DoDAF (EA) -- Use Artifacts/Views/Models/Unified Modeling Language (UML)
Model-Based Systems Engineer (MBSE) -- To use models
Well-Architected Framework (WAF) -- Cloud Service Providers (CSF)
Scaled Agile Framework (SAFe) -- Agile-base
TOGAF (The Open Group Architectural Framework) -- Private Sector. Kinda DoDAF.
Azure Well-Architected Framework (WAF)
BLUF: Azure WAF is a roadmap for achieving architectural excellence in the cloud. A set of guidelines and resources from Microsoft to help you build, run, and optimize secure, reliable, and cost-effective workloads on Azure. -- By following its principles and utilizing its resources, one can build and maintain secure, reliable, cost-effective cloud workloads supporting your business needs.
Structure (Azure WAF Pillars): (5 Pillars / Principles)
Five Pillars: (1) Cost Optimization, (2) Operational Excellence, (3) Performance Efficiency, (4) Reliability, and (5) Security. Each represents a crucial aspect of well-architected workloads:
Cost optimization: Managing costs to maximize the value generated by your Azure resources.
Focus on business value: Align resource deployment with specific business needs and avoid over-provisioning.
Choose the right service tier: Select the service tier that meets your desired performance and cost needs.
Embrace rightsizing: Regularly monitor and adjust resource allocation based on actual usage.
Utilize reserved instances and savings plans: Secure discounts by committing to resources for a specific period.
Automate cost management: Implement tools and processes to optimize resource utilization and avoid wasting money.
Operational excellence: Streamlining operations for efficient management and performance.
Design for manageability: Build architectures that are easy to deploy, configure, and maintain.
Automate operations: Use automation tools to reduce manual tasks and improve efficiency.
Monitor and log everything: Track key metrics and events to identify and resolve issues quickly.
Implement continuous improvement: Regularly review and optimize your operational processes.
Build for disaster recovery: Design your architecture to withstand outages and data loss.
Performance efficiency: Optimizing infrastructure to deliver responsive and scalable applications.
Optimize for workload requirements: Choose services and resources that match your workload's performance needs.
Apply performance best practices: Implement caching, content delivery networks, and other optimization techniques.
Scale efficiently: Design your architecture to handle fluctuating loads and scale dynamically.
Monitor performance metrics: Continuously track and analyze performance metrics to identify bottlenecks.
Utilize performance diagnostics tools: Use tools provided by Azure to diagnose and resolve performance issues. --TOOLS (4) --
Azure Monitor (Monitor the health and performance of your Azure resources, including VMs, applications, and services)
Azure App Service diagnostics or SQL Server on Azure VM performance diagnostics (Provides a central location to access service-specific troubleshooting guides, automated troubleshooters, and curated solutions for common issues)
Azure Monitor Application Insights: Monitors web apps, APIs, and mobile apps deployed on Azure or on-prem.
Azure Log Analytics (Collects and analyzes logs from various Azure resources and on-prem systems).
Reliability: Building resilient systems that can withstand disruptions and maintain availability.
Design for resiliency: Build redundant and fault-tolerant architectures.
Implement application health checks: Regularly monitor the health of your applications and services.
Automate failover and recovery: Establish automated processes for responding to failures and outages.
Minimize single points of failure: Avoid situations where a single component can bring down the entire system.
Perform regular backups and testing: Ensure critical data is backed up and disaster recovery plans are tested regularly.
Security: Protecting your data and resources from unauthorized access and attacks.
Implement Least Privilege: Grant users and applications the minimum level of access required. -- TOOLS (5) --
Azure AD (RBAC-Role-Based Access Control, pre-defined roles with specific permissions; MFA; Conditional Access-More access control factors).
Azure Key Vault (Stores sensitive info like passwords, connection strings, and encryption keys in a central, highly secure location).
Azure Security Center (Recommendations and insights and optimizing RBAC permissions).
Azure Policy (Create and enforce security policies).
Azure SQL Database (Supports database roles to assign specific permissions to users within the database).
Use Strong Authentication and Authorization: Implement MFA and role-based access control (RBAC).
MS Entra ID (aka Azure AD): MFA; Conditional Access; Identity Protection-Provides security features like password protection, brute force attack detection, and suspicious sign-in activity monitoring to enhance user authentication security; Secure External Access; SSO).
Azure Application Insights (Tracks user authentication events and can detect suspicious login).
Azure Key Vault (Stores sensitive info like passwords, connection strings, and encryption keys in a central, highly secure location).
Azure SQL Database (Supports database roles to assign specific permissions to users within the database).
Encrypt Data At Rest and In Transit: Protect sensitive data by encrypting it both when stored and transmitted.
Server-Side Encryption (SSE): (3)
(1) Azure Storage Service Encryption (SSE): Automatically encrypts data at rest for Azure Blob Storage and Azure File Shares, transparently managing encryption keys and decryption without impacting application performance. (2) Azure SQL Database Transparent Data Encryption (TDE): Encrypts the entire database file at rest using industry-standard encryption algorithms, including AES-256. Encryption keys are managed by Azure Key Vault for enhanced security. (3) Azure Cosmos DB Transparent Data Encryption (TDE): Offers server-side encryption for data at rest across all Azure Cosmos DB document databases.
Client-Side Encryption: (2)
(1) Azure Storage client libraries: Support client-side encryption for blobs and queues before uploading to Azure Storage, offering greater control over encryption keys and encryption algorithms. (2) Azure Data Encryption for VMs: Secures data at rest by encrypting virtual disk files on Azure VMs using industry-standard tools like BitLocker (Windows) or dm-crypt (Linux). You manage the encryption keys yourself or leverage Azure Key Vault for centralized key management.
3. Azure Key Vault:
Secure key management: Provides a central, highly secure location to store and manage cryptographic keys used for encrypting data across various Azure services. By controlling access to these keys, you can enhance the overall security of your data encryption strategy.
4. Azure Managed Services:
Many Azure managed services like Azure SQL Managed Instance, Azure Cosmos DB, and Azure App Service offer built-in data encryption for both data at rest and in transit. You configure and manage the encryption settings within the service itself.
Additional best practices:
Encrypt sensitive data wherever possible: Prioritize encrypting data that contains confidential information like personally identifiable information (PII) or financial data.
Choose the appropriate encryption algorithm: Consider the security needs and performance requirements of your data when selecting an encryption algorithm like AES-256 or RSA.
Rotate encryption keys regularly: Periodically change your encryption keys to mitigate the risk of compromise even if an attacker gains access to a previous key.
Monitor and audit encryption activity: Implement logging and monitoring solutions to track encryption activity and identify potential security threats or unauthorized access attempts.
