Category: Security

  • From Technical Architecture to Business Value: Mapping AI Security Capabilities with Power BI

    A security architecture diagram can be completely accurate and still fail to answer the question that matters to a board member. Consider a proposed enterprise AI security initiative: The engineering team presents an impressive topology of Cloud proxies, API gateways, policy engines and data stores. But the board asks a simpler question: Which critical capabilities will this investment strengthen, and how will we know?

    This is where a capability oriented architecture view changes the conversation. In this hands on demonstration, we will map business concerns to security enabling capabilities, Microsoft controls, implementation evidence and technical assets. Then we will build three different views over the same model using Power BI. The approach is inspired by ArchiMate 3.2, but the dashboard is not presented as a certified ArchiMate modeling tool.

    “Show me what the AI security investment enables, how far delivery has progressed, and where I can verify the evidence.”

    Demo disclosure: Every organization name, maturity score, test percentage, owner and status in this article is fictional. No production Microsoft tenant was used to produce these results. The aim is to build and explain a repeatable architecture reporting pattern, not to claim measured business improvements.

    The following ArchiMate inspired architecture view shows how the board question links to security capabilities, delivery initiatives, Microsoft controls and implementation evidence. It is a conceptual traceability view rather than a formal ArchiMate model.

    ArchiMate-inspired layered architecture connecting a board question and business outcomes to AI security capabilities, initiatives, Microsoft controls, evidence and technical assets
    Figure 1. Executive to technical AI security traceability (synthetic architecture example).

    The architecture mistake: starting with technology instead of the stakeholder concern

    An API gateway, a Copilot DLP policy or a Conditional Access rule is not the answer to a board level capability question. These are important implementation details, but they belong in the next layer of the conversation. An executive needs to see the investment’s intended outcome, the capabilities it supports, delivery confidence and proof that controls are working.

    We should not throw away the engineering diagram. We should add an executive capability view and preserve the connections to implementation. The technical view remains available for architecture review and audit, without forcing every executive to interpret network components.

    Our design: one initiative, three complementary views

    1. Board capability view: Which security enabling capabilities support safer AI adoption? What is their baseline, target, implementation coverage and evidence review status?
    2. Capability traceability view: Which initiatives and Microsoft controls support the selected capability, who owns them, and what evidence supports the mapping?
    3. Engineering detail view: Which policies, gateways, applications, interfaces and technical diagrams implement the control?

    The traceability chain is easy to explain: Business objective → Capability → Initiative → Security control → Evidence → Technical asset. It is a navigational reporting chain, not a claim that these are all direct ArchiMate relationships.

    Sample scenario: Enterprise AI Security Foundation

    Imagine an organization planning to expand Microsoft 365 Copilot adoption and internal AI agents. The proposed security program includes sensitive data protection, governed identity access, risky OAuth app visibility, incident monitoring, audit assurance and secure custom agent interfaces.

    Capability we want to supportMicrosoft control exampleWhat we should validate
    Protect sensitive business informationMicrosoft Purview DLP for CopilotPercentage of approved sensitive prompt test cases that behave as expected
    Govern employee AI accessMicrosoft Entra Conditional AccessAccess policy test results across intended user and device scenarios
    Manage third party AI applicationsDefender for Cloud Apps app governancePercentage of identified risky OAuth apps reviewed
    Detect and respond to AI related threatsMicrosoft Defender XDRPercentage of agreed simulated incident scenarios detected and triaged
    Produce audit ready assuranceMicrosoft Purview AuditPercentage of required evidence retrieval tests passed
    Secure custom AI agent interfacesAzure API ManagementAuthentication and rate limit policy test results

    These are examples of security enabling capabilities. To tie them to the wider business, record the associated outcome, such as trusted employee AI adoption, protected customer information or repeatable regulatory assurance. Do not equate the number of configured controls with business value.

    A product specific caveat matters here: Microsoft Purview DLP has different actions for restricting external web searches, processing sensitive prompts and excluding labeled content. Sensitive prompt blocking is described by Microsoft as a feature subject to preview rollout, so validate availability and supported conditions in your tenant before designing tests. See Microsoft’s Copilot DLP documentation.

    Step 1: prepare the data model

    For an initial proof of concept, we can build the report entirely in Power BI Desktop using synthetic CSVs. No Microsoft 365 admin privileges or production telemetry are needed.

