Cloud SecOps

IT Courses

This comprehensive 120-hour curriculum provides a complete journey from cloud security fundamentals to advanced AI-powered SecOps. The…

AdminElevate
24
120h
0
(0)

This comprehensive 120-hour curriculum provides a complete journey from cloud security fundamentals to advanced AI-powered SecOps. The project-based approach ensures practical skills with industry-standard tools, while the extensive AI integration modules—including agentic AI automation , intelligent copilots , and Graph RAG for threat analysis —prepare students for the future of intelligent cloud security operations where AI transforms security from reactive to predictive and autonomous. Students will graduate with a robust portfolio of 6+ projects demonstrating their expertise across the entire Cloud SecOps spectrum and will be prepared for roles such as Cloud Security Engineer, SecOps Analyst, and AI Security Specialist.

What Will You Learn?

Master cloud computing fundamentals across AWS and Azure platforms including compute, storage, networking, and security services
Build and optimize CI/CD pipelines using Jenkins, GitHub Actions, and Azure DevOps for automated software delivery
Implement Infrastructure as Code (IaC) using Terraform and AWS CloudFormation to provision and manage cloud resources reproducibly
Containerize applications with Docker and orchestrate containers using Kubernetes for scalable, resilient deployments
Configure and manage configuration management tools like Ansible for automated server provisioning
Leverage AI-powered DevOps tools including GitHub Copilot, AI copilots for monitoring, and intelligent incident response systems to enhance productivity and reduce downtime
Implement AI-driven monitoring and observability using AI copilots to transform raw telemetry into actionable insights and reduce alert fatigue
Apply MLOps principles to manage machine learning workflows in production environments

Course Curriculum

Cloud Computing & Security Fundamentals

Cloud deployment models: public, private, hybrid, multi-cloud
Shared responsibility model across AWS, Azure, GCP
Cloud security domains: IAM, network, data, infrastructure, application, compliance
Regulatory frameworks: HIPAA, GDPR, SOC2, PCI DSS, FedRAMP
Zero Trust architecture principles in cloud environments

Identity and Access Management

AWS IAM: users, groups, roles, policies, trust relationships
Azure AD: identities, RBAC, conditional access, managed identities
Principle of least privilege implementation
Privileged access management in cloud
Identity federation and SSO

Network Security in the Cloud

AWS VPC design: subnets, route tables, internet/NAT gateways, VPC peering, transit gateways
Azure Virtual Network: subnets, network security groups, Azure Firewall, VNet peering
Security groups vs. network ACLs
Web Application Firewall (WAF) configuration
DDoS protection (AWS Shield, Azure DDoS Protection)
Private connectivity: VPN, Direct Connect, ExpressRoute

Data Protection & Encryption

Encryption at rest: AWS KMS, SSE-S3, EBS encryption, RDS encryption
Encryption in transit: TLS/SSL, certificate management
AWS Secrets Manager vs. Systems Manager Parameter Store
Azure Key Vault for secrets and certificates
Data classification and protection policies

Project 1: Cloud Security Posture Assessment

Design cloud architecture for HIPAA-compliant healthcare application
Implement IAM roles with least privilege
Configure secure VPC with public/private subnets
Set up encryption for data at rest and in transit
Document security controls mapped to compliance requirements
Conduct security posture assessment with AWS Security Hub

Cloud Logging and Monitoring Fundamentals

AWS CloudTrail: management events, data events, insights
AWS CloudWatch: metrics, logs, alarms, dashboards
AWS Config: configuration tracking, compliance rules
Azure Monitor: activity logs, metrics, log analytics
Azure Security Center and Azure Defender

Threat Detection Services

AWS GuardDuty: threat detection findings, threat intelligence
AWS Detective: root cause analysis
Azure Sentinel: cloud-native SIEM
Third-party CNAPP platforms: Wiz, CrowdStrike, Prisma Cloud

SIEM Implementation and Log Management

SIEM architecture in cloud environments
Log aggregation strategies across multi-cloud
Log retention and cost optimization
Creating detection rules and correlation
Alert fatigue and noise reduction strategies

Project 2: Security Monitoring Implementation

Deploy comprehensive monitoring across AWS environment
Configure CloudTrail, GuardDuty, and Security Hub
Set up Azure Sentinel with log ingestion from AWS
Create custom detection rules
Build monitoring dashboard with key security metrics
Document monitoring architecture and response procedures

