AI Risk Management Framework: A General Overview

Posted on September 25, 2025 by Mohsen Akhavan

Introduction

Artificial Intelligence (AI) is increasingly being embedded in products, services, and systems across many sectors. As its use expands, so do the risks — not just technical failures, but ethical, legal, social, and reputational hazards. Recognizing this, the U.S. National Institute of Standards and Technology (NIST) developed the AI Risk Management Framework (AI RMF) as a voluntary guidance to help organizations conceive, design, deploy, and monitor trustworthy and responsible AI systems.

The AI RMF is designed to support alignment and interoperability with other risk management or governance frameworks, rather than replacing them. In 2024, NIST also published a Generative AI Profile to help organisations deal with unique risks posed by generative AI systems.

What is the AI RMF?

At its core, the AI RMF:

Is voluntary and non-sector-specific, meaning organizations of any size or domain can adopt it.
Aims to embed trustworthiness into AI systems — ensuring they are valid, reliable, secure, fair, transparent, accountable, resilient, and privacy-enhancing.
Encourages organizations to treat AI risk management as an ongoing lifecycle activity rather than a one-off compliance exercise.
Provides a core structure (functions, categories, subcategories) that organizations can adapt or align with their existing governance frameworks.
Includes supporting materials such as the AI RMF Playbook, Roadmap, Crosswalks, and Use-Case Profiles to guide adoption.

In simple terms: Part 1 of the framework helps define what “trusted AI” means and what risks need attention, while Part 2 gives actionable steps to manage them.

Core Structure: Four Key Functions

The heart of the AI RMF is its Core, which is built around four interrelated functions. These are not strictly sequential phases, but activities that should be iterated and revisited throughout the AI lifecycle.

Function	Purpose / Focus	Key Activities
Govern	Establish accountability, policies, and culture around AI risk	Define governance structures, roles, responsibilities, policies, and oversight
Map	Understand the context, scope, and potential risks of a given AI system	Identify boundaries, stakeholders, foreseeable harms, and scenarios
Measure	Assess and monitor risks systematically	Use qualitative and quantitative methods, define metrics, validate and test
Manage	Take actions to mitigate, transfer, accept, or monitor risks	Prioritize risk responses, apply controls, monitor outcomes, and update processes

Governance is foundational — without governance, sustainable risk practices are difficult.

Map ensures context is clear: risks vary by domain, regulation, and stakeholders.

Measure acknowledges that some risks require qualitative judgement rather than numbers.

Manage focuses on prioritization and iteration as AI systems and risks evolve.

Trustworthiness and Risk: Key Concepts

Before applying the four functions, organizations are encouraged to reflect on two key areas: trustworthiness attributes and risk categories.

Trustworthiness attributes include:

Validity and reliability
Safety, security, and resilience
Transparency and explainability
Privacy protection
Fairness and bias mitigation
Accountability and governance

Common AI risk categories include:

Bias and unfair outcomes
Model drift and degradation
Adversarial attacks or manipulation
Privacy violations
Misuse or unintended use
Legal, compliance, or intellectual property risks
Systemic or societal impacts

The framework emphasizes identifying and prioritizing the most relevant risks in context.

Profiles, Playbooks & Supporting Materials

To move from theory to practice, the AI RMF is supported by additional resources:

Use-Case Profiles: Tailored guidance for specific applications (e.g. hiring, healthcare, generative AI).
Playbook: Step-by-step guidance, worksheets, and examples.
Roadmap: Long-term planning advice to evolve AI risk practices.
Crosswalks: Mappings to other frameworks like cybersecurity or privacy standards.
Perspectives: Domain-specific or stakeholder-focused best practices.

These tools make the framework more practical and adaptable.

Benefits & Challenges

Benefits

Promotes systematic and proactive risk management.
Provides a shared language for AI governance across teams.
Supports building and maintaining trustworthy AI systems.
Scales to organizations of any size or maturity.
Helps demonstrate accountability to regulators, customers, and partners.

Challenges

Requires customization — it’s not a “plug and play” checklist.
Measuring social or systemic risks is complex.
Needs strong governance culture and leadership buy-in.
Must evolve continuously as AI and risks change.
Integration with existing governance processes can be difficult.

Conclusion

The AI Risk Management Framework is not a rigid standard, but a flexible guide to help organizations build trustworthy AI while managing uncertainty and risk responsibly. By focusing on governance, context, measurement, and management, it provides a structured yet adaptable approach to handling AI risks.

For organizations investing in AI, adopting the AI RMF is not just about compliance or risk reduction — it is a step toward building confidence, accountability, and long-term trust in AI systems.

