AI and Cybersecurity – Apis Training

AI in Telecom

linda — Wed, 20 May 2026 18:47:26 +0000

AI in Telecom

Virtual Classroom

€ 1020*

Classroom Training

€ 1020*

*Prices exclude VAT

Course Contents

This course gives engineers and architects a technically rigorous grounding in how Artificial Intelligence and Machine Learning are deployed across modern telecom networks. It moves beyond vendor marketing to examine the real data pipelines, model architectures, API interfaces, and operational constraints that determine whether an AI system succeeds or fails in a production network environment.

The modules are ordered to build understanding progressively: from the industry context and programmable-network foundations, through domain-specific use cases in RAN, Core, and BSS, to Generative AI and the governance disciplines required for safe deployment. Lab exercises are integrated into each module.

The course covers both classical ML models (anomaly detection, churn prediction, reinforcement learning) and Large Language Model applications (NOC copilots, agentic operations, configuration generation). Hands-on lab exercises are included throughout.

Target Audience

The target group is engineers, architects, and technical managers who need to understand, design, or evaluate AI systems operating on live telecom networks including network operations engineers, RAN and Core architects, BSS developers, and data scientists working in the telecom domain.

Note: Lab exercises require a Python 3.11+ environment.

Course outline

Introduction

• The four macro-trends converging in telecom AI: margin pressure, software-defined networks, democratised ML tooling, and Generative AI
• Market investment signals and deployment maturity: Customer Experience AI, Revenue Assurance, AIOps, RAN Optimisation, Generative AI
• The Autonomous Network vision: TM Forum ANL maturity scale (L0-L5) and where operators stand today
• Hype vs. reality: NOC engineer replacement, zero-touch networks, federated learning at scale, LLM configuration push
• The API foundation: 3GPP SBA, O-RAN interfaces (A1/E2/O1/O2), CAMARA/GSMA Open Gateway, TM Forum Open APIs

Network Anomaly Detection & AIOps

• The problem with threshold-based operations: alert storms, blind spots, slow MTTR, excessive false positives
• Data sources and ingestion: SNMP traps, 3GPP PM counters, Syslogs, NetFlow/IPFIX, TM Forum TMF642 alarm feeds, signalling traces
• ML techniques for anomaly detection: Isolation Forest, Autoencoders, LSTM-based forecasting
• Supervised methods: Gradient Boosted Trees (XGBoost/LightGBM), Graph Neural Networks for root cause analysis
• Alarm correlation and Root Cause Analysis (RCA) topology-aware graph scoring
• Closed-loop remediation: low-risk automated actions, medium-risk configuration within safe bounds, high-risk human approval gate

AI-Driven RAN Optimisation

• Why the RAN is the hardest optimisation problem: rule-based SON limitations and multi-objective trade-offs
• O-RAN architecture and AI integration points: Non-RT RIC, Near-RT RIC, xApps and rApps
• O-RAN A1/E2 control loops timescales and primary use cases
• Handover optimisation with Reinforcement Learning: state, action, reward formulation; PPO agents
• AI-driven energy saving: traffic prediction, sleep candidate identification, coverage verification, O1 NETCONF/YANG command issuance
• Beam management in 5G NR mmWave: CNN/LSTM on CSI feedback, position-assisted beam prediction, federated learning across gNBs

Intelligent Core: Traffic Steering & Network Slicing

• The 5G Core as a programmable system: Service Based Architecture, NRF service discovery, key NFs relevant to AI (PCF, NWDAF, SMF, UDM, AMF)
• NWDAF – the native AI node of the 5G Core: analytics types, Nnwdaf_AnalyticsInfo and Nnwdaf_DataManagement services (3GPP TS 23.288 / TS 29.520)
• AI-powered network slicing: demand forecasting per slice, admission control models for URLLC, resource reallocation via NSSMF
• Intent-Based Networking: intent parsing (NLP/LLM), intent fulfilment engine, actuator layer (NETCONF/YANG, 5GC SBI, SDN controller APIs)

AI in BSS: Churn, Revenue Assurance & Customer Experience

• Why BSS data is uniquely valuable for AI: CDRs, CRM data, billing and payment data, customer care interaction transcripts
• Churn prediction: feature engineering with ARPU trend, data usage delta, care contacts, network quality signals; LightGBM/XGBoost model; SHAP explanations
• Revenue assurance with AI: anomaly detection on CDR streams, Wangiri fraud detection, graph-based fraud ring detection via GNN
• AI-powered customer experience: intelligent care routing and next-best-action (NBA), network experience indexing (QoE index), proactive care

