingerprinting for application and threat identification
- Detecting malware communication using encrypted traffic patterns
- Analysing certificate anomalies and self-signed certificate abuse
- Using JA3 and JA3S hashes for client and server fingerprinting
- Identifying shadow IT through unapproved encrypted services
- Detecting data exfiltration via encrypted channels
- Monitoring for unusual cipher suite usage and downgrade attacks
- Correlating encrypted traffic with user and device context
- Automating inspection rules for high-risk encrypted sessions
- Integrating with SSL decryption gateways and forward proxies
Module 11: Automated Incident Response and Forensics - Building AI-assisted triage workflows for SOC teams
- Automatically classifying incidents by severity and scope
- Collecting and preserving digital evidence at first detection
- Running automated memory and disk acquisition scripts
- Correlating events across endpoints, network, and cloud
- Generating timeline reconstructions of attack sequences
- Using AI to predict attacker objectives and next steps
- Automating legal hold notifications for forensic data
- Creating standardised reporting templates for leadership
- Integrating with digital forensics platforms like Autopsy and FTK
Module 12: AI-Enhanced Vulnerability Management - Automating vulnerability scanning triggers based on asset changes
- Prioritising patching using AI-driven risk scoring
- Correlating CVE data with real-world exploitation trends
- Detecting unpatched systems through passive traffic observation
- Automating patch deployment workflows for critical assets
- Using AI to predict which vulnerabilities will be exploited next
- Integrating with vulnerability scanners like Nessus and Qualys
- Reducing scan impact on production networks with adaptive scheduling
- Generating compliance evidence for audit requirements
- Creating executive summaries of vulnerability exposure trends
Module 13: AI for Insider Threat Detection - Establishing baseline user access and data usage patterns
- Detecting unauthorised data downloads and bulk transfers
- Monitoring for after-hours access and off-network logins
- Correlating email, file share, and network activity logs
- Detecting credential dumping and privilege escalation attempts
- Identifying risky third-party vendor access patterns
- Using AI to detect policy violations without surveillance
- Integrating with HR systems for offboarding automation
- Automating alerts to data owners and compliance teams
- Conducting ethical monitoring with legal and privacy safeguards
Module 14: Infrastructure as Code and AI Security - Automating security checks in CI/CD pipelines
- Scanning Terraform and CloudFormation templates for risks
- Detecting hard-coded secrets and credentials in code
- Enforcing secure network configuration patterns
- Integrating AI with Git-based change management
- Automating drift detection and policy enforcement
- Generating real-time compliance reports for infrastructure
- Validating network designs before deployment
- Using AI to recommend optimised security group rules
- Building self-healing infrastructure configurations
Module 15: AI-Driven DDoS Detection and Mitigation - Identifying volumetric, protocol, and application-layer DDoS attacks
- Using AI to distinguish attacks from legitimate traffic spikes
- Automating traffic diversion to scrubbing centres
- Implementing rate limiting based on real-time threat context
- Detecting slow-rate and low-and-slow DDoS variants
- Monitoring for amplification attacks using DNS and NTP
- Coordinating mitigation across cloud and on-prem networks
- Automating post-attack forensics and clean-up
- Integrating with cloud DDoS protection services
- Generating business impact reports for executive review
Module 16: AI for Wireless and IoT Network Security - Mapping AI detection to rogue access points and ad hoc networks
- Detecting device impersonation and MAC spoofing
- Monitoring for unauthorised IoT device onboarding
- Automating wireless client isolation based on risk
- Analysing beacon frame anomalies for wireless attacks
- Identifying sleep-deprivation attacks on smart devices
- Correlating IoT traffic with operational technology systems
- Detecting firmware update tampering and supply chain risks
- Automating device revocation and quarantine procedures
- Generating asset inventory reports for IoT governance
Module 17: Building Your Board-Ready AI Automation Proposal - Defining the business case for AI security automation
- Calculating ROI using reduced incident response times
- Quantifying risk reduction through faster containment
- Estimating cost savings from reduced SOC workload
- Aligning automation goals with business continuity objectives
- Creating visual dashboards for leadership communication
- Developing pilot project plans with clear KPIs
- Identifying quick wins to build internal momentum
- Addressing leadership concerns about AI reliability
