Control Freak: Risk and Control in Azure DevOps

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This document discusses controls for audit and risk management in Azure DevOps. It covers controls for environments, code, data at rest, and data in motion. For environments, it recommends using ARM templates, VSTS branch policies, and deployment pipelines. For code, it suggests using GitFlow, branch policies, and deployment pipelines with approvals. For data at rest, it proposes encryption with TDE, Always Encrypted, and Storage Service Encryption. For data in motion, it advises using TLS and virtual networks. The document also outlines logging, monitoring, and response controls using tools like Application Insights, Azure Policy, and Security Center.

Technology

P re s e n t e d b y
Control Freak: Risk and Control in
Azure DevOps
Barkha Herman
South Florida Code Camp ‘18

What will be covered:
• Audit and Controls for Environments
• Audit and Controls for Code
• Audit and Controls Data at Rest
• Audit and Controls Data In Motion
• Monitoring and Response

Some sample Controls:
1. Logical and physical Segregation of Environments
2. Lifecycle Methodology for Deployments
3. Process set for Approvals and review

Implementation
1. Use ARM Templates to create PaaS and IaaS Applications
2. Use VSTS Branch Policies to control changes to Templates
3. Use a deployment Pipeline to control Environment Changes

Use ARM Templates to deploy
1. Azure Resource Manager
templates automate
Deployment.
2. Creating environment becomes
repeatable.
3. Creating environments can be
scripted.

1.Use VSTS for Templates
1. Use VSTS to maintain ARM
Templates and standardize
changes to environments.
2. Use gitflow Pull Request Process
to validate and audit any
changes to the environments.

1.Use Pipelines for Deployments
1. Use Deployment Pipelines for Deployments.
2. Use SPNs for environments; devops cannot deploy directly to an environment.
3. Approvers setup for each environment – QA approvers differ from PROD
approvers.

Some Sample Controls
1. Code is located in a secure location
2. Access to modify code is restricted
3. Code is reviewed, tested and scanned etc.
4. Code deployment is “gated” and “Audited”

Implementation
1. Git Flow & Branch Policies in VSTS
2. Build once, deploy several for consistency
3. Deployment Pipeline with Approvals for “Gates” and audits

Git Flow + Controls
1. Use GitFlow
2. Pull Requests for Merges, required reviews
and Work Items
3. Developer code lives in PR branches, merged
into Develop
4. Master keeps release versions
5. Code must compile before merge to develop
6. Builds run tests, scan for issues
7. Deployments are gated

Deployment
Pipelines
1. Build artifacts are created once
2. Continuous deployment ensures
compile, unit tests, etc.
3. Deployment to any environment
from CD requires approvals
4. Create different groups for
approvals to different
environments

Some Sample Controls
1. Ensure that Data is Encrypted at rest
2. Access to static data is controlled and audited
3. Ensure that Data is “Highly Available”
4. Ensure Data is Restorable, i.e. Loss Prevention
5. Ensure Data is auditable, i.e., Retention Policies

Implementation - SQL
1. TDE is available for Azure SQL. Uses Key Vault for
Encryption Keys.
2. Always Encrypted Option available.

Implementation – Storage Blob / Files
1. Storage Service
Encryption is also
available.
2. Key Management using
Key Vault.

Implementation - CosmosDB
1. Encrypted by default.
2. Backup to Blob is also
encrypted.

Some Sample Controls
1. All end points use TLS
2. Authentication and Authorization is Implemented
3. All communication is secure in transit – not only from client
to server, but within a data center

Implementation
1. TLS is default in PaaS Services
2. ASEs can be setup web apps and web api for performance,
virtual networks, isolation
3. Azure site-to-site VPN
4. Azure Point-to-site VPN
5. ExpressRoute

Some Sample Controls
1. All end points have logs for auditing.
2. All end points have monitoring available.
3. Alerts are set for disaster as well as security related events.
4. Diagnostics are available for all services.

Logging and Analysis Tools available
1. Application Insights
2. Azure Policy
3. Security Center
4. Azure Monitor
5. Others…

Stay Connected
If you have questions or would like more
information, feel free to contact me via email
barkha.herman@agilethought.com
• www.agilethought.com
• www. linkedin.com/company/AgileThought
• @AgileThought

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"Database isolation: how we deal with hundreds of direct connections to the d...Fwdays

What can go wrong if you allow each service to access the database directly? In a startup, this seems like a quick and easy solution, but as the system scales, problems appear that no one could have guessed. In my talk, I'll share Solidgate's experience in transforming its architecture: from the chaos of direct connections to a service-based data access model. I will talk about the transition stages, bottlenecks, and how isolation affected infrastructure support. I will honestly show what worked and what didn't. In short, we will analyze the controversy of this talk.