Monitor for Security Threats: Continuously monitor your environment for potential security vulnerabilities and attacks.
Implement a Layered Security Approach: Utilize a combination of security controls like firewalls, intrusion detection systems, and security incident response plans.
Design Principles: Each pillar is supported by a set of design principles, outlining fundamental best practices for achieving that pillar's goals.
Design Recommendations: Within each principle, you'll find specific recommendations for implementing its best practices in your Azure workloads.
Design Tradeoffs: WAF acknowledges that sometimes optimizing one pillar might entail compromises with others. It guides navigating these tradeoffs and making informed decisions.
Value & Benefits: (5)
Enhanced security: By following WAF best practices, you can build robust and secure cloud architectures, minimizing risks and protecting your data.
Improved performance: Optimizing your infrastructure using WAF can lead to faster, more responsive applications and services.
Reduced costs: Efficient resource utilization and streamlined operations can help you save money on your Azure deployments.
Increased reliability: Well-architected systems are less prone to failures and can remain available even during unexpected events.
Agility and scalability: WAF principles promote flexible and scalable architectures that can adapt to changing business needs.
Resources: -- BLUF: WAF provides a wealth of resources to help you implement its principles:
Azure Well-Architected Review: A tool to assess your existing Azure workloads against WAF best practices and identify areas for improvement.
Azure Advisor: A service that recommends ways to optimize your Azure resources for cost, performance, and security.
Documentation: A comprehensive library of white papers, guides, and templates to support your WAF journey.
Partners and support: Access to a network of partners and Microsoft support to assist you in implementing WAF successfully.
Data Architecture.
BLUF: How an organization will manage its data assets to meet business needs. It defines the structure, flow, storage, and technology for data. -- Focuses on optimizing data workflows, managing data pipelines, and operation of data systems. -- Skills: Python, SQL, ETL (Extract, Transfer, Load)/ELT, DBT (Data Build Type).
R&R: A data architect designs, creates, and manages an organization's data infrastructure. -- Analogy: Think of them as the chief engineer of a city's water system; they don't lay the pipes themselves but design the entire network, ensuring water (data) flows correctly, is clean (quality), and reaches its destination safely (security). -- They Do: (1) Enterprise Strategy (2) Data Modeling (3) Technology Selection (4) Governance & Compliance (5) Focus on the "Big Pix" data ecosystem.
Data Pipeline Architect (aka "Engineers"): The focus on the "pipes" that move data from one place to another. They are the "plumbers" who focus on the practical, hands-on implementation of the data architect's designs. -- They Do: (1) Hands-on Implementation: They build, test, and maintain the data pipelines that extract, transform, and load (ETL) data. (2) Orchestration: They use tools to automate and schedule data workflows. (3) Performance and Optimization: They monitor the performance of data pipelines and troubleshoot issues to ensure data flows smoothly. (4) Data Transformation: They write the code and scripts to clean, normalize, and transform raw data into a usable format for analytics and business intelligence. (5) Specific Focus: Their scope is more limited and tactical, centered on the mechanics of data movement and transformation within the larger architecture.
A Day In the Life:
Morning: Strategic Planning & Meetings -- (1) Reviewing architectural blueprints and data models for new projects. (2) Meeting with business leaders to understand their goals and translate them into technical data requirements. (3) Collaborating with data engineers, data scientists, and software developers to ensure the data architecture supports their work.
Afternoon: Design & Problem-Solving -- (1) Designing the flow of data from various sources into data warehouses or data lakes. (2) Selecting the right technologies (e.g., specific databases, cloud services) for a new initiative. (3) Troubleshooting performance bottlenecks or data quality issues in existing systems.
Late Afternoon: Documentation & Governance -- (1) Documenting data models, standards, and best practices. (2) Ensuring the architecture complies with data governance (guidance) policies and security regulations. (3) Planning for future scalability and technology adoption.
Data Pipeline / Lakehouse Architecture (using Azure): (5) -- BLUF: To Move, Transform, & Analyze.
High-Level Data Pipeline Flow (8): (1. Raw data Sources > (2. ADF) > (3. ADLS: Raw) > (4. Azure Databricks) > (5. ADLS: Cleaned) > (6. ADF) > (7. Azure Synapse Analytics) > (8. Power BI & Reporting Tools).
Raw data sources: Like Excel (or CSV file), etc.
Azure Data Factory (ADF): (Data Integration: ETL/ELT) the process of collecting and importing (moving) raw data from one place to another, to a data warehouse or Azure Data Lake (Storage), where it can be processed, analyzed, and stored. It's the critical first step in any data pipeline, making data available for BI, analytics, and machine learning.
AV-2: ETL (Extract, Transfer, Load) ; ELT (Extract, Load, Transfer)
Action: ADF acts as the primary data integration (moving data) tool. It collects raw data from various sources (databases, applications, IoT devices, etc.) and orchestrates its movement.
Purpose: The goal here is to centralize all incoming data into a single, scalable storage location without changing its original format.
Azure Data Lake Storage Gen2 (ADLS) (Data Lake: Storage): This is the ideal storage for consolidating all raw data (structured, semi-structured, and unstructured) in its native format. -- It is built on top of Azure Blob Storage.
Action: All the raw data collected by ADF is stored in ADLS. This service is a highly scalable and cost-effective data lake solution.
Purpose: ADLS serves as the central "repository" or "single source of truth" for all your data, regardless of its structure.
Azure Databricks (Data Transformation: ETL & ELT): This is a collaborative, Apache Spark-based analytics service that can be used to cleanse, transform, and prepare the raw data in ADLS, creating the "single source of truth." -- Processes large data for Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) workloads. -- It reads raw data from sources like ADLS and transform it into a cleaned, structured format for analysis.
Action: Azure Databricks reads the raw data from ADLS. Using its powerful Apache Spark engine, it performs the heavy-duty work of cleansing, transforming, and structuring the data.
Purpose: This step processes the raw data into a clean, refined format suitable for analysis and reporting.
Microsoft Purview: (Data Governance: Guidance) and discovery, ensuring that the consolidated data is well-documented and easily found by the right people, reducing data duplication.
Action: Microsoft Purview works in parallel with the entire pipeline. It discovers and documents all the data assets in ADLS, Azure Databricks, and Azure Synapse Analytics.
Purpose: This service provides a comprehensive view of your data landscape, helping you understand where data comes from, how it's used, and who can access it. It ensures data is well-governed and discoverable.
Azure Synapse Analytics (Data Warehouse: Data Processing & Analysis): It can serve as the data warehouse where the refined and structured data is loaded for BI and reporting.