- Presenting your final certification project as a strategic initiative
- Collecting NetFlow, IPFIX, and sFlow at scale
- Extracting metadata from full packet captures without storage overload
- Normalising firewall, IDS, and proxy logs for AI ingestion
- Building unified event schemas across heterogeneous vendors
- Configuring real-time data pipelines using syslog, Kafka, or API feeds
- Applying data compression and retention policies for efficiency
- Using hashing and anonymisation for privacy compliance
- Automated data quality checks and pipeline monitoring
- Handling missing or corrupted data in AI training sets
- Creating synthetic data for rare attack scenarios
Module 5: Building AI-Powered Anomaly Detection Systems - Designing baselines for normal network behaviour
- Detecting deviations in traffic volume, protocol usage, and timing
- Identifying lateral movement through user and host clustering
- Spotting encrypted tunneling and covert exfiltration patterns
- Monitoring DNS tunneling and domain generation algorithms
- Using sequence modelling to detect multi-stage attacks
- Implementing unsupervised clustering for zero-day anomaly spotting
- Reducing false positives with contextual correlation filters
- Generating actionable alerts with confidence scoring
- Visualising anomaly trends for operational reporting
Module 6: Automated Playbook Design and Orchestration - Mapping detection outcomes to automated response actions
- Creating decision trees for conditional escalation and containment
- Designing human-in-the-loop controls for high-risk actions
- Integrating SOAR platforms with custom AI detection engines
- Automating firewall rule creation and revocation
- Scripting endpoint isolation via EDR and NAC integrations
- Blocking malicious IPs at the edge using BGP or DNS sinkholes
- Sending auto-generated incident reports to ticketing systems
- Validating automation effectiveness through red team emulation
- Building rollback procedures for failed automation steps
Module 7: AI-Driven Firewall and Routing Automation - Dynamic firewall policy updates based on real-time threat feeds
- Automated geoblocking of traffic from high-risk regions
- Detecting and blocking port scanning and brute force attacks
- Adaptive segmentation using user and device risk scores
- AI-based QoS adjustments during DDoS mitigation
- Automated failover and routing changes during network attacks
- Monitoring for BGP hijacking and route leaks
- Using AI to predict and prevent policy misconfigurations
- Integrating cloud-native firewalls with on-prem automation
- Generating compliance reports for audit-ready firewall changes
Module 8: Zero Trust Integration with AI Security Automation - Aligning AI detection with Zero Trust policy enforcement
- Dynamic access control based on behavioural analytics
- Automating device trust scoring and continuous authentication
- Detecting compromised identities through access pattern anomalies
- Triggering MFA challenges in response to risky behaviour
- Integrating with identity providers like Okta and Azure AD
- Automated session termination for suspicious logins
- Enforcing least privilege using AI-identified roles
- Monitoring for API abuse and excessive data access
- Generating user activity summaries for policy refinement
Module 9: Cloud and Hybrid Network Security Automation - Extending AI automation across AWS, Azure, and GCP
- Monitoring VPC flow logs for policy violations
- Detecting public S3 bucket exposure and IAM misconfigurations
- Automating cloud security group updates based on threat context
- Responding to crypto-mining and unauthorised VM launches
- Integrating with cloud-native logging and monitoring tools
- Detecting workload impersonation and token theft
- Automating hybrid DNS and DHCP policy enforcement
- Orchestrating responses across multi-cloud and on-prem environments
- Building unified visibility dashboards for hybrid threat hunting
Module 10: AI for Encrypted Traffic Analysis - Performing TLS fingerprinting for application and threat identification
- Detecting malware communication using encrypted traffic patterns
- Analysing certificate anomalies and self-signed certificate abuse
- Using JA3 and JA3S hashes for client and server fingerprinting
- Identifying shadow IT through unapproved encrypted services
- Detecting data exfiltration via encrypted channels
- Monitoring for unusual cipher suite usage and downgrade attacks
- Correlating encrypted traffic with user and device context
- Automating inspection rules for high-risk encrypted sessions
- Integrating with SSL decryption gateways and forward proxies
Module 11: Automated Incident Response and Forensics - Building AI-assisted triage workflows for SOC teams
- Automatically classifying incidents by severity and scope
- Collecting and preserving digital evidence at first detection
- Running automated memory and disk acquisition scripts
- Correlating events across endpoints, network, and cloud