Generative AI & LLMs in Telecom Operations

• Where Generative AI fits and where it does not: NOC copilots, configuration drafting, customer care chatbots vs. real-time closed-loop control
• NOC Copilot architecture: Retrieval-Augmented Generation (RAG) with runbook vector store, alarm context, and topology data
• Network configuration generation from natural-language intent: LLM drafting + deterministic validation agent + human approval gate
• Agentic AI in network operations: ReAct framework, tool-equipped agents for multi-step diagnostic workflows
• Telecom domain adaptation of LLMs: RAG over 3GPP/O-RAN specs, domain fine-tuning, structured output with schema validation

Architecture Patterns, Governance & Deployment

• Reference architecture: AI/ML Platform layer, AI Orchestration & Application layer, Network Exposure & API layer, Physical/Virtual Network
• MLOps for telecom: data drift monitoring, model versioning and staged rollout, shadow mode testing, feedback loop monitoring
• Explainability and auditability: SHAP values for tabular models, attention visualisation for neural networks, full audit trails for configuration changes
• Privacy and data governance: GDPR requirements, telecom-specific regulation, federated learning, differential privacy, data minimisation in feature pipelines
• Standards references: 3GPP TS 23.501/23.502/23.288/29.520/32.500, O-RAN WG1/WG2/WG3 specs, TM Forum IG1230/IG1251, ETSI GR ENI 001, ETSI GS ZSM 002

Practical Exercises During the Course

• Anomaly detection with Isolation Forest (Python / scikit-learn) on synthetic PM counter dataset
• Reinforcement learning for handover optimisation (PPO agent, Stable-Baselines3) using E2 telemetry dataset
• NWDAF slice load monitoring and proactive resource reallocation via mock NSSMF API (Python / Flask)
• Churn prediction with LightGBM and SHAP feature attribution on synthetic CDR + CRM dataset
• NOC Copilot with Anthropic API: incident summary, root cause, and ranked remediation steps from alarm context

Prerequisites

• Familiarity with RESTful / gRPC APIs
• Basic understanding of telecom network architecture (RAN, Core, OSS/BSS)
• Exposure to ML concepts (helpful but not required)

The Resilience Cycle for NIS2 Readiness

linda — Wed, 19 Nov 2025 15:26:13 +0000

The Resilience Cycle for NIS2 Readiness

Upcoming Public Event

Date: 2-4 February 2026
Hours: 9:00 – 13:00 CET
Format: Virtual Classroom
Price: € 1 870

Virtual Classroom

€ 1870*

*Prices exclude VAT

Course Contents

This masterclass provides a complete, end-to-end journey through the capabilities required to achieve operational NIS2 readiness.

Participants learn how to identify weaknesses, engineer secure systems, manage suppliers, and produce audit-ready evidence — following a structured resilience cycle that connects technical execution with regulatory obligations.

Led by Two Complementary Experts

The course is co-delivered by:

● A Cloud Security & DevSecOps specialist who guides participants through technical design, automation, and evidence generation.

● A Governance & Risk management expert who translates operations into frameworks, metrics, and audit-ready documentation.

Together they provide a 360-degree perspective—linking technology, regulation, and leadership decision-making.

Key Learning Outcomes

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

● Understand how modern threats and NIS2 converge into a resilience mandate
● Prioritize vulnerabilities using risk and business impact
● Build secure-by-design technical environments aligned with NIS2.
● Translate technical controls into measurable indicators.
● Manage NIS2-compliant incident response, including third-party breaches

Course outline

Resilience Foundations

Theme: Clarifying threats, obligations, and organisational weaknesses.

● Overview of modern cyber threats and NIS2 accountability expectations
● How regulation aligns with resilience, not checklists
● Strategic vulnerability and exposure management
● Workshop: Prioritising real vulnerabilities using business context

Outcome: A clear resilience baseline connected to NIS2 expectations.

Building the Resilient Engine

Theme: Moving from reactive defence to proactive, engineered security.

● Security-by-design principles: identity, access, segmentation, data protection
● Establishing secure baselines and architectural discipline
● Detection engineering and behaviour-based investigation
● Hands-On Lab: Analysing a simulated incident and recommending improvements

Outcome: Practical resilience-building techniques applicable to modern environments.

