A comprehensive, security‑focused programme for SOC teams, IT security, risk, compliance, and leadership.
Modern threat actors use automation, AI, and large‑scale tooling to evade traditional defences. AI‑driven threat detection enables organisations to identify anomalies, correlate signals, and respond faster than human‑only teams can manage. This course equips enterprise teams with the knowledge to deploy, evaluate, and govern AI‑based detection systems.
Audience:
SOC analysts, cybersecurity teams, IT operations, threat hunters, risk managers, compliance officers, and senior leadership.
Duration:
4 hours (or 2 × 2‑hour sessions)
Learning Outcomes:
Participants will be able to:
Understand how AI enhances threat detection across the enterprise.
Recognise AI‑enabled attack techniques and evasion methods.
Apply AI‑driven detection models to networks, endpoints, identities, and cloud workloads.
Integrate AI into SOC workflows and incident response.
Govern AI models responsibly, ensuring accuracy, fairness, and auditability.
Strengthen organisational resilience through AI‑supported security operations.
Four modules progress from threat landscape to detection capabilities to governance and operationalisation.
This module explains how adversaries use AI and how this changes detection requirements.
Key Topics
AI‑powered malware generation and obfuscation
Automated reconnaissance and vulnerability scanning
AI‑driven phishing, spear‑phishing, and social engineering
Deepfake voice and video impersonation
AI‑assisted credential‑stuffing and botnet attacks
Evasion of signature‑based and rule‑based systems
Enterprise Examples
AI‑generated phishing emails bypassing filters
Malware that mutates to avoid detection
Deepfake calls impersonating executives
Automated lateral movement using reinforcement learning
AI‑driven cloud misconfiguration exploitation
Learning Activities
Analyse an AI‑generated phishing campaign
Map AI‑enabled attack techniques to MITRE ATT&CK
Group discussion: “Which parts of our environment are most vulnerable?”
Take‑Home Actions
Review detection rules for AI‑generated threats
Strengthen identity verification for high‑risk actions
Update staff awareness materials to include AI‑enabled attacks
This module focuses on how AI enhances detection across the enterprise.
Core Detection Capabilities
Behavioural analytics for user and entity behaviour (UEBA)
Machine‑learning anomaly detection
Endpoint detection and response (EDR) with AI correlation
Network traffic analysis using ML models
Cloud workload protection with AI‑based baselining
Identity‑centric detection (impossible travel, privilege misuse)
Automated correlation across logs, SIEM, and telemetry
Enterprise Use Cases
Detecting insider threats through behavioural deviations
Identifying anomalous API calls in cloud environments
Flagging unusual authentication patterns
Spotting lateral movement through graph‑based analysis
Detecting malware variants unseen in signature databases
Learning Activities
Explore a sample AI‑driven SOC dashboard
Walk through an AI‑flagged anomaly investigation
Group challenge: Map AI tools to existing detection gaps
Take‑Home Actions
Review telemetry coverage across endpoints and cloud
Identify gaps in behavioural analytics
Strengthen integration between SIEM, EDR, and identity systems
This module ensures AI‑based detection systems meet regulatory, ethical, and operational standards.
Governance Principles
Accountability: humans remain responsible for security decisions
Explainability: AI‑driven alerts must be interpretable
Fairness: avoid biased detection against specific user groups
Privacy: ensure data used for training is compliant
Security: protect models from poisoning or evasion
Auditability: maintain logs for investigations and regulators
Regulatory Considerations
Data protection and privacy obligations
Sector‑specific security regulations
AI governance frameworks
Model risk management requirements
Documentation for audits and incident reporting
Enterprise Risks
False positives overwhelming SOC teams
False negatives due to model drift
Poor data quality reducing detection accuracy
Over‑reliance on automated decisions
Lack of transparency in model outputs
Learning Activities
Evaluate an AI detection policy for gaps
Conduct a model risk assessment exercise
Scenario: Respond to a regulator requesting model explainability
Take‑Home Actions
Add AI detection models to risk registers
Review model governance documentation
Strengthen human‑in‑the‑loop controls
This module focuses on integrating AI into real‑world security operations.
SOC Integration Priorities
AI‑assisted triage and alert prioritisation
Automated incident enrichment
Threat hunting with ML‑generated hypotheses
AI‑supported playbooks in SOAR platforms
Continuous model tuning and feedback loops
Cross‑team collaboration (SOC, IT, cloud, identity, risk)
Operational Controls
Multi‑factor authentication
Privileged access monitoring
Endpoint hardening
Network segmentation
Cloud posture management
Incident response automation
Enterprise Scenarios
AI flags anomalous admin behaviour
AI detects unusual data exfiltration patterns
AI identifies a compromised service account
AI correlates multiple low‑severity alerts into a high‑risk incident
Learning Activities
Tabletop exercise: AI‑assisted incident response
Build a detection‑to‑response workflow using AI insights
Action planning: Strengthen SOC maturity with AI
Take‑Home Actions
Update SOC playbooks to include AI steps
Conduct regular AI‑driven threat simulations
Improve telemetry quality and coverage
A structured assessment reinforces enterprise‑level competence.
15 multiple‑choice questions
3 scenario‑based questions
Group reflection on organisational detection gaps
Full attendance
Active participation
Completion of assessment
This training aims to:
Strengthen threat detection maturity
Reduce dwell time and improve response speed
Enhance SOC efficiency and accuracy
Support compliance with regulatory expectations
Build a culture of security vigilance
Improve resilience against AI‑enabled attacks