Action: Once the data is refined, it's loaded into a dedicated SQL pool within Azure Synapse Analytics, which acts as the data warehouse (Data Processing & Analysis).
Purpose: This is where the processed data is stored for high-performance business intelligence (BI) and reporting. It's optimized for analytical queries from tools like Power BI.
AuthS (Governance & Compliance).
Regulatory & Legal Frameworks:
GDPR (General Data Protection Regulation): For protecting the personal data of EU citizens.
* HIPAA (Health Insurance Portability and Accountability Act): For protecting sensitive patient health information in the U.S.
CCPA (California Consumer Privacy Act): For protecting the personal information of California residents.
* ISO 27001: An international standard for information security management systems.
Industry Standards & Best Practices:
* DAMA-DMBOK2 (Data Management Body of Knowledge): A comprehensive guide published by DAMA International that defines a standard framework for data management. It's a core resource for data architects.
* NIST Cybersecurity Framework: A set of voluntary guidelines for managing cybersecurity risk.
* HITRUST CSF: A certifiable framework that helps organizations manage information risk and compliance.
Enterprise Guidance:
* Internal Data Governance Policies: Rules and guidelines set by the organization for managing data assets.
* Enterprise Architecture Frameworks: Such as the Federal Enterprise Architecture Framework (FEAF) ro DoDAF for government agencies, which provides a common language and framework for describing and analyzing enterprise investments.
Data Pipeline Architecture.
BLUF: A data pipeline architecture is the blueprint for how data moves through a system, from its source to its destination. It defines the stages—from ingestion, transformation, and storage—and the technologies and processes that connect them. -- PURPOSE: To automate and optimize the data flow, ensuring it's reliable, scalable, and ready for analysis. Think of it as a set of instructions for a factory assembly line, but for data.
My Experience:
Roadmap development: Following the ETL pattern (Extract-Transform-Load) and used Power BI, Power Automate (Canvas), and Lucidchart as visualizations & reporting.
ETL & ELT (~ 2 Common Patterns): [YouTube]
* ETL (Extract, Transform, Load) -- BLUF: ETL is the traditional approach. This process is well-suited for smaller, structured datasets and environments with on-premise data warehouses. A major advantage is that the data is already in the final, usable format when it arrives at the destination, which can make analysis faster. A downside is that the transformation step can be slow and requires a dedicated server, which can be a bottleneck for large volumes of data. It involves:
Extract: Data is pulled from various source systems, such as databases, files, and applications.
Transform: The extracted data is cleaned, structured, and manipulated in a staging area before it's loaded. This step can involve things like filtering out bad data, joining data from different sources, and standardizing formats.
Load: The transformed and "clean" data is then loaded into a target data warehouse.
ELT (Extract, Load, Transform) -- BLUF: ELT is a more modern approach that gained popularity with the rise of cloud computing and cloud data warehouses. ELT is ideal for big data and unstructured data because it can handle massive volumes quickly. Since raw data is retained, it provides greater flexibility, as analysts can perform different transformations on the same raw data for different use cases. The main trade-off is that it might require more storage space and could expose raw, sensitive data in the data warehouse before it's transformed. It involves:
Extract: Data is pulled from various sources.
Load: The raw, unprocessed data is immediately loaded into a data warehouse or data lake. This happens much faster than in ETL because no intermediate transformation is required.
Transform: The data is transformed after it's loaded, using the powerful processing capabilities of the cloud data warehouse.
Data Pipeline Architecture (using Azure)-(How to Implement): (4)
Goal 1: Improve Data Accessibility and Timeliness -- Ensure that users across the organization have fast, easy access to the most up-to-date data for their reporting and analysis needs.
Objectives:
Reduce Data Latency: (1) Implement a data pipeline that can ingest and process data in real-time or near-real-time (e.g., within minutes or hours, not days). (2) Establish Service Level Agreements (SLAs) for data freshness (e.g., "all daily sales data must be available in the data warehouse by 9:00 AM every business day").
Standardize Data Access: (1) Create a centralized data repository (like a data warehouse or data lake) to serve as a single source of truth. (2) Provide a clear, well-documented data catalog so that users can easily find and understand the available datasets.
Automate Data Delivery: (1) Eliminate manual, ad-hoc data requests and deliveries. (2) Automate the entire data flow from source to destination, reducing human effort and the risk of error.
Azure Services:
Azure Data Factory (ADF): ADF is a cloud-based ETL/ELT service that's excellent for orchestrating and automating data movement. It has over 90 built-in connectors to pull data from various sources, making data easily accessible. You can use it to build pipelines that automatically move data from source to destination on a schedule, directly addressing the objective of automating data delivery.
Azure Event Hubs: For real-time data latency objectives, Event Hubs is a fully managed, scalable event ingestion service. It can handle millions of events per second from sources like IoT devices, web applications, and telemetry. It acts as a buffer, ensuring high-velocity data is ingested reliably before being processed by other services.
Goal 2: Enhance Data Quality and Reliability. -- Ensure that the data used for decision-making is accurate, consistent, and trustworthy.
Objectives:
Implement Data Validation: (1) Establish data quality checks at various stages of the pipeline (e.g., during ingestion, transformation, and before loading). (2) Validate data formats, check for missing values, and identify and remove duplicates.
Establish Data Governance: (1) Define clear data ownership and responsibilities for each dataset. (2) Maintain a detailed data lineage to track the origin and transformations of every piece of data.
Build a Robust Error Handling System: (1) Design the pipeline to handle and log failures gracefully without data loss. (2) Set up automated alerts to notify data engineering teams of pipeline failures or data quality issues.
Azure services:
Azure Databricks: Databricks is a unified analytics platform built on Apache Spark. It's great for complex data transformations and quality checks. You can use it to write code (in Python, SQL, etc.) to perform advanced data cleaning, enrichment, and validation at scale. Databricks' integration with tools like Delta Lake also helps in maintaining data quality and consistency by providing ACID transactions for your data lake.
Azure Data Factory: ADF's data flows feature, a visual, code-free transformation designer, can be used to build logic for data quality rules, such as identifying and removing bad data records. It can also manage the orchestration of these data quality checks.
Goal 3: Support Scalability and Growth. -- Build an architecture that can handle increasing data volumes, new data sources, and evolving business needs without major re-engineering.
Objectives:
Design for Scalability: (1) Select tools and technologies that can scale horizontally (e.g., by adding more processing nodes) to handle growing data loads. (2) Use a modular design that allows for the addition of new data sources or transformation logic without disrupting the entire pipeline.
Optimize Performance: (1) Continuously monitor pipeline performance and identify bottlenecks. (2) Implement efficient data formats and compression techniques to reduce storage and processing costs.