Infrastructure as Code Fundamentals

Terraform basics: providers, resources, variables, state management
AWS CloudFormation: templates, stacks, change sets
Infrastructure as Code security principles
Secret management in IaC workflows

Policy as Code and Compliance Scanning

Checkov for Terraform security scanning
tfsec for infrastructure security analysis
AWS CloudFormation Guard for policy enforcement
Open Policy Agent (OPA) and Rego language
Integrating security scanning into CI/CD pipelines

Secure CI/CD Pipeline Integration

CI/CD security best practices
Integrating security scanning in GitHub Actions
AWS CodePipeline with security stages
Preventing misconfigurations from reaching production
Automated remediation of IaC findings

Project 3: Secure IaC Pipeline

Design Terraform modules for secure cloud infrastructure
Implement comprehensive security scanning with Checkov
Create GitHub Actions CI/CD pipeline
Document pipeline security controls
Demonstrate prevention of misconfigured infrastructure

Incident Response Fundamentals

Incident response lifecycle: preparation, detection, containment, eradication, recovery, lessons learned
Cloud-specific incident response challenges
NIST SP 800-61 framework application to cloud
Incident response team roles and responsibilities
Communication and escalation procedures

Forensic Data Collection in Cloud

Collecting evidence from cloud environments
Preserving volatile data (memory, processes)
Disk and snapshot forensics
CloudTrail and log analysis for investigations
Chain of custody in cloud incidents

Automated Containment and Remediation

Create Lambda function to quarantine compromised EC2 instance
Set up CloudWatch Events to trigger automated response
Implement automated IAM policy remediation
Create ServiceNow tickets for security incidents

Post-Incident Activities

Root cause analysis techniques
Lessons learned documentation
Improving detection and response capabilities
Compliance reporting after incidents
Communication with stakeholders

Project 4: Incident Response Simulation

Design complete incident response plan for cloud environment
Simulate security incident (compromised credentials, data exfiltration)
Execute full incident response lifecycle
Create incident report for management
Demonstrate automated response capabilities

AI in Cloud Security Operations

How AI is transforming SecOps: from automation to intelligence
AI as force multiplier for security teams
Key AI applications across cloud security lifecycle
Evolution from reactive to predictive operations
AI ethics and governance in security

Agentic AI Automation

Agentic AI vs. traditional automation
Swimlane Turbine platform for security automation
AI agents for vulnerability response management
Autonomous decision-making with guardrails
Reducing mean time to respond (MTTR) by up to 70%

Intelligent Copilots for SecOps

Panther AI for intelligent alert triage
Google Cloud Duet AI for security operations
Natural language query translation for investigations
Automated case summaries and analysis
Reducing triage time by 50% or more

Autonomous Remediation and Compliance

Prowler Lighthouse AI for autonomous security
MCP Server integration with developer workflows
Automated remediation pull requests
Compliance monitoring with audit-ready reporting
Self-healing cloud infrastructure

Anomaly Detection and Threat Prediction

Machine learning for behavioral anomaly detection
Predictive threat intelligence
Reducing false positives with AI-powered filtering
Detecting subtle attack patterns
Continuous model training and adaptation

Project 5: AI-Enhanced Security Dashboard

Build comprehensive security dashboard with AI capabilities
Integrate with cloud security tools (AWS Security Hub, GuardDuty)
Use AI APIs for intelligent features
Document AI applications in SecOps workflows
Present findings with actionable recommendations

Graph RAG for Threat Intelligence

Graph RAG (Retrieval-Augmented Generation) architecture
Hierarchical lexical graphs for security knowledge
Entity relationship extraction from security data
Long-term organizational memory vs. short-term operational data
Reducing 50,000 security issues to 1,300 actionable items

Cognitive Substrate and AI Factory Architecture

Long-term memory (lexical graphs) vs. short-term memory (document graphs)
Immutable separation of AI-generated vs. human-generated content
Recipe-based automation (not brittle code)
Check-do-check pattern for safe automation

Agentic AI for Security Operations

Swimlane Hero AI agents for security functions
Deep agents handling cognitive load
Autonomous deep agents for investigation
Fleet of specialized AI agents
Human-in-the-loop for high-impact decisions

Autonomous Cloud Defense

Self-healing cloud infrastructure
Predictive vs. reactive operations
Reducing breach costs by up to 43% with autonomous systems
MITRE ATT&CK for cloud framework integration
Preparing for the next wave: fully autonomous security