Governance & NIS2 Assurance

Theme: Converting technical controls into provable, governed resilience.

● NIS2-compliant incident response (24h/72h requirements)
● Third-party and supply-chain risk integration
● Assurance, reporting, and board-level communication
● Simulation: Drafting a NIS2 early-warning notification from a vendor breach

Outcome: The ability to demonstrate readiness, manage supplier incidents, and produce audit-ready resilience evidence.

Who Should Attend

– Cybersecurity engineers, SOC analysts, cloud architects, DevSecOps and IT professionals
– CISOs, risk and compliance managers, and internal auditors
– IT leaders and project owners contributing to NIS2 implementation

Cybersecurity for 5G – Threats and Defense Techniques

linda — Thu, 13 Mar 2025 13:44:56 +0000

Cybersecurity for 5G - Threats and Defense Techniques

Virtual Classroom

€ 1 020*

*Prices exclude VAT

The 5G Cybersecurity course is a comprehensive 8-hour training designed to equip cybersecurity professionals, telecom engineers, managers, and governance teams with the knowledge and skills to identify, mitigate, and manage security threats in 5G networks. The course covers the unique security challenges posed by 5G technology, explores emerging threats such as virtualization and cloud-native infrastructures, and provides practical guidance on implementing security controls across various 5G domains.

Course Objectives

1. Understanding and Identifying 5G Security Threats

– Understand the unique security challenges of 5G technology, including scalability, virtualization, IoT, low-latency use cases, and network slicing.
– Analyze the evolving 5G threat landscape, including risks related to network function virtualization (NFV), software-defined networking (SDN), and third-party supply chains.
– Identify and model security threats using tools such as threat modeling and risk assessment, particularly in key areas like the RAN, core network, and MEC (Multi-Access Edge Computing).
– Recognize new attack vectors introduced by AI-powered threats and how machine learning can be exploited by attackers.

2. Implementing Security Controls and Mitigation Strategies

– Implement industry best practices for securing 5G networks, focusing on the core network, RAN, MEC, and network slicing.

3. Securing Cloud and Containerized Network Functions

– Secure cloud-native 5G environments, including containerized network functions, orchestrators (e.g., Kubernetes), and micro-segmentation techniques to isolate critical functions.
– Apply encryption, mutual authentication, and other security measures to protect data flows and secure communication between VNFs and other network components.
– Design slice-specific security measures, ensuring isolation between network slices to prevent lateral movement and attacks across different use cases.

NOTE: This course is a subset of the 12-hours course “Cybersecurity for 5G”.

Course outline

Introduction to 5G Technology and Security Framework

Overview of 5G Technology: RAN, Core, MEC, network slicing, and IoT integration.
Unique Security Challenges in 5G: Scalability, virtualization, IoT, low-latency requirements, and diverse use cases.
Introduction to Key Standardization Bodies: 3GPP, ETSI, ITU-T, and their roles in 5G security.

In-Depth Analysis of the 5G Threat Landscape

Detailed analysis of threat modeling methods
Security risks introduced by network function virtualization (NFV) and software-defined networking (SDN).
Risks from third-party vendors, hardware, and software components.
Methods on securing the air interface, gNodeB, and managing IoT at scale.
Security risks due to distributed architecture at the network edge.
Emerging AI-powered threats and the use of AI/ML in defense.Securing Cloud and Containerized Network Functions in 5G

Best Practices for Implementing 5G Security Across Technical Domains

Core network security: Encryption, mutual authentication, and securing data flows.
RAN security: Securing the radio interface, handovers, and massive IoT deployments.
Network slicing: Slice-specific security measures, isolation, and preventing lateral movement.
Advanced security measures: AI-powered threat detection, behavioral analytics, balancing security with network performance.

Securing Cloud and Containerized Network Functions in 5G

Cloud-native security: Securing VNFs, containerization, and orchestrators (Kubernetes, Docker).
Multi-Access Edge Computing (MEC): Physical and logical security at the edge.
Micro-segmentation and isolation in virtualized environments.
Securing communication between VNFs and cloud infrastructure.

Target Audience

Cybersecurity Professionals: Those working in telecom, critical infrastructure, and technology sectors.

Telecom Operators and Vendors: Engineers and managers responsible for network security.

Regulators and Policymakers: Government and industry professionals involved in setting security standards and policies.