Facilitate New Data Integration: (1) Create a standardized process for onboarding new data sources. (2) Develop reusable components and templates for common data extraction and transformation tasks.
Azure services:
Azure Synapse Analytics: *Not Used* Synapse is an integrated analytics service that brings together data warehousing and big data analytics. It offers a serverless and dedicated SQL pool and is designed to handle massive data volumes and complex queries. It's the ideal destination for your processed data, as it provides the scalability needed for BI and machine learning applications. Its built-in data pipeline capabilities, which are based on ADF, also allow for seamless integration of data movement and transformation.
Azure Databricks: Databricks provides an auto-scaling cluster that can automatically adjust its size based on the workload. This directly addresses the objective of designing for scalability and ensures that your data pipeline can handle growing data volumes efficiently without manual intervention.
Goal 4: Improve Operational Efficiency. -- Reduce the manual effort and time required for data preparation and delivery.
Objectives:
Automate Manual Tasks: (1) Automate the scheduling and execution of all data pipeline jobs. (2) Eliminate repetitive manual tasks like data cleanup, report generation, and file transfers.
Centralize Management and Monitoring: (1) Use a single orchestration tool to manage and monitor all pipeline workflows. (2) Create a dashboard to provide a real-time view of the pipeline's health, status, and performance.
Reduce Maintenance Overhead: (1) Choose technologies that require minimal maintenance and support. (2) Implement version control for all pipeline code to simplify updates and rollbacks.
Azure services:
Azure Data Factory: ADF is a core tool for centralized management and monitoring. It provides a visual dashboard to monitor all pipeline runs, see logs, and set up alerts for failures. This eliminates the need to manually track individual jobs and helps reduce maintenance overhead.
Azure Stream Analytics: This service is excellent for real-time operational efficiency. It allows you to analyze and react to streaming data in motion using simple SQL-like queries. For example, it can be used to identify anomalies or trigger an alert when a certain condition is met in real-time sensor data, providing immediate insights and reducing the time to action.
Zero Trust Architecture (ZTA) and Data Pipelines. -- ZTA and data pipelines aren't competing architectures; rather, ZTA is a security model that should be implemented within a data pipeline. ZTA operates on the principle of "never trust, always verify." It assumes that no user, device, or system is inherently trustworthy, even if it's inside the network perimeter. -- ZTA aligns with a data pipeline's need for security by:
Continuous Verification: Every stage of the pipeline—from data ingestion to storage—requires explicit verification. This means that a component won't just trust a data source or another component; it will authenticate and authorize every interaction.
Least Privilege Access: ZTA enforces the principle of least privilege, meaning that each user or service within the pipeline is only granted the minimum access necessary to perform its job. For example, a transformation service would have read-only access to the raw data and write access only to its specific output destination, but it wouldn't have access to other parts of the system.
Micro-segmentation: Networks are divided into smaller, isolated zones. This prevents lateral movement. If one part of the pipeline is compromised, the attacker can't easily move to other parts of the system or access sensitive data.
Monitoring and Logging: All activity within the pipeline is continuously monitored and logged. This helps detect anomalies and potential security threats in real time.
AuthS.
The Data Management Body of Knowledge (DAMA-DMBOK) -- BLUF: The DAMA-DMBOK is the closest thing to a comprehensive standard for the entire data management discipline. Published by DAMA International, it outlines a framework of data management functions, including data governance, data architecture, data modeling, and data integration. -- How it helps: DAMA-DMBOK provides the strategic context for data pipelines. It doesn't tell you which tool to use, but it does define the principles for ensuring data quality, lineage, and security—all of which are critical components of a well-architected pipeline. It's the "what" and "why" behind the process, rather than the "how."
WAF (Well-Architected Framework) -- (via Azure). See below... Other CSP have their own WAF.
DMAIC (Define, Measure, Analyze, Improve, and Control) Framework (A 6 Sigma Approach).
BLUF: DMAIC refers to an "improvement cycle" of process improvement that is data-driven and aims at improving, optimizing, and stabilizing business processes and designs. DMAIC came from PDSA (“plan, do, study, act”).
5 Phases: [Ref]
Define -- Define the problem -- Select the most critical and impactful opportunities for improvement -- The low-hanging fruit, the daily operational improvements.
Measure -- Improve the activity -- Establish a baseline to assess the performance of a given process.
Analyze -- Identify the opportunities for improvement -- The goal is to identify and test the underlying causes of problems to ensure that improvement occurs from deep down, where the problems stem from (the root causes).
Improve -- Set project goals & objectives to make improvements -- Steps (1) Brainstorm and put forth solution ideas (2) Develop a Design of Experiments (DOE) to determine the expected benefits of a solution. (3) Revise process maps and plans according to the data collected in the previous stage (4) Outline a test solution and plan (5) Implement Kaizen events to improve the process (6) Inform all stakeholders about the solution.
Control -- Meet the needs of the customer (internal and external). -- Bring the process under control to ensure its long-term effectiveness, aka "Mututurity Assessment Plan" (a Check-List).
DoD Architectural Framework (DoDAF).
URL via DOD CIO: https://dodcio.defense.gov/Library/DoD-Architecture-Framework/
Interrogatives: The "What (Date)," "How (Function)," "Where (Network)," "Who (People)," "When (Time)," and "Why (Motivation)."
Model List (AV-2): -- BLUF: A List of Artifacts/Models. -- URL: https://dodcio.defense.gov/Library/DoD-Architecture-Framework/dodaf20_models/
My Standard DoDAF Approach: (4 Artifacts)
OV-1 (High-Level Operational Concept Graphic or Process Map): The high-level graphical/textual description of the operational concept. -- An OV-1 can be very minimal or very intricate.
OV-5b (Operational Activity Model) -- A process map/model. Can use a "swimlane" approach (see TekSynap: "Welcome to TekSynap").
Process Map-Types. -- One may use 1 or the other to create the same effect.
OV-5b (Operational Activity Model). See "USAF 15 IS."
SV-5a (or SV-5); SV-1; SV-2; OV-2; OV-5a; AV-1 (System View, Operational Activity to Systems Function Traceability Matrix): A mapping of system functions (activities) back to operational activities. GOAL: -- Describes the services provided by the system.
Some use SV-1 (Systems Interface Description). The identification of systems, system items, and their interconnections. See MSC/OSD/Projects/DoDAF Projects/SV.
SV-6 (Systems Resource Flow Matrix) -- This is the Goals, Objectives, and Technology/Solutions, etc. in the DOE "Master Data Roadmap."