- Generating timeline reconstructions of attack sequences
- Using AI to predict attacker objectives and next steps
- Automating legal hold notifications for forensic data
- Creating standardised reporting templates for leadership
- Integrating with digital forensics platforms like Autopsy and FTK
Module 12: AI-Enhanced Vulnerability Management - Automating vulnerability scanning triggers based on asset changes
- Prioritising patching using AI-driven risk scoring
- Correlating CVE data with real-world exploitation trends
- Detecting unpatched systems through passive traffic observation
- Automating patch deployment workflows for critical assets
- Using AI to predict which vulnerabilities will be exploited next
- Integrating with vulnerability scanners like Nessus and Qualys
- Reducing scan impact on production networks with adaptive scheduling
- Generating compliance evidence for audit requirements
- Creating executive summaries of vulnerability exposure trends
Module 13: AI for Insider Threat Detection - Establishing baseline user access and data usage patterns
- Detecting unauthorised data downloads and bulk transfers
- Monitoring for after-hours access and off-network logins
- Correlating email, file share, and network activity logs
- Detecting credential dumping and privilege escalation attempts
- Identifying risky third-party vendor access patterns
- Using AI to detect policy violations without surveillance
- Integrating with HR systems for offboarding automation
- Automating alerts to data owners and compliance teams
- Conducting ethical monitoring with legal and privacy safeguards
Module 14: Infrastructure as Code and AI Security - Automating security checks in CI/CD pipelines
- Scanning Terraform and CloudFormation templates for risks
- Detecting hard-coded secrets and credentials in code
- Enforcing secure network configuration patterns
- Integrating AI with Git-based change management
- Automating drift detection and policy enforcement
- Generating real-time compliance reports for infrastructure
- Validating network designs before deployment
- Using AI to recommend optimised security group rules
- Building self-healing infrastructure configurations
Module 15: AI-Driven DDoS Detection and Mitigation - Identifying volumetric, protocol, and application-layer DDoS attacks
- Using AI to distinguish attacks from legitimate traffic spikes
- Automating traffic diversion to scrubbing centres
- Implementing rate limiting based on real-time threat context
- Detecting slow-rate and low-and-slow DDoS variants
- Monitoring for amplification attacks using DNS and NTP
- Coordinating mitigation across cloud and on-prem networks
- Automating post-attack forensics and clean-up
- Integrating with cloud DDoS protection services
- Generating business impact reports for executive review
Module 16: AI for Wireless and IoT Network Security - Mapping AI detection to rogue access points and ad hoc networks
- Detecting device impersonation and MAC spoofing
- Monitoring for unauthorised IoT device onboarding
- Automating wireless client isolation based on risk
- Analysing beacon frame anomalies for wireless attacks
- Identifying sleep-deprivation attacks on smart devices
- Correlating IoT traffic with operational technology systems
- Detecting firmware update tampering and supply chain risks
- Automating device revocation and quarantine procedures
- Generating asset inventory reports for IoT governance
Module 17: Building Your Board-Ready AI Automation Proposal - Defining the business case for AI security automation
- Calculating ROI using reduced incident response times
- Quantifying risk reduction through faster containment
- Estimating cost savings from reduced SOC workload
- Aligning automation goals with business continuity objectives
- Creating visual dashboards for leadership communication
- Developing pilot project plans with clear KPIs
- Identifying quick wins to build internal momentum
- Addressing leadership concerns about AI reliability
- Presenting your final certification project as a strategic initiative
- Mapping detection outcomes to automated response actions
- Creating decision trees for conditional escalation and containment
- Designing human-in-the-loop controls for high-risk actions
- Integrating SOAR platforms with custom AI detection engines
- Automating firewall rule creation and revocation
- Scripting endpoint isolation via EDR and NAC integrations
- Blocking malicious IPs at the edge using BGP or DNS sinkholes
- Sending auto-generated incident reports to ticketing systems
- Validating automation effectiveness through red team emulation
- Building rollback procedures for failed automation steps
Module 7: AI-Driven Firewall and Routing Automation - Dynamic firewall policy updates based on real-time threat feeds
- Automated geoblocking of traffic from high-risk regions
- Detecting and blocking port scanning and brute force attacks
- Adaptive segmentation using user and device risk scores