Cybersecurity for 5G – Threats and Compliance Strategies

linda — Mon, 10 Mar 2025 14:22:32 +0000

Cybersecurity for 5G - Threats and Compliance Strategies

Virtual Classroom

€ 1 020*

*Prices exclude VAT

The 5G Cybersecurity course is a comprehensive 8-hour training designed to equip cybersecurity professionals, telecom engineers, managers, and governance teams with the knowledge and skills to identify, mitigate, and manage security threats in 5G networks. The course covers the unique security challenges of 5G technology, explores emerging threats such as virtualization and cloud-native infrastructures, and provides practical guidance on implementing security controls, governance and risk management practices required to maintain a secure and compliant 5G network.

Course Objectives

1. Understanding and Identifying 5G Security Threats

– Understand the unique security challenges of 5G technology, including scalability, virtualization, IoT, low-latency use cases, and network slicing.

– Analyze the evolving 5G threat landscape, including risks related to network function virtualization (NFV), software-defined networking (SDN), and third-party supply chains.

– Identify and model security threats using tools such as threat modeling and risk assessment, particularly in key areas like the RAN, core network, and MEC (Multi-Access Edge Computing).

– Recognize new attack vectors introduced by AI-powered threats and how machine learning can be exploited by attackers.

2. Implementing Security Controls and Best Practices

– Implement industry best practices for securing 5G networks, focusing on the core network, RAN, MEC, and network slicing.

3. Managing and Governing Security in 5G Networks

– Develop governance frameworks for telecom operators, communication service providers (CSP) and 5G vendors, incorporating best practices in security management, compliance, and auditing for 5G networks.

– Comply with global security standards such as 3GPP TS 33.501, GSMA, ETSI, and other regulatory requirements affecting telecom operators.

– Manage ongoing risks and incidents in live 5G networks, creating business continuity and disaster recovery plans tailored to 5G architecture.

– Develop and execute incident response plans, utilizing automated response mechanisms and collaborating with stakeholders across borders to ensure swift resolution of breaches.

– Conduct security audits and assessments to ensure that the network remains compliant and secure over time, while identifying new vulnerabilities as technology evolves.

NOTE: This course is a subset of the 12-hours course “Cybersecurity for 5G”.

Course outline

Introduction to 5G Technology and Security Framework

Overview of 5G Technology: RAN, Core, MEC, network slicing, and IoT integration.
Unique Security Challenges in 5G: Scalability, virtualization, IoT, low-latency requirements, and diverse use cases.
Introduction to Key Standardization Bodies: 3GPP, ETSI, ITU-T, and their roles in 5G security.

In-Depth Analysis of the 5G Threat Landscape

Detailed analysis of threat modeling methods
Security risks introduced by network function virtualization (NFV) and software-defined networking (SDN).
Risks from third-party vendors, hardware, and software components.
Methods on securing the air interface, gNodeB, and managing IoT at scale.
Security risks due to distributed architecture at the network edge.
Emerging AI-powered threats and the use of AI/ML in defense. Securing Cloud and Containerized Network Functions in 5G

Best Practices for Implementing 5G Security Across Technical Domains

Core network security: Encryption, mutual authentication, and securing data flows.
RAN security: Securing the radio interface, handovers, and massive IoT deployments.
Network slicing: Slice-specific security measures, isolation, and preventing lateral movement.
Advanced security measures: AI-powered threat detection, behavioral analytics, balancing security with network performance.

5G Security Governance, Risk Management, and Compliance

Governance in 5G Security: Developing governance frameworks for telecom operators¸ CSPs and 5G Vendors.
3GPP TS 33.501: Key security standards and requirements for compliance.
Security Audits: Regular audits and assessments to ensure compliance with both standards and internal policies.
Risk Management: Developing mitigation strategies and prioritizing risks based on critical assets.
Business continuity: Creating business continuity plans tailored to 5G networks.
Incident response plans: Automated response mechanisms, cross-border coordination, and collaboration with external stakeholders.

Target Audience

Cybersecurity Professionals: Those working in telecom, critical infrastructure, and technology sectors.

Telecom Operators and Vendors: Engineers and managers responsible for network security.

Regulators and Policymakers: Government and industry professionals involved in setting security standards and policies.

Cybersecurity for 5G

linda — Fri, 18 Oct 2024 16:41:34 +0000

Cybersecurity for 5G

As 5G networks become the foundation for next-generation telecommunications and critical services (such as healthcare, smart cities, and autonomous vehicles), the security of these networks is more vital than ever.

