AI in ITSM Monitoring and Protection: 10-Day Course

Name: 10-Day AI for ITSM Monitoring & Protection
Availability: InStock

Master machine learning tools, proactive incident automation, and security analytics across enterprise IT service management over 10 focused days.

AI in ITSM Monitoring and Protection: 10-Day Course

At a glance

Duration: 12 days
Format: Classroom
Cities: Dubai, Barcelona, Jakarta, Cape town, Cairo, London and more
Next session: 12 – 23 October 2026, Dubai
Average fee: 10,150 €

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10-Day AI for ITSM Monitoring & Protection

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Overview

Enterprise digital infrastructures require intelligent automation to sustain service availability, mitigate security risks, and accelerate operational recovery. This ten-day programme explores AI for ITSM monitoring and protection, providing IT professionals with actionable frameworks to transition from reactive troubleshooting to predictive, data-driven service assurance. Participants examine machine learning anomaly detection, automated incident triage, post-deployment IT security, and service desk cognitive agents to improve service level agreement compliance and operational resilience across hybrid ecosystems. This course is delivered by Agile Leaders Training Center.

Who Should Attend

IT operations managers seeking to integrate predictive analytics and automated event correlation into infrastructure workflows.
Service desk leads and incident managers aiming to reduce mean time to resolution using automated routing and virtual agents.
IT security officers and risk specialists responsible for monitoring post-deployment vulnerabilities and threat detection.
System administrators, service delivery coordinators, and software engineers deploying machine learning models in service management environments.

Targeted Departments and Industries

This programme supports cross-functional technical teams operating within complex enterprise architectures and regulatory environments.

IT Operations and Infrastructure Teams in Telecommunications and Cloud Services
Cybersecurity and Risk Compliance Units in Banking and Financial Services
Service Desk and Technical Support Groups in E-Commerce and Retail
Service Delivery and Operations Divisions in Healthcare and Hospital Systems
Software Release and Quality Assurance Teams in Technology and Manufacturing

Course Objectives

By the end of this course, participants will be able to:

Deploy predictive AIOps platforms to identify infrastructure degradation before outages occur.
Automate incident triage, ticket classification, and remediation workflows within service management platforms.
Establish machine learning models for telemetry monitoring and post-deployment security auditing.
Design conversational virtual agents and automated routing mechanisms for IT service desks.
Configure key performance indicators and service level agreements tailored for automated IT operations.
Formulate governance frameworks and operational roadmaps for scaling machine learning in service continuity.

Course Agenda

Day 1: Foundation of AI in IT Service Management

Core concepts of AI in IT Service Management and operational modernization
Establishing baseline service level agreements and operational level agreements
Principles of AIOps architectures for infrastructure monitoring
Evaluating automated event analysis for service availability improvement
Predictive modeling techniques for early incident prevention
Service quality metrics and data telemetry collection methodologies

Day 2: AIOps and Incident Management Automation

Architecture of AIOps engines within enterprise IT operations
Proactive event filtering and alert correlation models
Automating operational responses to recurring system alerts
Real-time telemetry ingestion and multi-source event monitoring
Integration patterns for embedding machine learning into IT service workflows
Applied scenario: automated incident triage during critical service disruptions

Day 3: Post-Deployment IT Security and Resilience

Integrating machine learning into post-deployment IT security controls
Automated vulnerability discovery and exploit pattern recognition
Securing digital service delivery pathways using intelligent monitoring
Bridging operational incident response with cybersecurity mitigation
Protecting operational continuity during system changes and application updates
Applied scenario: automated threat containment in post-release production

Day 4: Service Desk Cognitive Automation

Modernizing tier-one support channels using natural language processing
Intelligent incident routing, impact classification, and automated escalation
Deploying conversational virtual assistants for common IT service requests
Empowering end-user self-service through dynamic knowledge retrieval
Enhancing user satisfaction ratings through reduced resolution latency
Applied scenario: implementing ticket classification automation

Day 5: Service Performance Optimization and Observability

Continuous performance telemetry tracking using observability tools
Predictive workload analytics for resource forecasting
Closed-loop remediation scripts for automated performance recovery
Tracking service level agreement compliance through live event analytics
Identifying architectural performance bottlenecks across distributed systems
Practical guidelines for deploying proactive infrastructure monitoring

Day 6: Advanced Security Analytics and Anomaly Detection

Aligning machine learning detection tools with IT governance standards
Real-time threat detection and suspicious activity scoring
Scripted security incident response and automated containment
Risk scoring algorithms for compliance tracking and audit preparation
Anomaly detection across system logs, access events, and network traffic
Applied scenario: mitigating unauthorized lateral movement in enterprise systems

Day 7: Governance and Management of AI-Powered Services

Establishing operational governance for automated decision-making engines
Operational lifecycle management for machine learning models in production
Regulatory compliance validation and data privacy in automated ITSM
Evaluating quantitative business impact and cost efficiency of automation
Scaling machine learning architectures across multinational enterprise domains
Applied scenario: developing an operational governance charter for AIOps