Some use AV-1 (Overview and Summary Information) -- Describes a Project's Visions, Goals, Objectives, Plans, Activities, Events, Conditions, Measures, Effects (Outcomes), and produced objects. See "USAF 15 IS."
AV-2 (Integrated Dictionary): A glossary-type of the document with acronyms and definitions
Benefit: So all speak the same language.
Additional Common Artifacts:
Operations views (OV), systems views (SV), capability views (CV), data views (DV) using systems modeling language (SysML)
OV-1 (High-Level Process Map),
SV-5a (System View Detailing the Process Map),
AV-1 (All Views: Detailed description of the SV-5a), and
AV-2 (Integrated dictionary).
ICAM Architecture (Identity, Credential, Access Management).
BLUF: ICAM implementation focuses on the "who" and "what" of access—to design the strategic "blueprint" for managing who can access an organization's resources, ensuring the right person has the right access at the right time for the right reason. The steps centered on managing digital identities and controlling access.
Steps to Implement ICAM (using Azure) General View: (5)
Initial Assessment & Requirements Gathering: -- BLUF: Understand the organization's needs for identity and access, including business objectives, compliance requirements (e.g., NIST, GDPR), and existing identity systems.
MS Entra ID (1o10): Formerly Azure AD. Analyze your existing identity data, including users, groups, and applications.
MS Purview (1o2): Use this to discover, classify, secure, categorize sensitive data, helping you determine who needs access to what info doc.
Azure Policy & Azure Security Benchmark: Review these to understand your initial compliance requirements and to establish a baseline for your security posture.
Strategic Roadmap Development: -- BLUF: Create a plan for implementing ICAM capabilities, including prioritizing which systems and user groups to onboard first.
MS Entra ID PIM (Privileged Identity Management) (2o10): Plan for a least-privilege access model by identifying privileged roles and users who need just-in-time (JIT) access.
MS Defender for Cloud: Formally Azure Security Center. Use its secure score and recommendations to prioritize which identity-related security controls to implement first.
Solution Design & Technology Selection: -- BLUF: Choose and design the specific technologies and policies to support identity management, credentialing, and access control. This involves selecting tools for multi-factor authentication (MFA), single sign-on (SSO), and privileged access management (PAM).
MS Entra ID (3o10): The foundational service for all identity and access management.
MS Entra ID B2B & B2C (4o10): Design for external users (partners and customers) with these specific services.
MS Intune: Plan for mobile device management (MDM) and mobile application management (MAM) to enforce access policies on devices.
MS Entra Conditional Access (5o10): Design granular, context-aware access policies that require multi-factor authentication (MFA) or other controls based on user, location, device, and risk.
Azure Key Vault: Plan to securely store and manage cryptographic keys and secrets for applications and services.
Implementation & Configuration: -- BLUF: Setting up the ICAM infrastructure, synchronizing directories, configuring policies, and integrating the solution with various applications and systems.
MS Entra Connect (6o10): Synchronize on-premises Active Directory with Microsoft Entra ID for a hybrid identity solution.
MS Entra ID MFA (7o10): Configure and enforce multi-factor authentication across your organization.
MS Entra Conditional Access (8o10): Roll out the designed policies to various user groups and applications.
MS Entra PIM (Privileged Identity Management) (2o10): Activate JIT access and just-enough-administration (JEA) for privileged roles.
MS Entra ID Governance (9o10): Use entitlement management to automate access requests, workflows, and reviews.
Monitoring, Auditing & Training, Support: -- BLUF: Provide training for administrators and end-users, and establish a support system for the new ICAM platform.
MS Entra ID Identity Protection (10o10): Proactively detect and remediate identity-based risks.
MS Sentinel: Ingest Microsoft Entra ID logs and other signals for comprehensive threat hunting and automated response (SOAR).
MS Purview Audit (Standard and Premium) (2o2): Track and audit all identity and access activities for compliance and forensic analysis.
M.A.C.H. Architecture.
BLUF: The MACH acronym stands for Microservices, API-first, Cloud-native, and Headless. It's a modern architectural approach that promotes flexibility, scalability, and agility in a system. When you combine this philosophy with MS Azure services, you get a powerful, flexible, and robust solution.
Breakdown of MACH Architecture (w Azure): (4)
Microservices: -- BLUF: The many types of vehicles in the tunnel (internet).
Azure Kubernetes Service (AKS): A managed container orchestration service that's a perfect fit for deploying and managing microservices. It handles the complexity of running and scaling containerized applications.
Azure Service Fabric: A distributed systems platform for building and managing microservices at massive scale.
Azure Functions (1o3): A serverless compute service that lets you run individual microservices without managing any infrastructure. It's great for event-driven architectures.
API (Application Programming Interface): -- BLUF: The on/off ramps for the vehicles.
Azure API Management: It acts as the gateway (manage on/off ramps) for all APIs, allowing one to secure, manage, and publish them centrally. It handles authentication, rate limiting, and analytics, so developers can focus on building the APIs themselves.
Azure Functions (2o3): To build APIs, as they provide a simple and scalable way to expose an HTTP endpoint.
Cloud (Azure):
Azure App Service: A fully managed platform for building and deploying web apps and APIs.
Azure SQL Database & Azure Cosmos DB: Managed database services that handle all the complexities of scaling and maintenance.
Azure DevOps: Provides continuous integration and continuous delivery (CI/CD), automating the build and deployment process.
Headless (or Serverless):
Azure Functions (3o3): The serverless compute service. It's the perfect way to build the "headless" back-end logic without managing any servers.
Azure Front Door: A global, scalable entry point that provides a unified gateway for your web apps and APIs, routing traffic to the right "head" or back-end service.
Static Web Apps: For hosting the front-end application, as it's designed for lightweight, serverless front-ends that consume APIs.
Model-Based Systems Engineering (MBSE). -- DoDAF Model-Based.
BLUF: MBSE is a systematic approach to developing complex systems that emphasizes the use of models (ex. DoDAF: OV-1, AV-1/2, SV-5a, etc.) throughout the entire lifecycle of the system.
Value: By following the below principles, MBSE can improve the efficiency, effectiveness, and affordability of complex system development projects.
MBSE Principles: (4)
Tool support: Specialized software tools are used to create, manage, and analyze models (ex. EA Tools: Visio, MagicDraw, Miro (simple draw) -- Full EA Tools -- LeanIX, Lucid Charts, Software AG, Sparx, Avolution by ABACUS, etc.). These tools can help to ensure that models are consistent and complete, and can also automate some tasks.
Model-centricity: Centralizes models as the primary source of information for all aspects of the system, including requirements, design, analysis, and verification. This contrasts with traditional document-centric approaches.