- AI-based QoS adjustments during DDoS mitigation
- Automated failover and routing changes during network attacks
- Monitoring for BGP hijacking and route leaks
- Using AI to predict and prevent policy misconfigurations
- Integrating cloud-native firewalls with on-prem automation
- Generating compliance reports for audit-ready firewall changes
Module 8: Zero Trust Integration with AI Security Automation - Aligning AI detection with Zero Trust policy enforcement
- Dynamic access control based on behavioural analytics
- Automating device trust scoring and continuous authentication
- Detecting compromised identities through access pattern anomalies
- Triggering MFA challenges in response to risky behaviour
- Integrating with identity providers like Okta and Azure AD
- Automated session termination for suspicious logins
- Enforcing least privilege using AI-identified roles
- Monitoring for API abuse and excessive data access
- Generating user activity summaries for policy refinement
Module 9: Cloud and Hybrid Network Security Automation - Extending AI automation across AWS, Azure, and GCP
- Monitoring VPC flow logs for policy violations
- Detecting public S3 bucket exposure and IAM misconfigurations
- Automating cloud security group updates based on threat context
- Responding to crypto-mining and unauthorised VM launches
- Integrating with cloud-native logging and monitoring tools
- Detecting workload impersonation and token theft
- Automating hybrid DNS and DHCP policy enforcement
- Orchestrating responses across multi-cloud and on-prem environments
- Building unified visibility dashboards for hybrid threat hunting
Module 10: AI for Encrypted Traffic Analysis - Performing TLS fingerprinting for application and threat identification
- Detecting malware communication using encrypted traffic patterns
- Analysing certificate anomalies and self-signed certificate abuse
- Using JA3 and JA3S hashes for client and server fingerprinting
- Identifying shadow IT through unapproved encrypted services
- Detecting data exfiltration via encrypted channels
- Monitoring for unusual cipher suite usage and downgrade attacks
- Correlating encrypted traffic with user and device context
- Automating inspection rules for high-risk encrypted sessions
- Integrating with SSL decryption gateways and forward proxies
Module 11: Automated Incident Response and Forensics - Building AI-assisted triage workflows for SOC teams
- Automatically classifying incidents by severity and scope
- Collecting and preserving digital evidence at first detection
- Running automated memory and disk acquisition scripts
- Correlating events across endpoints, network, and cloud
- Generating timeline reconstructions of attack sequences
- Using AI to predict attacker objectives and next steps
- Automating legal hold notifications for forensic data
- Creating standardised reporting templates for leadership
- Integrating with digital forensics platforms like Autopsy and FTK
Module 12: AI-Enhanced Vulnerability Management - Automating vulnerability scanning triggers based on asset changes
- Prioritising patching using AI-driven risk scoring
- Correlating CVE data with real-world exploitation trends
- Detecting unpatched systems through passive traffic observation
- Automating patch deployment workflows for critical assets
- Using AI to predict which vulnerabilities will be exploited next
- Integrating with vulnerability scanners like Nessus and Qualys
- Reducing scan impact on production networks with adaptive scheduling
- Generating compliance evidence for audit requirements
- Creating executive summaries of vulnerability exposure trends
Module 13: AI for Insider Threat Detection - Establishing baseline user access and data usage patterns
- Detecting unauthorised data downloads and bulk transfers
- Monitoring for after-hours access and off-network logins
- Correlating email, file share, and network activity logs
- Detecting credential dumping and privilege escalation attempts
- Identifying risky third-party vendor access patterns
- Using AI to detect policy violations without surveillance
- Integrating with HR systems for offboarding automation
- Automating alerts to data owners and compliance teams
- Conducting ethical monitoring with legal and privacy safeguards
Module 14: Infrastructure as Code and AI Security - Automating security checks in CI/CD pipelines
- Scanning Terraform and CloudFormation templates for risks
- Detecting hard-coded secrets and credentials in code
- Enforcing secure network configuration patterns
- Integrating AI with Git-based change management
- Automating drift detection and policy enforcement
- Generating real-time compliance reports for infrastructure
- Validating network designs before deployment
- Using AI to recommend optimised security group rules
- Building self-healing infrastructure configurations
Module 15: AI-Driven DDoS Detection and Mitigation - Identifying volumetric, protocol, and application-layer DDoS attacks
