Technical Expertise · Cloud Security Architecture

Cloud Security Architecture, Built for Enterprise Reality.

Reference architectures, security control design, implementation patterns, and risk-based decisions across cloud and Microsoft security platforms.

Technical expertise

Seven disciplines, one control model.

The technology areas I work in — each grounded in the enterprise experience described on this page and in my career history.

Cloud Security

  • Microsoft Azure
  • Amazon Web Services
  • Google Cloud Platform
  • CNAPP
  • CSPM
  • CASB
  • Cloud governance
  • Cloud workload protection

Microsoft Security

  • Microsoft Defender XDR
  • Defender for Endpoint
  • Defender for Identity
  • Defender for Office 365
  • Defender for Cloud
  • Microsoft Sentinel
  • Microsoft Entra ID
  • Microsoft Purview

Identity & Zero Trust

  • Identity and Access Management
  • Conditional Access
  • Privileged Identity Management
  • Multi-Factor Authentication
  • Identity Governance
  • Zero Trust Architecture

Data Security

  • Data Loss Prevention
  • Information Rights Management
  • Sensitivity Labels
  • Customer-Managed Keys
  • Azure Key Vault
  • Data Governance

Infrastructure Security

  • Azure Kubernetes Service
  • Web Application Firewall
  • Private Endpoints
  • Azure Policy
  • Kubernetes Security
  • Cloud Network Security

Endpoint & Device Security

  • Microsoft Intune
  • Workspace ONE UEM
  • Microsoft Defender for Endpoint
  • Endpoint Compliance

DevSecOps & Application Security

  • Azure DevOps
  • CI/CD Security
  • API Security
  • OWASP
  • Secure Software Development Lifecycle
  • Container Security

How the layers fit

Identity first, telemetry everywhere.

Every capability below sits in this layered model: identity decides access, workloads are segmented, and every layer reports to security operations under one governance model.

Layered enterprise security architectureUsers pass through an identity and access layer into cloud workloads and an AI agent zone. A tool gateway separates agent decisions from privileged tools. Telemetry flows to security operations, and governance spans every layer.GOVERNANCE · POLICY · OWNERSHIPUsers & devicesIdentity & accessEntra ID · Conditional Access · PIMCloud workloadsAzure · AWS · GCPAI agent trust boundaryAgent orchestrationTool gatewaySecurity telemetry & responseSentinel · Defender XDR · audit records
Dashed lines show telemetry; the gold dashed outline marks the AI agent trust boundary.

Capability areas

Seven areas, one control model.

Identity and Zero Trust

NIST SP 800-207 · NIST CSF 2.0 PR.AA

Problem: Network location no longer indicates trust; identity is the primary control plane and the most attacked one.

Objectives

  • — Strong, phishing-resistant authentication
  • — Context-aware access decisions
  • — Minimal standing privilege

Components

  • — Microsoft Entra ID
  • — Conditional Access
  • — Privileged Identity Management
  • — Device compliance signals

Design considerations

  • — Phased policy rollout in report-only mode first
  • — Break-glass accounts excluded and monitored
  • — Workload identities governed like users

Validation · Sign-in log review against policy intent, What If simulations and periodic access reviews.

Microsoft Defender Security Platform

Problem: Endpoint, identity, email and cloud signals arrive in silos, delaying correlation and response.

Objectives

  • — Unified incident view across domains
  • — Consistent automated containment
  • — Reduced alert noise

Components

  • — Defender for Endpoint
  • — Defender for Identity
  • — Defender for Office 365
  • — Defender XDR

Design considerations

  • — Automation levels set per device group
  • — Attack surface reduction rules audited before block
  • — Sensor coverage tracked as a metric

Validation · Onboarding coverage reports and controlled attack simulations.

Cloud Security Posture Management

CIS Benchmarks · Microsoft Cloud Security Benchmark

Problem: Misconfiguration drifts faster than manual review can catch it.

Objectives

  • — Continuous baseline assessment
  • — Prioritized, owned remediation
  • — Governed exceptions

Components

  • — Defender for Cloud
  • — Azure Policy
  • — Secure score
  • — Regulatory compliance dashboards

Design considerations

  • — Deny effects only after audit evidence
  • — Exceptions time-bound with compensating controls
  • — Findings routed to resource owners

Validation · Trend of high-severity findings by owner and exception age.

Security Operations and Detection

MITRE ATT&CK · NIST CSF 2.0 DE/RS

Problem: Detections that are never tuned against real telemetry produce noise or blind spots.

Objectives

  • — Coverage mapped to attacker techniques
  • — Tuned, documented analytics
  • — Bounded automated response

Components

  • — Microsoft Sentinel
  • — KQL analytics rules
  • — Logic Apps playbooks
  • — MITRE ATT&CK mapping

Design considerations

  • — Ingestion cost versus investigative value
  • — Retention aligned to regulatory need
  • — Human approval for disruptive actions

Validation · Detection testing with known-bad activity and false-positive review.

Data Security and Information Protection

Problem: Sensitive data moves through email, collaboration and AI assistants faster than policy follows.

Objectives

  • — Consistent classification
  • — Leakage prevention at egress points
  • — Key custody aligned to jurisdiction

Components

  • — Microsoft Purview
  • — Sensitivity labels
  • — DLP policies
  • — Azure Key Vault / HSM

Design considerations

  • — Simulation mode before enforcement
  • — Label taxonomy users can apply correctly
  • — Key lifecycle ownership

Validation · DLP incident review and label coverage reporting.

Infrastructure and Workload Security

Problem: Flat networks and public endpoints expand blast radius.

Objectives

  • — Segmentation and private connectivity
  • — Hardened workloads
  • — Repeatable secure deployment

Components

  • — VNets and NSGs
  • — Private Endpoints
  • — Application Gateway / WAF
  • — Infrastructure as code

Design considerations

  • — Default-deny inbound
  • — Secrets never in pipelines
  • — Drift detection on baselines

Validation · Exposure scans and configuration drift reports.

Multi-cloud Security Governance

CSA Cloud Controls Matrix · ISO/IEC 27001

Problem: Azure, AWS and Google Cloud express the same control intent in different primitives.

Objectives

  • — Common control objectives
  • — Clear shared-responsibility ownership
  • — Comparable reporting

Components

  • — Cloud-native IAM
  • — KMS services
  • — Central logging
  • — CSPM across providers

Design considerations

  • — Map intent, not product features
  • — One owner per control per cloud
  • — Normalized log schema

Validation · Cross-cloud control matrix with evidence per provider.

Architecture case studies

Synthetic and sanitized designs.

Case studies use synthetic examples or sanitized patterns. No client names, network details, identifiers or configurations are published.