Integration: Models are integrated to provide a holistic (the whole) view of the system, enabling better understanding and communication among stakeholders from different disciplines.
Early verification and validation: Models are used to simulate and analyze system behavior early in the development process, allowing for early identification and correction of potential problems. This reduces the risk of costly rework later in the development cycle.
Stakeholder involvement: Models are used to communicate system concepts and requirements to stakeholders throughout the development process. This ensures that everyone involved is on the same page and that the system meets the needs of its users.
Microservices Architecture.
BLUF: Implementing a microservice architecture involves strategically decomposing an application (system) into smaller, independent services. This process enhances scalability, resilience, and maintainability. -- AV-2: Microservice are the vehicles traveling in the tunnel (the internet); The API is the "On/Off Ramps."
Example of a Microservice Architecture: You start by decomposing a single, monolithic application (system). This is the large, all-in-one codebase that has multiple functions tightly coupled together. For example, a retail "application" might handle user profiles, product catalogs, inventory, and order processing all in one deployable unit. The result of that decomposition is a system of microservices. Each of those functions (user profiles, catalog, inventory, etc.) becomes its own independent service. Together, they form a "distributed system" that, from the end-user's perspective, still delivers the functionality of the original application.
How to Implement a "Microservice Architecture" (using Azure). (6 Goals)
Goal 1: Decompose the Application (System) and Define Service Boundaries.
BLUF: The first step is to break down the application into a collection of small, autonomous services. The key is to define clear boundaries based on business capabilities, not technical layers.
Objective: Identify distinct business domains and establish "bounded contexts" where each microservice will own a specific business function.
Azure Resources: (1) Azure DevOps Boards & Wikis: Use these tools for collaborative domain analysis, event storming sessions, and documenting the identified service boundaries and APIs. This is primarily a design and planning phase.
Authoritative Source: (1) Domain-Driven Design (DDD): Coined by Eric Evans, this approach is the industry standard for identifying service boundaries based on the business domain. (2) Microsoft Cloud Adoption Framework: Provides guidance on defining strategy and planning for cloud adoption, which includes architectural decisions like microservices.
Goal 2: Develop and Containerize Individual Services.
BLUF: Each microservice should be developed, built, and packaged independently. Containerization is the standard approach to ensure consistency across different environments.
Objective 1: Establish a Continuous Integration (CI) pipeline for each service.
Azure Resources: (1) Azure Repos or GitHub: For version control of each microservice's source code. (2) Azure Pipelines: To automate the build and testing process for each service upon code check-in.
Objective 2: Package each service as a lightweight, portable container.
Azure Resources: (1) Azure Container Registry (ACR): A private registry to store and manage your Docker container images securely.
Authoritative Source: (1) The Twelve-Factor App: A methodology for building software-as-a-service apps that outlines best practices, including maintaining a single codebase, managing dependencies, and achieving dev/prod parity, all of which are facilitated by containerization. (2) .NET Microservices: Architecture for Containerized .NET Applications: A comprehensive guide from Microsoft detailing patterns and practices for building containerized microservices.
Goal 3: Implement Service Communication.
BLUF: Services in a microservice architecture must communicate with each other. You need a strategy for both direct, request-response communication and indirect, event-driven communication.
Objective 1: Expose service functionality through a managed API Gateway (On/Off Ramps).
Azure Resources: (1) Azure API Management: Acts as a single entry point ("front door") for all clients. It handles routing, security (authentication, rate limiting), caching, and monitoring of APIs exposed by your microservices.
Objective 2: Implement resilient synchronous (request-response) and asynchronous (event-based) communication patterns.
Azure Resources: -- Synchronous -- Services hosted on (1) Azure Kubernetes Service (AKS), (2a) Azure Functions, or (2b) Azure Container Apps can communicate directly via HTTP/gRPC APIs through the API Gateway. -- Asynchronous -- (1) Azure Service Bus: For reliable, queue-based messaging between services (e.g., placing an order). (2) Azure Event Grid: For reactive, event-driven programming and broadcasting events to multiple interested subscribers (e.g., an order has shipped).
Authoritative Source: (1) API Gateway Pattern: A standard design pattern for managing client-to-service communication. (2) Saga Pattern: A pattern for managing data consistency across services in distributed transactions using a sequence of local transactions.
Goal 4: Manage Decentralized Data.
BLUF: A core principle of microservices is that each service owns and manages its own data to ensure loose coupling.
Objective: Provision a dedicated database or data store for each microservice tailored to its specific needs.
Azure Resources: (1a) Azure SQL Database or (1b) Azure Database for PostgreSQL/MySQL: For services requiring relational data. (2) Azure Cosmos DB: A multi-model NoSQL database for services needing high availability, global distribution, and flexible data schemas. (3) Azure Cache for Redis: An in-memory data store for services that require high-throughput, low-latency data access.
Authoritative Source: (1) Database per Service Pattern: This is the foundational pattern ensuring data encapsulation and service autonomy. It is extensively documented on Chris Richardson's microservices.io and in Microsoft's architecture guidance.
Goal 5: Deploy and Orchestrate Services.
BLUF: You need a robust platform to deploy, manage, and scale your containerized microservices automatically.
Objective: Automate the deployment process (Continuous Delivery & Deployment) and orchestrate container lifecycles.
Azure Resources: (1) Azure Kubernetes Service (AKS): The leading container orchestrator for managing complex, large-scale microservice deployments, handling auto-scaling, service discovery, and health monitoring. (2) Azure Container Apps: A serverless container service built on Kubernetes, ideal for teams that want the benefits of orchestration without managing the underlying infrastructure. (3) Azure Pipelines (Release Pipelines) or GitHub Actions: To create a full CI/CD pipeline that automatically deploys container images from Azure Container Registry to your chosen host (AKS or Container Apps).
Authoritative Source:
Azure Well-Architected Framework: Provides five pillars of architectural best practices, including the "Operational Excellence" pillar which guides the implementation of reliable and automated deployment processes.
In a distributed system, centralized monitoring, logging, and security are critical for troubleshooting and protecting your application.
Objective 1: Centralize logs, metrics, and traces from all services into a unified platform.
Azure Resources:
Azure Monitor: The comprehensive solution in Azure for collecting, analyzing, and acting on telemetry from your cloud and on-premises environments.
Application Insights: A feature of Azure Monitor, it's an Application Performance Management (APM) service that provides deep insights into your application's usage, performance, and health.
Log Analytics Workspace: The primary repository within Azure Monitor for storing and querying log data from all your services.
Objective 2: Secure inter-service communication and manage secrets.