Securing Azure Environments: Implementing Zero Trust Architecture

Posted on May 9, 2025 by Mohsen Akhavan

As organizations shift toward cloud-first strategies, traditional security models based on a trusted perimeter are no longer sufficient. With remote workforces, distributed applications, and increasingly sophisticated cyber threats, the Zero Trust Architecture (ZTA) has emerged as a foundational approach to modern security—especially within the Azure ecosystem.

In this blog post, we’ll explore what Zero Trust is, why it matters, and how to implement it using native Azure services.

What is Zero Trust?

Zero Trust is a security model that assumes breach and verifies each request as though it originates from an open network. Instead of implicitly trusting users or devices inside the corporate perimeter, Zero Trust continually validates trust at every stage of a digital interaction.

Core principles of Zero Trust:

Verify explicitly: Authenticate and authorize based on all available data points (identity, location, device health, etc.).
Use least privileged access: Limit user access with just-in-time and just-enough-access (JIT/JEA) principles.
Assume breach: Operate with the assumption that attackers are already inside the network.

Azure Services That Enable Zero Trust

Azure provides a comprehensive set of tools and services to implement Zero Trust:

Zero Trust Pillar	Azure Service
Identity & Access Management	Azure AD, Conditional Access, Identity Protection
Device Compliance	Microsoft Intune, Defender for Endpoint
Application Security	Azure Front Door, Azure AD App Proxy
Data Protection	Azure Information Protection, Microsoft Purview
Network Microsegmentation	Azure Firewall, Network Security Groups, Azure Bastion
Threat Detection & Response	Microsoft Defender for Cloud, Sentinel

Implementing Zero Trust in Azure – Step by Step

1. Secure Identity with Azure Active Directory

Enable Multi-Factor Authentication (MFA) for all users.
Use Conditional Access to enforce location, device, and risk-based policies.
Monitor sign-ins and risky behavior via Azure AD Identity Protection.

2. Control Device Access

Integrate Microsoft Intune to manage device compliance.
Restrict access to only managed or compliant devices via Conditional Access.

3. Limit Access with Role-Based Access Control (RBAC)

Assign users the minimum permissions necessary using RBAC in Azure.
Implement PIM (Privileged Identity Management) for just-in-time access.

4. Segment Your Network

Use Azure Virtual Networks and Network Security Groups (NSGs) to restrict lateral movement.
Deploy Azure Firewall and Azure Bastion to isolate management access.

5. Protect Applications and Data

Deploy Azure Web Application Firewall (WAF) with Azure Front Door.
Label, classify, and protect sensitive data with Microsoft Purview and AIP.

6. Enable Continuous Monitoring

Enable Microsoft Defender for Cloud to assess configuration and alert on threats.
Use Azure Sentinel to collect, correlate, and respond to security events.

Best Practices and Recommendations

Start with Identity: Ensure you have strong identity protection before anything else.
Use Security Defaults: Azure AD Security Defaults are a great baseline for small orgs.
Continuously Monitor: Logging, auditing, and threat detection are not optional.
Educate Users: Zero Trust is also about awareness—users must understand new access patterns.
Integrate with DevSecOps: Extend Zero Trust into CI/CD pipelines and development practices.

Conclusion

Zero Trust is not a product—it’s a strategic shift. By leveraging Azure’s built-in security tools, organizations can transition from perimeter-based defenses to a model that protects resources regardless of where users and data reside.

Start small, build incrementally, and make Zero Trust the backbone of your Azure security strategy.

In recent years, artificial intelligence (AI) has significantly enhanced cybersecurity. When combined with the Zero Trust model, AI can form a robust foundation for enterprise-level security. For a deeper dive into how AI is shaping the future of cybersecurity, check out this insightful article by Pouya Koushandehfar: PwC: Why AI is Transforming Enterprise Cybersecurity

A Deep Dive into AI-Powered Threat Detection in Azure

Posted on March 11, 2024 by Mohsen Akhavan

In the ever-evolving landscape of cybersecurity, the ability to swiftly and accurately detect threats is paramount. As cyber adversaries become increasingly sophisticated, traditional security measures alone are no longer sufficient to safeguard digital assets. In this blog post, we’ll explore how Azure leverages the transformative capabilities of artificial intelligence (AI) to elevate threat detection to new heights, ensuring proactive defense against emerging cyber threats.

Unraveling the Complexity of Threat Detection

Traditional methods of threat detection often rely on predefined rules and signatures to identify known patterns of malicious activity. While effective against known threats, these approaches struggle to keep pace with the rapidly evolving tactics employed by cybercriminals. This is where AI-driven threat detection in Azure shines, leveraging advanced machine learning algorithms to analyze vast volumes of data and uncover subtle indicators of potential threats.