- Using AI to distinguish attacks from legitimate traffic spikes
- Automating traffic diversion to scrubbing centres
- Implementing rate limiting based on real-time threat context
- Detecting slow-rate and low-and-slow DDoS variants
- Monitoring for amplification attacks using DNS and NTP
- Coordinating mitigation across cloud and on-prem networks
- Automating post-attack forensics and clean-up
- Integrating with cloud DDoS protection services
- Generating business impact reports for executive review
Module 16: AI for Wireless and IoT Network Security - Mapping AI detection to rogue access points and ad hoc networks
- Detecting device impersonation and MAC spoofing
- Monitoring for unauthorised IoT device onboarding
- Automating wireless client isolation based on risk
- Analysing beacon frame anomalies for wireless attacks
- Identifying sleep-deprivation attacks on smart devices
- Correlating IoT traffic with operational technology systems
- Detecting firmware update tampering and supply chain risks
- Automating device revocation and quarantine procedures
- Generating asset inventory reports for IoT governance
Module 17: Building Your Board-Ready AI Automation Proposal - Defining the business case for AI security automation
- Calculating ROI using reduced incident response times
- Quantifying risk reduction through faster containment
- Estimating cost savings from reduced SOC workload
- Aligning automation goals with business continuity objectives
- Creating visual dashboards for leadership communication
- Developing pilot project plans with clear KPIs
- Identifying quick wins to build internal momentum
- Addressing leadership concerns about AI reliability
- Presenting your final certification project as a strategic initiative
- Aligning AI detection with Zero Trust policy enforcement
- Dynamic access control based on behavioural analytics
- Automating device trust scoring and continuous authentication
- Detecting compromised identities through access pattern anomalies
- Triggering MFA challenges in response to risky behaviour
- Integrating with identity providers like Okta and Azure AD
- Automated session termination for suspicious logins
- Enforcing least privilege using AI-identified roles
- Monitoring for API abuse and excessive data access
- Generating user activity summaries for policy refinement
Module 9: Cloud and Hybrid Network Security Automation - Extending AI automation across AWS, Azure, and GCP
- Monitoring VPC flow logs for policy violations
- Detecting public S3 bucket exposure and IAM misconfigurations
- Automating cloud security group updates based on threat context
- Responding to crypto-mining and unauthorised VM launches
- Integrating with cloud-native logging and monitoring tools
- Detecting workload impersonation and token theft
- Automating hybrid DNS and DHCP policy enforcement
- Orchestrating responses across multi-cloud and on-prem environments
- Building unified visibility dashboards for hybrid threat hunting
Module 10: AI for Encrypted Traffic Analysis - Performing TLS fingerprinting for application and threat identification
- Detecting malware communication using encrypted traffic patterns
- Analysing certificate anomalies and self-signed certificate abuse
- Using JA3 and JA3S hashes for client and server fingerprinting
- Identifying shadow IT through unapproved encrypted services
- Detecting data exfiltration via encrypted channels
- Monitoring for unusual cipher suite usage and downgrade attacks
- Correlating encrypted traffic with user and device context
- Automating inspection rules for high-risk encrypted sessions
- Integrating with SSL decryption gateways and forward proxies
Module 11: Automated Incident Response and Forensics - Building AI-assisted triage workflows for SOC teams
- Automatically classifying incidents by severity and scope
- Collecting and preserving digital evidence at first detection
- Running automated memory and disk acquisition scripts
- Correlating events across endpoints, network, and cloud
- Generating timeline reconstructions of attack sequences
- Using AI to predict attacker objectives and next steps
- Automating legal hold notifications for forensic data
- Creating standardised reporting templates for leadership
- Integrating with digital forensics platforms like Autopsy and FTK
Module 12: AI-Enhanced Vulnerability Management - Automating vulnerability scanning triggers based on asset changes
- Prioritising patching using AI-driven risk scoring
- Correlating CVE data with real-world exploitation trends
- Detecting unpatched systems through passive traffic observation
- Automating patch deployment workflows for critical assets
- Using AI to predict which vulnerabilities will be exploited next
- Integrating with vulnerability scanners like Nessus and Qualys
- Reducing scan impact on production networks with adaptive scheduling
- Generating compliance evidence for audit requirements