Azure Resources:
Microsoft Entra ID (formerly Azure AD): For securing access to your APIs using modern authentication protocols like OAuth 2.0 and OpenID Connect.
Azure Key Vault: For securely storing and managing application secrets, keys, and certificates, ensuring they are not hard-coded in your application's configuration.
Authoritative Source:
OpenTelemetry: An open-source observability framework (and CNCF project) that standardizes how you collect and export telemetry data. Azure Monitor has native support for it.
Microsoft's Zero Trust Security Model: A security strategy based on the principle of "never trust, always verify," which is essential for securing distributed microservice architectures.
What is SAFe (Scaled Agile Framework).
BLUF (2): -- (1) Focuses on software development (DevOps) scaling agile practices across large organizations to improve software development and delivery. It provides a roadmap (Culture change) for aligning teams, processes, and tools to deliver value faster and more consistently. (2) It integrates Lean, Agile, and DevOps principles to help enterprises deliver value faster, more predictably, and with higher quality.
Benefits (5): -- (1) Deliver value faster and more predictably (2) Improve quality and reduce risk (3) Increase customer satisfaction and engagement (4) Enhance employee morale and productivity (5) Achieve business agility and adaptability in a rapidly changing market.
Value (4): -- (1) Enhanced Flow: Increased emphasis on optimizing value flow through the system, with new practices and metrics for flow measurement and improvement. (2) Accelerated Value Delivery: Addition of eight "flow accelerators" to help organizations identify and address common bottlenecks that impede value delivery. (3) Expanded Guidance for AI, Big Data, and Cloud: Provides more comprehensive guidance on integrating these technologies into SAFe for strategic advantage. (4) Focus on Business Agility: Restructured content and added resources to better support organizations in achieving business agility through SAFe.
Use Cases / In a Nutshell (2): -- (1) SAFe (2): (1) A framework for implementing agile practices in large organizations (2) Used across various industries to improve software development efficiency, team collaboration, and time-to-market. (2) DoDAF (2): (1) A standardized language for describing and analyzing architectures (2) To ensure consistent communication, efficient integration, and interoperability of different systems and capabilities.
Core Tenets / Attributes: (9)
Business Agility: Focuses on aligning business strategy with technology delivery to achieve continuous innovation and value creation.
Customer Centricity: Prioritizes understanding and fulfilling customer needs through rapid feedback loops and experimentation.
Lean-Agile Leadership: Emphasizes servant leadership, empowerment, and decentralized decision-making to foster agility.
Team and Technical Agility: Empowers teams to self-organize, learn, and adapt, while promoting technical excellence and continuous improvement.
DevOps and Release on Demand: Integrates development and operations to enable frequent, reliable, and high-quality releases.
Built-in Quality: Incorporates quality practices throughout the value stream to prevent defects and ensure customer satisfaction.
Adaptive Planning: Embraces uncertainty and promotes flexibility through iterative planning and prioritization.
Enterprise Awareness: Encourages alignment and collaboration across teams and business units to optimize value delivery.
Continuous Learning Culture: Fosters a learning environment where individuals and teams continuously improve their skills and practices.
Components: (4)
SAFe Big Picture: A visual representation of the framework's various levels and elements, interconnected to illustrate value flow. Ex. OV-1.
Essential SAFe: The foundational CCRM for scaling agile practices, focusing on Agile Release Trains (ARTs), teams, and basic roles.
Large Solution SAFe: For enterprises building complex solutions that require coordination across multiple ARTs and Solution Trains.
Portfolio SAFe: Extends SAFe to the portfolio level, aligning strategy, funding, governance, and Lean Portfolio Management practices.
Resources: (4)
https://www.nvisia.com/insights/agile-methodology -- SAFe Agile DevOps Processes (5-Steps).
https://www.bmc.com/blogs/scaled-agile-framework-safe-explained/ -- Initial START!
DoDAF: Serves as a common framework for describing and documenting architectures within the US DoD. It provides a standardized language and set of Viewpoints (7) to understand, communicate, and analyze various aspects of DoD systems and capabilities.
1. Establish Lean-Agile Leadership:
Secure executive sponsorship: Gain buy-in from top leadership to drive the transformation and provide resources.
Identify change agents: Form a core team of individuals passionate about agility and change management to guide the implementation.
Educate leaders: Train leaders on Lean-Agile mindset, principles, and practices to enable effective support and decision-making.
Link: scaledagileframework.com
LeanAgile Leadership in SAFe v6
2. Train Teams and Individuals:
Provide SAFe training: Equip teams and individuals with the knowledge and skills to work effectively within a SAFe environment.
Develop coaching capabilities: Foster a coaching culture to support continuous learning and improvement.
Build communities of practice (CoP): Encourage knowledge sharing and collaboration across teams.
Link: www.childsafe.org.au
3. Launch Agile Release Trains (ARTs):
Identify value streams: Map the flow of value from customer needs to solution delivery.
Form ARTs: Create cross-functional teams aligned to value streams, typically composed of 50-125 people.
Initiate PI Planning (2-Day Events): Conduct regular 2-day Program Increment (PI) planning events to align teams and coordinate work across the ART.
Link: scaledagileframework.com
4. Implement DevOps and Continuous Integration / Continuous Delivery (CI/CD) Pipelines:
Automate processes: Automate build, test, and deployment processes to enable rapid and reliable delivery.
Break down silos: Integrate development, operations, and security teams to collaborate seamlessly.
Establish continuous feedback loops: Monitor system performance and customer feedback to drive continuous improvement.
Link: scaledagileframework.com
5. Scale to Larger Solutions and Portfolio:
Apply Large Solution SAFe: Coordinate multiple ARTs and Solution Trains for complex solutions requiring enterprise-wide alignment.
Adopt Portfolio SAFe: Align strategy, funding, governance, and Lean Portfolio Management practices across the enterprise.
Link: scaledagileframework.com
6. Foster a Continuous Learning Culture:
Embrace experimentation and learning: Encourage teams to experiment, learn from failures, and continuously improve.
Conduct regular retrospectives: Reflect on what's working well and identify areas for improvement.
Celebrate successes: Recognize and reward achievements to reinforce positive change.
Remember:
SAFe implementation is a journey, not a destination. It requires ongoing commitment, adaptation, and learning.
Seek guidance from experienced SAFe coaches and consultants to tailor the framework to your specific context and needs.
Continuously evaluate and adjust your approach based on feedback and results to ensure successful adoption and long-term benefits.
Security Architecture (Broader View).
BLUF: Designs and builds a comprehensive security system (the architecture "blueprint") for an organization, aligning security controls and strategies with business goals to protect against cyber threats.