Virtual Classroom Training

€ 1870*

*Prices exclude VAT

The Cybersecurity for 5G course is a comprehensive 12-hour training designed to equip cybersecurity professionals, telecom engineers, managers, and governance teams with the knowledge and skills to identify, mitigate, and manage security threats in 5G networks. The course covers the unique security challenges posed by 5G technology, explores emerging threats such as virtualization and cloud-native infrastructures, and provides practical guidance on implementing security controls across various 5G domains. Additionally, the course delves into the governance and risk management practices required to maintain a secure 5G network once deployed.

COURSE OBJECTIVES

1. Understanding and Identifying 5G Security Threats

– Understand the unique security challenges of 5G technology, including scalability, virtualization, IoT, low-latency use cases, and network slicing.
– Analyze the evolving 5G threat landscape, including risks related to network function virtualization (NFV), software-defined networking (SDN), and third-party supply chains.
– Identify and model security threats using tools such as threat modeling and risk assessment, particularly in key areas like the RAN, core network, and MEC (Multi-Access Edge Computing).
– Recognize new attack vectors introduced by AI-powered threats and how machine learning can be exploited by attackers.

2. Implementing Security Controls and Mitigation Strategies

– Implement industry best practices for securing 5G networks, focusing on the core network, RAN, MEC, and network slicing.
– Secure cloud-native 5G environments, including containerized network functions, orchestrators (e.g., Kubernetes), and micro-segmentation techniques to isolate critical functions.
– Apply encryption, mutual authentication, and other security measures to protect data flows and secure communication between VNFs and other network components.
– Design slice-specific security measures, ensuring isolation between network slices to prevent lateral movement and attacks across different use cases.

3. Managing and Governing Security in 5G Networks

– Develop governance frameworks for telecom operators, incorporating best practices in security management, compliance, and auditing for 5G networks.
– Comply with global security standards such as 3GPP TS 33.501, GDPR, HIPAA, and other regulatory requirements affecting telecom operators.
– Manage ongoing risks and incidents in live 5G networks, creating business continuity and disaster recovery plans tailored to 5G architecture.
– Develop and execute incident response plans, utilizing automated response mechanisms and collaborating with stakeholders across borders to ensure swift resolution of breaches.
– Conduct security audits and assessments to ensure that the network remains compliant and secure over time, while identifying new vulnerabilities as technology evolves.

Course outline

Introduction to 5G Technology and Security Framework

Overview of 5G Technology: RAN, Core, MEC, network slicing, and IoT integration.
Unique Security Challenges in 5G: Scalability, virtualization, IoT, low-latency requirements, and diverse use cases.
Introduction to Key Standardization Bodies: 3GPP, ETSI, ITU-T, and their roles in 5G security.
Introduction to ENISA’s 5G Threat Landscape: Overview of ENISA’s 5G threat landscape methodology and its relevance to 5G security.

In-Depth Analysis of the 5G Threat Landscape

Detailed analysis of threats identified by ENISA, including those related to virtualization and supply chains.
Security risks introduced by network function virtualization (NFV) and software-defined networking (SDN).
Risks from third-party vendors, hardware, and software components.
Methods on securing the air interface, gNodeB, and managing IoT at scale.
Security risks due to distributed architecture at the network edge.
Emerging AI-powered threats and the use of AI/ML in defense.

Best Practices for Implementing 5G Security Across Technical Domains

Core network security: Encryption, mutual authentication, and securing data flows.
RAN security: Securing the radio interface, handovers, and massive IoT deployments.
Network slicing: Slice-specific security measures, isolation, and preventing lateral movement.
Advanced security measures: AI-powered threat detection, behavioral analytics, balancing security with network performance.

Securing Cloud and Containerized Network Functions in 5G

Cloud-native security: Securing VNFs, containerization, and orchestrators (Kubernetes, Docker).
Multi-Access Edge Computing (MEC): Physical and logical security at the edge.
Micro-segmentation and isolation in virtualized environments.
Securing communication between VNFs and cloud infrastructure.

5G Security Governance, Risk Management, and Compliance

Governance in 5G Security : Developing governance frameworks for telecom operators.
ENISA’s Cybersecurity Assessment Methodology: A detailed look at sectoral assessments and how they apply to telecom operators.
3GPP TS 33.501: Key security standards and requirements for compliance.
Regulatory compliance: GDPR, HIPAA, and other key regulations affecting 5G networks.
Security Audits: Regular audits and assessments to ensure compliance with both standards and internal policies.
Risk Management: Developing mitigation strategies and prioritizing risks based on critical assets.
Business continuity : Creating business continuity plans tailored to 5G networks.
Incident response plans: Automated response mechanisms, cross-border coordination, and collaboration with external stakeholders.