Quantum AI Discoveries: Fractal Patterns Consciousness and Cyclical UniversesSaikat Basu

Cracking the Code - Unveiling Synergies Between Open Source Security and AI.pdfPriyanka Aash

You are not excused! How to avoid security blind spots on the way to productionMichele Leroux Bustamante

We live in an ever evolving landscape for cyber threats creating security risk for your production systems. Mitigating these risks requires participation throughout all stages from development through production delivery - and by every role including architects, developers QA and DevOps engineers, product owners and leadership. No one is excused! This session will cover examples of common mistakes or missed opportunities that can lead to vulnerabilities in production - and ways to do better throughout the development lifecycle.

Securing Account Lifecycles in the Age of Deepfakes.pptxFIDO Alliance

Quantum AI: Where Impossible Becomes ProbableSaikat Basu

The Future of Product Management in AI ERA.pdfAlyona Owens

Hi, I’m Aly Owens, I have a special pleasure to stand here as over a decade ago I graduated from CityU as an international student with an MBA program. I enjoyed the diversity of the school, ability to work and study, the network that came with being here, and of course the price tag for students here has always been more affordable than most around. Since then I have worked for major corporations like T-Mobile and Microsoft and many more, and I have founded a startup. I've also been teaching product management to ensure my students save time and money to get to the same level as me faster avoiding popular mistakes. Today as I’ve transitioned to teaching and focusing on the startup, I hear everybody being concerned about Ai stealing their jobs… We’ll talk about it shortly. But before that, I want to take you back to 1997. One of my favorite movies is “Fifth Element”. It wowed me with futuristic predictions when I was a kid and I’m impressed by the number of these predictions that have already come true. Self-driving cars, video calls and smart TV, personalized ads and identity scanning. Sci-fi movies and books gave us many ideas and some are being implemented as we speak. But we often get ahead of ourselves: Flying cars,Colonized planets, Human-like AI: not yet, Time travel, Mind-machine neural interfaces for everyone: Only in experimental stages (e.g. Neuralink). Cyberpunk dystopias: Some vibes (neon signs + inequality + surveillance), but not total dystopia (thankfully). On the bright side, we predict that the working hours should drop as Ai becomes our helper and there shouldn’t be a need to work 8 hours/day. Nobody knows for sure but we can require that from legislation. Instead of waiting to see what the government and billionaires come up with, I say we should design our own future. So, we as humans, when we don’t know something - fear takes over. The same thing happened during the industrial revolution. In the Industrial Era, machines didn’t steal jobs—they transformed them but people were scared about their jobs. The AI era is making similar changes except it feels like robots will take the center stage instead of a human. First off, even when it comes to the hottest space in the military - drones, Ai does a fraction of work. AI algorithms enable real-time decision-making, obstacle avoidance, and mission optimization making drones far more autonomous and capable than traditional remote-controlled aircraft. Key technologies include computer vision for object detection, GPS-enhanced navigation, and neural networks for learning and adaptation. But guess what? There are only 2 companies right now that utilize Ai in drones to make autonomous decisions - Skydio and DJI.

Wenn alles versagt - IBM Tape schützt, was zählt! Und besonders mit dem neust...Josef Weingand

OWASP Barcelona 2025 Threat Model LibraryPetraVukmirovic

"Scaling in space and time with Temporal", Andriy Lupa.pdfFwdays

Design patterns like Event Sourcing and Event Streaming have long become standards for building real-time analytics systems. However, when the system load becomes nonlinear with fast and often unpredictable spikes, it's crucial to respond quickly in order not to lose real-time operating itself. In this talk, I’ll share my experience implementing and using a tool like Temporal.io. We'll explore the evolution of our system for maintaining real-time report generation and discuss how we use Temporal both for short-lived pipelines and long-running background tasks.

cnc-processing-centers-centateq-p-110-en.pdfAmirStern2

מרכז עיבודים תעשייתי בעל 3/4/5 צירים, עד 22 החלפות כלים עם כל אפשרויות העיבוד הדרושות. בעל שטח עבודה גדול ומחשב נוח וקל להפעלה בשפה העברית/רוסית/אנגלית/ספרדית/ערבית ועוד.. מסוגל לבצע פעולות עיבוד שונות המתאימות לענפים שונים: קידוח אנכי, אופקי, ניסור, וכרסום אנכי.