Day 8: Performance Analytics and ROI Measurement

Defining algorithmic operational metrics and telemetry performance indicators
Refining service targets and error budgets for automated operations
Calculating return on investment and cost savings from incident automation
Benchmarking enterprise performance metrics against industry baselines
Automating service health scorecards and operational reporting pipelines
Applied scenario: constructing an executive reporting dashboard for AIOps metrics

Day 9: Continuous Service Improvement Through Machine Learning

Iterative service improvement cycles powered by operational intelligence
Pattern mining across unresolved tickets to eliminate systemic root causes
Automating repetitive post-incident administrative workflows
Applying predictive ticket scoring to optimize support staff allocation
Formulating a ten-step implementation roadmap for AIOps adoption
Applied scenario: building a continuous improvement plan for IT operations

Day 10: Strategic Roadmaps and Future Operational Trends

Emerging capabilities in automated infrastructure orchestration
Evolutionary trajectories of cognitive operations and autonomous support
Preparing enterprise teams and operational culture for intelligent automation
Critical success factors for multi-phase AI adoption across operations
Expanding cognitive automation into release pipelines and continuous integration
Synthesizing a long-term roadmap for intelligent IT service management

Course Toolbox

Reference checklists for evaluating AIOps platforms and telemetry pipelines
Standard templates for post-deployment security auditing and incident response
Workflow templates for automated incident triage and ticket routing
Readings and implementation guides covering machine learning governance in ITSM

FAQs

What specific qualifications or prerequisites are needed for participants before enrolling in the course?

Participants should have a foundational understanding of standard IT Service Management processes, ticketing workflows, and IT infrastructure operations. Practical exposure to cloud platforms or systems administration is helpful.

How long is each day's session, and is there a total number of hours required for the entire course?

Each day involves approximately four to five hours of interactive learning, case discussions, and practical exercises. Over the full ten-day duration, participants complete approximately 40 to 50 instructional hours.

How does AIOps differ from traditional ITSM approaches, and why should I care about its integration?

Traditional ITSM depends on manual event reviews, human-driven ticket triage, and reactive troubleshooting after service outages happen. AIOps applies machine learning to correlate telemetry in real time, forecast impending failures, and execute automated fixes, significantly lowering downtime and operational costs.

credits: 5 credit per day

Course Mode: full-time

Provider: Agile Leaders Training Center

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Showing 21-40 of 80 events

Location	Dates	Duration	Mode	Price
Zoom	Week 49, 2026 30 November – 11 December 2026	12 Days	Online	€3,000
Manama	Week 49, 2026 6 – 17 December 2026	12 Days	Onsite	€8,000
Dubai	Week 50, 2026 7 – 18 December 2026	12 Days	Onsite	€8,500
Abu Dhabi	Week 51, 2026 14 – 25 December 2026	12 Days	Onsite	€8,000
Porto	Week 51, 2026 14 – 25 December 2026	12 Days	Onsite	€10,000
Cairo	Week 52, 2026 21 December 2026 – 1 January 2027	12 Days	Onsite	€7,000
Munich	Week 52, 2026 21 December 2026 – 1 January 2027	12 Days	Onsite	€10,000
Phuket	Week 52, 2026 27 December 2026 – 7 January 2027	12 Days	Onsite	€9,000
Bangkok	Week 53, 2027 3 – 14 January 2027	12 Days	Onsite	€9,000
Accra	Week 02, 2027 17 – 28 January 2027	12 Days	Onsite	€5,800
Nice	Week 04, 2027 25 January – 5 February 2027	12 Days	Onsite	€10,000
Berlin	Week 05, 2027 1 – 12 February 2027	12 Days	Onsite	€10,000
London	Week 06, 2027 8 – 19 February 2027	12 Days	Onsite	€14,000
Bali	Week 08, 2027 28 February – 11 March 2027	12 Days	Onsite	€10,000
Zanzibar	Week 09, 2027 7 – 18 March 2027	12 Days	Onsite	€9,000
Nairobi	Week 12, 2027 28 March – 8 April 2027	12 Days	Onsite	€6,400
Amman	Week 13, 2027 4 – 15 April 2027	12 Days	Onsite	€7,000
Tbilisi	Week 15, 2027 12 – 23 April 2027	12 Days	Onsite	€8,800
Prague	Week 16, 2027 19 – 30 April 2027	12 Days	Onsite	€10,000
Barcelona	Week 17, 2027 26 April – 7 May 2027	12 Days	Onsite	€10,000

Frequently asked questions

What does this course cover?

OverviewEnterprise digital infrastructures require intelligent automation to sustain service availability, mitigate security risks, and accelerate operational recovery. This ten-day programme explores AI for ITSM monitoring and protection, providing IT professionals with actionable frameworks to transition from reactive troubleshooting to predictive, data…

Are training dates available?

Yes. Available dates and destinations are listed in the course dates section on this page.

How can I register?

Choose an available date on this page and complete the registration form, or send a programme enquiry.

Can I download the course brochure?

Yes. Use the brochure download link provided on this page.

AI in ITSM Monitoring and Protection: 10-Day Course