Future Trends in 5G Security and Emerging Use Cases

Emerging 5G Use Cases: IoT Security, URLLC, Massive Machine-Type
Communication (mMTC)
The Role of AI and Machine Learning: AI-powered automation for security
monitoring and threat detection in 5G environments.
Future Trends in 5G Security: Post-Quantum Cryptography, Security
Automation, 6G and Beyond

Target Audience

Cybersecurity Professionals: Those working in telecom, critical infrastructure, and technology sectors.
Telecom Operators and Vendors: Engineers and managers responsible for network security.
Regulators and Policymakers: Government and industry professionals involved in setting security standards and policies.

AI for Telecom in an Hour

linda — Mon, 01 Jul 2024 10:37:38 +0000

AI for Telecom in an Hour

This introductory course delves into the transformative impact of artificial intelligence on the telecom industry, covering a wide array of topics.

eLearning

€ 95*

*Prices exclude VAT

You’ll learn how AI optimizes network operations through intelligent traffic management, resource allocation, and predictive maintenance.

We’ll explore the enhancements AI brings to customer experience, including personalized interactions, AI-driven chatbots, and proactive support. Additionally, the course addresses the critical role of AI in network security, from threat detection and prevention to automating incident response and securing IoT devices.

As we look to the future, this course examines the expansion of 5G, the arrival of 6G, and the evolution towards autonomous networks, highlighting AI’s central role in these advancements. We’ll also discuss the synergy between AI and blockchain, the ethical implications of AI deployment, and its contribution to sustainability in telecom. By the end of this course, you’ll have a good understanding of how AI is revolutionizing the telecom industry, driving innovation, and opening up new opportunities for service providers and their customers.

Course outline

Introduction

The Current Landscape of the Telecom Industry
The Role of AI in Telecom
AI Native
Drivers for AI Adoption in Telecom
AI Technologies Transforming Telecom

Network Optimization and Intelligent Automation

Understanding Telecom Networks
AI in Traffic Management
Resource Allocation
Spectrum Management
Energy Management
Predictive Maintenance
Digital Twins
AI-Driven Network Planning
Self-Optimizing Networks (SON)
AI in 5G

Enhancing Customer Experience

Personalized Customer Interactions
AI-Driven Chatbots and Virtual Assistants
Proactive Customer Support
Sentiment Analysis
Optimizing Call Center Operations
AI in Billing and Payments
Streamlining Onboarding Process
Personalized Marketing Campaigns
Enhancing Customer Loyalty Programs

AI in Network Security

Threat Detection and Prevention
Predictive Threat Modeling
Vulnerability Management
Enhanced Identity and Access Management (IAM)
Automating Incident Response
Learning and Evolving Security Systems
Fraud Detection and Prevention
Protecting User Privacy
Securing IoT Devices

The Future of AI in Telecom

Expansion of 5G and Arrival of 6G
Autonomous Networks
Role of AI in Edge Computing
AI and Blockchain Synergy
Ethical Implications of AI in Telecom
AI Development and Industry Collaboration
Conclusion

Prerequisites

None.

AI and Cybersecurity – Apis Training

AI in Telecom

AI in Telecom

Virtual Classroom

Classroom Training

Course Contents

Target Audience

Course outline

Prerequisites

The Resilience Cycle for NIS2 Readiness

The Resilience Cycle for NIS2 Readiness

Virtual Classroom

Course Contents

Led by Two Complementary Experts

Key Learning Outcomes

Course outline

Who Should Attend

Cybersecurity for 5G – Threats and Defense Techniques

Cybersecurity for 5G - Threats and Defense Techniques

Virtual Classroom

CONTENTS

Course Objectives

Course outline

Target Audience

Cybersecurity for 5G – Threats and Compliance Strategies

Cybersecurity for 5G - Threats and Compliance Strategies

Virtual Classroom

CONTENTS

Course Objectives

Course outline

Target Audience

Cybersecurity for 5G

Cybersecurity for 5G

Virtual Classroom Training

CONTENTS

COURSE OBJECTIVES

Course outline

Target Audience

AI for Telecom in an Hour

AI for Telecom in an Hour

eLearning

Course outline

Prerequisites