- Creating executive summaries of vulnerability exposure trends
Module 13: AI for Insider Threat Detection - Establishing baseline user access and data usage patterns
- Detecting unauthorised data downloads and bulk transfers
- Monitoring for after-hours access and off-network logins
- Correlating email, file share, and network activity logs
- Detecting credential dumping and privilege escalation attempts
- Identifying risky third-party vendor access patterns
- Using AI to detect policy violations without surveillance
- Integrating with HR systems for offboarding automation
- Automating alerts to data owners and compliance teams
- Conducting ethical monitoring with legal and privacy safeguards
Module 14: Infrastructure as Code and AI Security - Automating security checks in CI/CD pipelines
- Scanning Terraform and CloudFormation templates for risks
- Detecting hard-coded secrets and credentials in code
- Enforcing secure network configuration patterns
- Integrating AI with Git-based change management
- Automating drift detection and policy enforcement
- Generating real-time compliance reports for infrastructure
- Validating network designs before deployment
- Using AI to recommend optimised security group rules
- Building self-healing infrastructure configurations
Module 15: AI-Driven DDoS Detection and Mitigation - Identifying volumetric, protocol, and application-layer DDoS attacks
- Using AI to distinguish attacks from legitimate traffic spikes
- Automating traffic diversion to scrubbing centres
- Implementing rate limiting based on real-time threat context
- Detecting slow-rate and low-and-slow DDoS variants
- Monitoring for amplification attacks using DNS and NTP
- Coordinating mitigation across cloud and on-prem networks
- Automating post-attack forensics and clean-up
- Integrating with cloud DDoS protection services
- Generating business impact reports for executive review
Module 16: AI for Wireless and IoT Network Security - Mapping AI detection to rogue access points and ad hoc networks
- Detecting device impersonation and MAC spoofing
- Monitoring for unauthorised IoT device onboarding
- Automating wireless client isolation based on risk
- Analysing beacon frame anomalies for wireless attacks
- Identifying sleep-deprivation attacks on smart devices
- Correlating IoT traffic with operational technology systems
- Detecting firmware update tampering and supply chain risks
- Automating device revocation and quarantine procedures
- Generating asset inventory reports for IoT governance
Module 17: Building Your Board-Ready AI Automation Proposal - Defining the business case for AI security automation
- Calculating ROI using reduced incident response times
- Quantifying risk reduction through faster containment
- Estimating cost savings from reduced SOC workload
- Aligning automation goals with business continuity objectives
- Creating visual dashboards for leadership communication
- Developing pilot project plans with clear KPIs
- Identifying quick wins to build internal momentum
- Addressing leadership concerns about AI reliability
- Presenting your final certification project as a strategic initiative
- Performing TLS fingerprinting for application and threat identification
- Detecting malware communication using encrypted traffic patterns
- Analysing certificate anomalies and self-signed certificate abuse
- Using JA3 and JA3S hashes for client and server fingerprinting
- Identifying shadow IT through unapproved encrypted services
- Detecting data exfiltration via encrypted channels
- Monitoring for unusual cipher suite usage and downgrade attacks
- Correlating encrypted traffic with user and device context
- Automating inspection rules for high-risk encrypted sessions
- Integrating with SSL decryption gateways and forward proxies
Module 11: Automated Incident Response and Forensics - Building AI-assisted triage workflows for SOC teams
- Automatically classifying incidents by severity and scope
- Collecting and preserving digital evidence at first detection
- Running automated memory and disk acquisition scripts
- Correlating events across endpoints, network, and cloud
- Generating timeline reconstructions of attack sequences
- Using AI to predict attacker objectives and next steps
- Automating legal hold notifications for forensic data
- Creating standardised reporting templates for leadership
- Integrating with digital forensics platforms like Autopsy and FTK
Module 12: AI-Enhanced Vulnerability Management - Automating vulnerability scanning triggers based on asset changes
- Prioritising patching using AI-driven risk scoring
- Correlating CVE data with real-world exploitation trends
- Detecting unpatched systems through passive traffic observation
- Automating patch deployment workflows for critical assets
- Using AI to predict which vulnerabilities will be exploited next
- Integrating with vulnerability scanners like Nessus and Qualys
- Reducing scan impact on production networks with adaptive scheduling