Curietech AI in action - Accelerate MuleSoft developmentshyamraj55

CurieTech AI in Action – Accelerate MuleSoft Development Overview: This presentation demonstrates how CurieTech AI’s purpose-built agents empower MuleSoft developers to create integration workflows faster, more accurately, and with less manual effort linkedin.com +12 curietech.ai +12 meetups.mulesoft.com +12 . Key Highlights: Dedicated AI agents for every stage: Coding, Testing (MUnit), Documentation, Code Review, and Migration curietech.ai +7 curietech.ai +7 medium.com +7 DataWeave automation: Generate mappings from tables or samples—95%+ complete within minutes linkedin.com +7 curietech.ai +7 medium.com +7 Integration flow generation: Auto-create Mule flows based on specifications—speeds up boilerplate development curietech.ai +1 medium.com +1 Efficient code reviews: Gain intelligent feedback on flows, patterns, and error handling youtube.com +8 curietech.ai +8 curietech.ai +8 Test & documentation automation: Auto-generate MUnit test cases, sample data, and detailed docs from code curietech.ai +5 curietech.ai +5 medium.com +5 Why Now? Achieve 10× productivity gains, slashing development time from hours to minutes curietech.ai +3 curietech.ai +3 medium.com +3 Maintain high accuracy with code quality matching or exceeding manual efforts curietech.ai +2 curietech.ai +2 curietech.ai +2 Ideal for developers, architects, and teams wanting to scale MuleSoft projects with AI efficiency Conclusion: CurieTech AI transforms MuleSoft development into an AI-accelerated workflow—letting you focus on innovation, not repetition.

PyCon SG 25 - Firecracker Made Easy with Python.pdfMuhammad Yuga Nugraha

Explore the ease of managing Firecracker microVM with the firecracker-python. In this session, I will introduce the basics of Firecracker microVM and demonstrate how this custom SDK facilitates microVM operations easily. We will delve into the design and development process behind the SDK, providing a behind-the-scenes look at its creation and features. While traditional Firecracker SDKs were primarily available in Go, this module brings a simplicity of Python to the table.

Enhance GitHub Copilot using MCP - Enterprise version.pdfNilesh Gule

EIS-Webinar-Engineering-Retail-Infrastructure-06-16-2025.pdfEarley Information Science

As AI reshapes expectations in retail and B2B commerce, organizations are recognizing a critical reality: meaningful AI outcomes depend on well-structured, adaptable infrastructure. In this session, Seth Earley is joined by Phil Ryan - AI strategist, search technologist, and founder of Glass Leopard Technologies - for a candid conversation on what it truly means to engineer systems for scale, agility, and intelligence. Phil draws on more than two decades of experience leading search and AI initiatives for enterprise organizations. Together, he and Seth explore the challenges businesses face when legacy architectures limit personalization, agility, and real-time decisioning - and what needs to change to support agentic technologies and next-best-action capabilities. Key themes from the webinar include: Composability as a prerequisite for AI - Why modular, loosely coupled systems are essential for adapting to rapid innovation and evolving business needs Search and relevance as foundational to AI - How techniques honed-in enterprise search have laid the groundwork for more responsive and intelligent customer experiences From MDM and CDP to agentic systems - How data platforms are evolving to support richer customer context and dynamic orchestration Engineering for business alignment - Why successful AI programs require architectural decisions grounded in measurable outcomes The conversation is practical and forward-looking, connecting deep technical understanding with real-world business needs. Whether you’re modernizing your commerce stack or exploring how AI can enhance product discovery, personalization, or customer journeys, this session provides a clear-eyed view of the capabilities, constraints, and priorities that matter most.

From Manual to Auto Searching- FME in the Driver's SeatSafe Software

Finding a specific car online can be a time-consuming task, especially when checking multiple dealer websites. A few years ago, I faced this exact problem while searching for a particular vehicle in New Zealand. The local classified platform, Trade Me (similar to eBay), wasn’t yielding any results, so I expanded my search to second-hand dealer sites—only to realise that periodically checking each one was going to be tedious. That’s when I noticed something interesting: many of these websites used the same platform to manage their inventories. Recognising this, I reverse-engineered the platform’s structure and built an FME workspace that automated the search process for me. By integrating API calls and setting up periodic checks, I received real-time email alerts when matching cars were listed. In this presentation, I’ll walk through how I used FME to save hours of manual searching by creating a custom car-finding automation system. While FME can’t buy a car for you—yet—it can certainly help you find the one you’re after!

Security Tips for Enterprise Azure SolutionsMichele Leroux Bustamante

Delivering solutions to Azure may involve a variety of architecture patterns involving your applications, APIs data and associated Azure resources that comprise the solution. This session will use reference architectures to illustrate the security considerations to protect your Azure resources and data, how to achieve Zero Trust, and why it matters. Topics covered will include specific security recommendations for types Azure resources and related network security practices. The goal is to give you a breadth of understanding as to typical security requirements to meet compliance and security controls in an enterprise solution.