Harnessing the Power of Machine Learning

At the core of Azure’s threat detection capabilities lies the power of machine learning. By ingesting and analyzing diverse datasets encompassing network traffic, user behavior, system logs, and more, Azure’s AI algorithms can discern patterns and anomalies indicative of malicious activity. Unlike rule-based systems, which are limited by predefined parameters, machine learning models in Azure adapt and evolve over time, continuously refining their ability to differentiate between benign and malicious behavior.

Real-Time Anomaly Detection

One of the key strengths of AI-powered threat detection in Azure is its ability to identify anomalies in real-time. By establishing baseline profiles of normal system behavior, Azure’s AI algorithms can detect deviations from these norms that may signal a potential security threat. Whether it’s unusual network traffic patterns, unauthorized access attempts, or anomalous user behavior, Azure’s AI-driven approach ensures timely detection and response to emerging threats.

Behavioral Analysis for Enhanced Insights

In addition to detecting anomalies, Azure’s AI-powered threat detection goes a step further by performing behavioral analysis to gain deeper insights into potential threats. By examining patterns of behavior across multiple data points and over extended periods, Azure can discern subtle indicators of malicious intent that may elude traditional detection methods. Whether it’s identifying the subtle signs of a targeted phishing campaign or detecting the early stages of a ransomware attack, Azure’s behavioral analysis capabilities provide invaluable intelligence for proactive threat mitigation.

Empowering Security Teams with Actionable Insights

Effective threat detection is not just about identifying anomalies; it’s also about providing security teams with actionable insights to respond swiftly and decisively. Azure’s AI-driven threat detection capabilities empower security analysts with rich contextual information, including the severity of the threat, the potential impact on the organization, and recommended courses of action. By streamlining the incident response process and providing granular insights into detected threats, Azure enables security teams to mitigate risks more effectively and minimize the impact of security incidents.

Conclusion

In an era defined by escalating cyber threats and increasingly sophisticated adversaries, the need for advanced threat detection capabilities has never been greater. Azure’s AI-powered approach to threat detection represents a paradigm shift in cybersecurity, harnessing the power of machine learning and behavioral analysis to uncover threats that traditional methods may overlook. By empowering organizations with real-time insights and actionable intelligence, Azure enables proactive defense against emerging cyber threats, safeguarding digital assets and upholding the trust of customers and stakeholders alike.

Exploring AI’s Role in Azure Cybersecurity

Posted on March 2, 2024 by Mohsen Akhavan

In the dynamic landscape of digital technology, cybersecurity stands as a paramount concern. As our reliance on cloud services grows, ensuring the safety and integrity of data stored and processed on these platforms becomes increasingly critical. Azure, Microsoft’s cloud computing service, has emerged as a leader in providing robust cybersecurity measures, leveraging the power of artificial intelligence (AI) to fortify its defenses against evolving threats.

The Azure Advantage

Azure’s comprehensive suite of security features encompasses threat detection, identity and access management, data encryption, and network security, among others. However, what truly sets Azure apart is its integration of AI-driven tools and capabilities that continuously monitor, analyze, and respond to potential security risks in real-time.

AI-Powered Threat Detection

At the heart of Azure’s cybersecurity framework lies advanced AI algorithms capable of detecting anomalies and identifying potential threats with unmatched precision. By analyzing vast amounts of data generated across Azure’s expansive network, these AI systems can discern patterns indicative of suspicious activities, such as unauthorized access attempts, malware intrusions, or unusual data transfer behaviors.

Adaptive Defense Mechanisms

One of the key strengths of AI in cybersecurity is its ability to adapt and learn from emerging threats. Azure employs machine learning models that evolve over time, leveraging insights gained from past incidents to proactively anticipate and counteract future attacks. This adaptive approach not only enhances Azure’s ability to detect known threats but also enables it to identify previously unseen attack vectors, staying one step ahead of cyber adversaries.

Contextual Threat Intelligence

Effective cybersecurity requires more than just identifying threats; it demands understanding the context in which these threats operate. Azure’s AI algorithms leverage contextual intelligence by aggregating data from diverse sources, including user behavior analytics, threat intelligence feeds, and historical attack patterns. By contextualizing security alerts within the broader framework of an organization’s digital ecosystem, Azure enables more informed decision-making and targeted response strategies.

Automated Incident Response

In addition to detection and analysis, Azure’s AI capabilities extend to automating incident response workflows. By leveraging predefined playbooks and adaptive response mechanisms, Azure can autonomously mitigate security incidents in real-time, reducing response times and minimizing the impact of potential breaches. This automation not only alleviates the burden on cybersecurity teams but also ensures swift and consistent responses across diverse threat scenarios.