- Generating compliance evidence for audit requirements
- Creating executive summaries of vulnerability exposure trends
Module 13: AI for Insider Threat Detection - Establishing baseline user access and data usage patterns
- Detecting unauthorised data downloads and bulk transfers
- Monitoring for after-hours access and off-network logins
- Correlating email, file share, and network activity logs
- Detecting credential dumping and privilege escalation attempts
- Identifying risky third-party vendor access patterns
- Using AI to detect policy violations without surveillance
- Integrating with HR systems for offboarding automation
- Automating alerts to data owners and compliance teams
- Conducting ethical monitoring with legal and privacy safeguards
Module 14: Infrastructure as Code and AI Security - Automating security checks in CI/CD pipelines
- Scanning Terraform and CloudFormation templates for risks
- Detecting hard-coded secrets and credentials in code
- Enforcing secure network configuration patterns
- Integrating AI with Git-based change management
- Automating drift detection and policy enforcement
- Generating real-time compliance reports for infrastructure
- Validating network designs before deployment
- Using AI to recommend optimised security group rules
- Building self-healing infrastructure configurations
Module 15: AI-Driven DDoS Detection and Mitigation - Identifying volumetric, protocol, and application-layer DDoS attacks
- Using AI to distinguish attacks from legitimate traffic spikes
- Automating traffic diversion to scrubbing centres
- Implementing rate limiting based on real-time threat context
- Detecting slow-rate and low-and-slow DDoS variants
- Monitoring for amplification attacks using DNS and NTP
- Coordinating mitigation across cloud and on-prem networks
- Automating post-attack forensics and clean-up
- Integrating with cloud DDoS protection services
- Generating business impact reports for executive review
Module 16: AI for Wireless and IoT Network Security - Mapping AI detection to rogue access points and ad hoc networks
- Detecting device impersonation and MAC spoofing
- Monitoring for unauthorised IoT device onboarding
- Automating wireless client isolation based on risk
- Analysing beacon frame anomalies for wireless attacks
- Identifying sleep-deprivation attacks on smart devices
- Correlating IoT traffic with operational technology systems
- Detecting firmware update tampering and supply chain risks
- Automating device revocation and quarantine procedures
- Generating asset inventory reports for IoT governance
Module 17: Building Your Board-Ready AI Automation Proposal - Defining the business case for AI security automation
- Calculating ROI using reduced incident response times
- Quantifying risk reduction through faster containment
- Estimating cost savings from reduced SOC workload
- Aligning automation goals with business continuity objectives
- Creating visual dashboards for leadership communication
- Developing pilot project plans with clear KPIs
- Identifying quick wins to build internal momentum
- Addressing leadership concerns about AI reliability
- Presenting your final certification project as a strategic initiative
- Automating vulnerability scanning triggers based on asset changes
- Prioritising patching using AI-driven risk scoring
- Correlating CVE data with real-world exploitation trends
- Detecting unpatched systems through passive traffic observation
- Automating patch deployment workflows for critical assets
- Using AI to predict which vulnerabilities will be exploited next
- Integrating with vulnerability scanners like Nessus and Qualys
- Reducing scan impact on production networks with adaptive scheduling
- Generating compliance evidence for audit requirements
- Creating executive summaries of vulnerability exposure trends
Module 13: AI for Insider Threat Detection - Establishing baseline user access and data usage patterns
- Detecting unauthorised data downloads and bulk transfers
- Monitoring for after-hours access and off-network logins
- Correlating email, file share, and network activity logs
- Detecting credential dumping and privilege escalation attempts
- Identifying risky third-party vendor access patterns
- Using AI to detect policy violations without surveillance
- Integrating with HR systems for offboarding automation
- Automating alerts to data owners and compliance teams
- Conducting ethical monitoring with legal and privacy safeguards
Module 14: Infrastructure as Code and AI Security - Automating security checks in CI/CD pipelines
- Scanning Terraform and CloudFormation templates for risks
- Detecting hard-coded secrets and credentials in code
- Enforcing secure network configuration patterns
- Integrating AI with Git-based change management
- Automating drift detection and policy enforcement
- Generating real-time compliance reports for infrastructure
- Validating network designs before deployment