6 Steps to Implement Security Architecture (using Azure) The "Logical Flow": -- Involves all aspects of the MS Security Portfolio and the Azure Well-Architected Framework.
Define Security Objectives & Risk Assessment: -- BLUF: (1) Clearly outline the goals of the security program, such as protecting specific assets, ensuring business continuity, and/or comply with regulations. (2) Identify all potential threats, vulnerabilities, and risks to the organization's assets (e.g., data, systems, and physical infrastructure). -- This is the macro-level step. You determine what you're trying to protect (your assets) and why (your business objectives). You also conduct a high-level risk assessment to identify potential threats to the entire organization, not just a single system. For example, a risk assessment might identify that a data breach of customer information is a high-impact risk.
MS Defender for Cloud (1o2): Use its secure score and recommendations dashboard to get a holistic view of your security posture across your entire environment.
MS Sentinel (1o2): Use its built-in workbooks and data connectors to identify and prioritize risks across your cloud and on-premises environments.
MS Purview (1o3): Discover and classify sensitive data to understand what you need to protect and its compliance requirements.
Threat Modeling (2): -- BLUF: Creating a detailed model to identify potential attack vectors and prioritizing them based on their impact and likelihood. -- This is the micro-level step. Now that you know a data breach is a high-level risk, do (1) perform a threat model on the specific application that handles customer data. (2) You diagram the system, (3) identify data flows, and (4) use a framework like STRIDE (Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, Elevation of Privilege) to systematically find specific, technical vulnerabilities that could lead to a data breach.
To "Identify" Threats -- Use MS Threat Modeling Tool. It's a free primary tool, stand-alone, desktop application provided by Microsoft. It's a key part of the Microsoft Security Development Lifecycle (SDL). -- DOES 4 Things:
Architecture Diagramming: A simple drag-and-drop interface to create a Data Flow Diagram of the application's architecture, including Azure-specific stencils for services like Azure VMs, App Services, databases, and more. This visual representation is the foundation of the threat model.
Automated Threat Generation: The tool automatically generates a list of potential threats based on the STRIDE methodology (Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, Elevation of Privilege) as applied to your diagram. -- For example, it will identify threats related to data flows crossing a trust boundary (like a public internet connection to your Azure Web App) and suggest mitigations.
Suggested Mitigations: For each identified threat, the tool provides a list of potential mitigations, often with links to official Microsoft documentation on how to implement them in Azure. For instance, a "Tampering" threat on a data flow might suggest using TLS/SSL encryption and provide a link to Azure's documentation on configuring HTTPS.
Reporting: It generates a report that you can use to communicate findings to your team and integrate into your development backlog.
To "Mitigate" and "Validate" Threats -- Use Azure DevOps, MS Defender for Cloud, MS Sentinel, and Azure Policy.
Policy & Governance Development: -- BLUF: Establish the foundational rules and guidelines for security, including incident response plans, data handling policies, and acceptable use policies.
Azure Policy: Enforce organizational standards by creating policies that prevent the creation of non-compliant resources (e.g., VMs without encryption, public IP addresses).
Azure Management Groups: Organize your subscriptions into a hierarchy to apply consistent policies and role-based access control (RBAC) across your entire organization.
MS Purview (2o3): Define and enforce data governance policies, including data lifecycle management and access control.
Layered Defense Strategy Implementation: (5) -- BLUF: Design a security approach that incorporates multiple, overlapping security mechanisms to protect against various threats. This includes controls for network security (firewalls, intrusion detection), endpoint security, application security, and physical security.
Network Security:
Azure Firewall: Provide network-level threat protection with filtering and traffic control.
Network Security Groups (NSGs): Control inbound and outbound traffic to Azure resources within a virtual network.
Azure DDoS Protection: Protect your resources from distributed denial-of-service (DDoS) attacks.
Identity, Credential & Access Management (ICAM):
MS Entra ID (full suite): Use the tools detailed in the ICAM section above.
Data Protection:
Azure Disk Encryption: Encrypt your VMs' operating system and data disks.
Azure Key Vault: Centrally manage and secure your cryptographic keys.
MS Purview (3o3): Automatically classify and label sensitive data and apply protection policies.
Endpoint & Application Security:
MS Defender for Endpoint: Provide advanced threat protection for servers and client devices.
Azure Web Application Firewall (WAF): Protect your web applications from common web exploits and vulnerabilities.
Azure App Service & API Management: Use built-in security features to protect your web apps and APIs.
Securing DevOps (DevSecOps):
Azure DevOps for GitHub Advanced Security: Integrate security scanning into your CI/CD pipelines to find and fix vulnerabilities early.
Implementation of Controls: -- BLUF: (1) Deploy and configure the specific technologies and policies to fulfill the layered defense strategy. Based on the strategy, (2) select and implement the actual security controls. -- For instance, to implement your "Network" layer, you would install and configure a firewall and a Network Security Group (NSG). To implement your "Endpoint" layer, you would deploy an Endpoint Detection and Response (EDR) solution.
MS Defender for Cloud implements and manages a broad range of security controls. Helps deploy, configure, and monitor security across the entire cloud environment. -- Auto-gen Controls: Provides a prioritized list of security recommendations with steps on how to fix them. Many of these recommendations come with a "Fix" button that allows you to directly implement the control.
Examples of Security Recommendations & Auto-Gen Controls:
Network Controls: It might recommend that you enable a firewall, restrict network access to specific ports, or apply a Network Security Group (NSG). You can then use its interface to click through and implement these controls directly.
Identity & Access Controls: Suggest to enable Multi-Factor Authentication (MFA) for privileged accounts. It will also highlight any accounts with excessive permissions and recommend to use Just-In-Time (JIT) access to reduce the attack surface.
Data Controls: It will tell you if your storage accounts are not encrypted and give a simple way to enable encryption at rest. It will also check for exposed sensitive data and recommend ways to lock it down.
Other Azure services: Azure Policy (to encrypt VMs or storage accts), MS Entra ID (IAM, Conditional Access, SSO, Privileged Identity management=PIM), Azure Firewall & Network Security Groups (NSG), and Azure Key Vault (implement data protection controls).
Continuous Monitoring & Auditing: -- BLUF: Regularly assess the effectiveness of the security controls through vulnerability scans, penetration testing, and security audits to ensure ongoing protection.
MS Sentinel (2o2): Act as your cloud-native SIEM and SOAR solution, collecting security data from all sources, analyzing it for threats, and automating responses.
MS Defender for Cloud (2o2): Provide continuous monitoring of your security posture and threat detection for all your Azure and hybrid workloads.
Azure Monitor: Collect and analyze logs and metrics from your Azure resources to monitor performance, health, and security events.