Continuous Improvement Through Feedback Loops

Central to Azure’s AI-driven cybersecurity approach is the concept of continuous improvement through feedback loops. As Azure’s AI systems detect and respond to security incidents, they generate valuable feedback data that is used to refine and enhance their algorithms further. This iterative process of learning and adaptation ensures that Azure’s cybersecurity defenses remain robust and effective in the face of evolving threats.

Azure Automation Account

Posted on May 7, 2023 by Mohsen Akhavan

What is Azure Automation Account?

An Azure Automation account is a cloud-based management solution offered by Microsoft Azure for automating tasks and processes across Azure resources and other third-party systems. It provides a centralized location to create, deploy, manage, and monitor workflows, runbooks, and scripts to perform repetitive tasks, orchestrate complex processes, and reduce manual errors.

Overall, the Azure Automation account is a powerful tool for managing and automating IT operations and business processes, improving efficiency, reducing costs, and minimizing errors.

Some key features:

Workflow Automation: Azure Automation allows you to automate a wide range of tasks, from simple script execution to complex process automation across hybrid environments, using various workflow tools such as PowerShell, Python, and Azure Logic Apps.
Configuration Management: Azure Automation enables you to manage and apply consistent configurations across Azure and on-premises resources by using Desired State Configuration (DSC).
Monitoring and Reporting: Azure Automation provides comprehensive monitoring and reporting capabilities, including alerting, logging, and reporting, to help you detect and resolve issues quickly and efficiently.
Integration with Other Azure Services: Azure Automation can be integrated with other Azure services, such as Azure Virtual Machines, Azure Functions, and Azure Storage, to automate workflows and improve efficiency.

In the below sample, I wanna describe how to config a scheduled task for Stop and Start a VM with the PowerShell runbook.

Create an Azure Automation account: Go to the Azure portal and create an Automation account. You can do this by searching for “Automation” in the search bar and clicking on “Automation accounts” under the “Services” category. Then click “Add” and follow the prompts to create an Automation account.
Import the AzureRM modules: In your Azure Automation account, click on “Modules” in the left-hand menu and then click “Browse gallery”. Search for “AzureRM.Compute” and “AzureRM.Profile” modules, and then click “Import” to import them into your Automation account.
Create a new runbook: Click on “Runbooks” in the left-hand menu and then click “Create a runbook”. Give your runbook a name and select “PowerShell” as the runbook type.
Add the PowerShell script: In the editor, copy and paste the following PowerShell script

`Connect to Azure`

$connection = Get-AutomationConnection -Name 'AzureRunAsConnection' Connect-AzAccount -ServicePrincipal -Tenant $connection.TenantID -ApplicationId $connection.ApplicationID -CertificateThumbprint $connection.CertificateThumbprint

`Stop the VM`

Stop-AzVM -ResourceGroupName "myResourceGroup" -Name "myVMName" -Force

`Start the VM`

Start-AzVM -ResourceGroupName "myResourceGroup" -Name "myVMName"

Replace “myResourceGroup” and “myVMName” with the name of your resource group and VM.

Create a schedule: Click on “Schedules” in the left-hand menu and then click “Add a schedule”. Give your schedule a name, set the recurrence and start time, and select the runbook you just created.

That’s it! Your VM will now automatically stop and start according to the schedule you set up. You can also use similar PowerShell scripts to perform other automation tasks, such as resizing VMs or backing up data.

Microsoft published Azure Communication Service. What is this and how to setup it with Azure Email Communication Service?

Posted on April 14, 2023 by Mohsen Akhavan

What is the Azure Communication Service?

Azure Communication Services is a fully-managed platform as a service (PaaS) offering from Microsoft Azure that enables developers to build communication features into their applications. It provides a suite of APIs and SDKs that developers can use to integrate voice and video calling, email, chat, SMS, and other communication capabilities into their applications.

With Azure Communication Services, developers can create a wide range of communication experiences, from one-to-one conversations to large-scale group chats and conferences. They can also leverage the platform’s built-in security and compliance features, as well as its global reach and scalability, to deliver reliable and secure communication experiences to users around the world.

What is the Azure Communication Service Email?

If you are looking to integrate email capabilities into your applications, you’ll be happy to know that Microsoft Azure has introduced a new primitive called Azure Communication Services Email. This new service facilitates high-volume transactional, bulk, and marketing emails on the Azure Communication Services platform, making it easy to enable Application-to-Person (A2P) use cases.

With Azure Communication Services Email, you can easily integrate email capabilities into your applications using production-ready email SDK options. This will enable you to send and receive emails, manage email accounts and settings, and integrate with other Azure services, all through a simple and intuitive API.