- Using AI to recommend optimised security group rules
- Building self-healing infrastructure configurations
Module 15: AI-Driven DDoS Detection and Mitigation - Identifying volumetric, protocol, and application-layer DDoS attacks
- Using AI to distinguish attacks from legitimate traffic spikes
- Automating traffic diversion to scrubbing centres
- Implementing rate limiting based on real-time threat context
- Detecting slow-rate and low-and-slow DDoS variants
- Monitoring for amplification attacks using DNS and NTP
- Coordinating mitigation across cloud and on-prem networks
- Automating post-attack forensics and clean-up
- Integrating with cloud DDoS protection services
- Generating business impact reports for executive review
Module 16: AI for Wireless and IoT Network Security - Mapping AI detection to rogue access points and ad hoc networks
- Detecting device impersonation and MAC spoofing
- Monitoring for unauthorised IoT device onboarding
- Automating wireless client isolation based on risk
- Analysing beacon frame anomalies for wireless attacks
- Identifying sleep-deprivation attacks on smart devices
- Correlating IoT traffic with operational technology systems
- Detecting firmware update tampering and supply chain risks
- Automating device revocation and quarantine procedures
- Generating asset inventory reports for IoT governance
Module 17: Building Your Board-Ready AI Automation Proposal - Defining the business case for AI security automation
- Calculating ROI using reduced incident response times
- Quantifying risk reduction through faster containment
- Estimating cost savings from reduced SOC workload
- Aligning automation goals with business continuity objectives
- Creating visual dashboards for leadership communication
- Developing pilot project plans with clear KPIs
- Identifying quick wins to build internal momentum
- Addressing leadership concerns about AI reliability
- Presenting your final certification project as a strategic initiative
- Automating security checks in CI/CD pipelines
- Scanning Terraform and CloudFormation templates for risks
- Detecting hard-coded secrets and credentials in code
- Enforcing secure network configuration patterns
- Integrating AI with Git-based change management
- Automating drift detection and policy enforcement
- Generating real-time compliance reports for infrastructure
- Validating network designs before deployment
- Using AI to recommend optimised security group rules
- Building self-healing infrastructure configurations
Module 15: AI-Driven DDoS Detection and Mitigation - Identifying volumetric, protocol, and application-layer DDoS attacks
- Using AI to distinguish attacks from legitimate traffic spikes
- Automating traffic diversion to scrubbing centres
- Implementing rate limiting based on real-time threat context
- Detecting slow-rate and low-and-slow DDoS variants
- Monitoring for amplification attacks using DNS and NTP
- Coordinating mitigation across cloud and on-prem networks
- Automating post-attack forensics and clean-up
- Integrating with cloud DDoS protection services
- Generating business impact reports for executive review
Module 16: AI for Wireless and IoT Network Security - Mapping AI detection to rogue access points and ad hoc networks
- Detecting device impersonation and MAC spoofing
- Monitoring for unauthorised IoT device onboarding
- Automating wireless client isolation based on risk
- Analysing beacon frame anomalies for wireless attacks
- Identifying sleep-deprivation attacks on smart devices
- Correlating IoT traffic with operational technology systems
- Detecting firmware update tampering and supply chain risks
- Automating device revocation and quarantine procedures
- Generating asset inventory reports for IoT governance
Module 17: Building Your Board-Ready AI Automation Proposal - Defining the business case for AI security automation
- Calculating ROI using reduced incident response times
- Quantifying risk reduction through faster containment
- Estimating cost savings from reduced SOC workload
- Aligning automation goals with business continuity objectives
- Creating visual dashboards for leadership communication
- Developing pilot project plans with clear KPIs
- Identifying quick wins to build internal momentum
- Addressing leadership concerns about AI reliability
- Presenting your final certification project as a strategic initiative
- Mapping AI detection to rogue access points and ad hoc networks
- Detecting device impersonation and MAC spoofing
- Monitoring for unauthorised IoT device onboarding
- Automating wireless client isolation based on risk
- Analysing beacon frame anomalies for wireless attacks
- Identifying sleep-deprivation attacks on smart devices
- Correlating IoT traffic with operational technology systems
- Detecting firmware update tampering and supply chain risks
- Automating device revocation and quarantine procedures
- Generating asset inventory reports for IoT governance