Project 6: Graph RAG Threat Analysis System

Build comprehensive threat analysis system using Graph RAG principles
Process simulated security findings (aim for 50,000+ events)
Distill to actionable insights with graph-based analysis
Generate remediation recommendations
Document architecture and performance metrics
Demonstrate reduction in investigation time

Final Project: Complete Cloud SecOps Transformation

Phase 1: Secure Infrastructure Design
Phase 2: Security Monitoring Implementation
Phase 3: Infrastructure as Code with Security
Phase 4: Automated Incident Response
Phase 5: AI-Powered Security Operations

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Cloud SecOps

What Will You Learn?

Course Curriculum

Cloud Computing & Security Fundamentals

Cloud deployment models: public, private, hybrid, multi-cloud

Shared responsibility model across AWS, Azure, GCP

Cloud security domains: IAM, network, data, infrastructure, application, compliance

Regulatory frameworks: HIPAA, GDPR, SOC2, PCI DSS, FedRAMP

Zero Trust architecture principles in cloud environments

Identity and Access Management

AWS IAM: users, groups, roles, policies, trust relationships

Azure AD: identities, RBAC, conditional access, managed identities

Principle of least privilege implementation

Privileged access management in cloud

Identity federation and SSO

Network Security in the Cloud

AWS VPC design: subnets, route tables, internet/NAT gateways, VPC peering, transit gateways

Azure Virtual Network: subnets, network security groups, Azure Firewall, VNet peering

Security groups vs. network ACLs

Web Application Firewall (WAF) configuration

DDoS protection (AWS Shield, Azure DDoS Protection)

Private connectivity: VPN, Direct Connect, ExpressRoute

Data Protection & Encryption

Encryption at rest: AWS KMS, SSE-S3, EBS encryption, RDS encryption

Encryption in transit: TLS/SSL, certificate management

AWS Secrets Manager vs. Systems Manager Parameter Store

Azure Key Vault for secrets and certificates

Data classification and protection policies

Project 1: Cloud Security Posture Assessment

Design cloud architecture for HIPAA-compliant healthcare application

Implement IAM roles with least privilege

Configure secure VPC with public/private subnets

Set up encryption for data at rest and in transit

Document security controls mapped to compliance requirements

Conduct security posture assessment with AWS Security Hub

Cloud Logging and Monitoring Fundamentals

AWS CloudTrail: management events, data events, insights

AWS CloudWatch: metrics, logs, alarms, dashboards

AWS Config: configuration tracking, compliance rules

Azure Monitor: activity logs, metrics, log analytics

Azure Security Center and Azure Defender

Threat Detection Services

AWS GuardDuty: threat detection findings, threat intelligence

AWS Detective: root cause analysis

Azure Sentinel: cloud-native SIEM

Third-party CNAPP platforms: Wiz, CrowdStrike, Prisma Cloud

SIEM Implementation and Log Management

SIEM architecture in cloud environments

Log aggregation strategies across multi-cloud

Log retention and cost optimization

Creating detection rules and correlation

Alert fatigue and noise reduction strategies

Project 2: Security Monitoring Implementation

Deploy comprehensive monitoring across AWS environment

Configure CloudTrail, GuardDuty, and Security Hub

Set up Azure Sentinel with log ingestion from AWS

Create custom detection rules

Build monitoring dashboard with key security metrics

Document monitoring architecture and response procedures

Infrastructure as Code Fundamentals

Terraform basics: providers, resources, variables, state management

AWS CloudFormation: templates, stacks, change sets

Infrastructure as Code security principles

Secret management in IaC workflows

Policy as Code and Compliance Scanning

Checkov for Terraform security scanning

tfsec for infrastructure security analysis

AWS CloudFormation Guard for policy enforcement

Open Policy Agent (OPA) and Rego language

Integrating security scanning into CI/CD pipelines

Secure CI/CD Pipeline Integration

CI/CD security best practices

Integrating security scanning in GitHub Actions

AWS CodePipeline with security stages

Preventing misconfigurations from reaching production

Automated remediation of IaC findings

Project 3: Secure IaC Pipeline

Design Terraform modules for secure cloud infrastructure

Implement comprehensive security scanning with Checkov

Create GitHub Actions CI/CD pipeline