    DatasetPurpose
    01_Capabilities.csvCapability ID, name, outcome, owner, criticality and baseline/target maturity
    02_Initiatives.csvInitiative, sponsor and delivery stage
    03_Controls.csvNamed controls, Microsoft product, owner and type
    04_Traceability.csvAssociations between an initiative, capability and control, plus status and rationale
    05_Evidence.csvTest metric, observed and target values, review status and source
    06_TechnicalAssets.csvImplementing component, technology layer, owner and diagram reference
    Synthetic sample data tables for AI security capabilities and associated initiatives
    Figure 2. Capabilities and Initiatives sample datasets used in the demonstration.
    Sample Microsoft security controls and their mappings to AI security capabilities and initiatives
    Figure 3. Controls and Traceability sample datasets showing the capability control mapping.
    Synthetic examples of control evidence records and implementing technical assets
    Figure 4. Evidence and TechnicalAssets sample datasets used to validate control mappings.

    Use stable text IDs such as CAP-01, INIT-01 and CTRL-01. The TraceID key is the bridge from a control mapping to its supporting evidence and engineering artifacts. Keep what we plan, what is implemented and what has been tested as separate attributes.

    Download the Power BI starter kit: AI Security Capability Map – Power BI Starter Workbook (.xlsx). This workbook includes six synthetic datasets, relationship guidance, a README and DAX measures. Alternatively, use the original CSV files with Get Data → Text/CSV as described in the steps below.

    Step 2: load the datasets into Power BI Desktop

    1. Open Power BI Desktop and select Home → Get data → Text/CSV.
    2. Load the six files from the starter kit. Use Transform data to confirm key columns are text and measurement columns are numeric.
    3. In Model view, create the five one to many relationships shown below. Use single-direction filtering in the first version.
    4. Disable or review automatically inferred relationships so that additional ambiguous filter paths are not introduced.
    01_Capabilities[CapabilityID] 1 → * 04_Traceability[CapabilityID]
    02_Initiatives[InitiativeID] 1 → * 04_Traceability[InitiativeID]
    03_Controls[ControlID] 1 → * 04_Traceability[ControlID]
    04_Traceability[TraceID] 1 → * 05_Evidence[TraceID]
    04_Traceability[TraceID] 1 → * 06_TechnicalAssets[TraceID]

    The relationships make it possible to filter a capability down to its mapped controls and then into relevant evidence and assets. Avoid casually enabling bidirectional filtering across all the tables, which can produce misleading totals and ambiguous navigation.

    Power BI semantic model showing capabilities, initiatives and Microsoft controls mapped to traceability and evidence, with many-to-one single-direction relationship
    Figure 5. Power BI relationship model connecting AI security capabilities, initiatives, controls and evidence (synthetic demonstration). The Traceability Evidence relationship is active with single direction filtering.

    Step 3: create simple DAX measures

    The first report should distinguish implemented mappings from evidence reviewed mappings. Start with these measures, using the imported table names from the starter kit:

    Mappings = DISTINCTCOUNT('04_Traceability'[TraceID])
    Implemented Mappings =
    CALCULATE(
    [Mappings],
    '04_Traceability'[ImplementationStatus] = "Implemented"
    )
    Implementation Coverage % =
    DIVIDE([Implemented Mappings], [Mappings])
    Evidence Records =
    DISTINCTCOUNT('05_Evidence'[EvidenceID])
    Reviewed Evidence Records =
    CALCULATE(
    [Evidence Records],
    '05_Evidence'[ValidationState] = "Reviewed"
    )
    Evidence Review % =
    DIVIDE([Reviewed Evidence Records], [Evidence Records])

    Format the two percentage measures as percentages. Implementation Coverage % measures the share of mapped controls flagged implemented in our sample, not the degree to which the business capability has improved. Similarly, reviewed evidence records show review completion, not independent assurance that an enterprise risk has been eliminated.

    Step 4: build the board capability view

    Create a report page called Board Capability Overview. Add a Matrix visual with CapabilityName as rows. Include BaselineMaturity, TargetMaturity, Implemented Mappings, Implementation Coverage % and Reviewed Evidence Records. Add a slicer for InitiativeName, with criticality and owner available as supplementary context.

    Use conditional formatting for implementation coverage, but keep maturity and evidence in separate columns. A red capability might be a delivery gap, an evidence gap, or both. The dashboard should tell the board which problem exists instead of combining everything into a single unexplained red/amber/green status.

    Executive capability matrix showing baseline maturity, target maturity, implementation coverage and evidence review status.

    Step 5: drill into capability traceability

    Create a second page named Capability Traceability. Add CapabilityID to the page’s drillthrough fields. Include the selected capability, mapped initiative, control name, implementation status, rationale, owner and evidence validation state.

    From the board report, right click a capability and use Drill through → Capability Traceability. Add a Back button for a natural navigation path. Microsoft documents this pattern in its Power BI drillthrough guidance.

    Our fictional example should let the reviewer start with Protect sensitive business information, inspect the Enterprise AI Security Foundation initiative, see the mapped Purview Copilot DLP control, and open its synthetic test evidence record. This is traceability from a high level capability to implementation, not just another static slide.

    Capability drillthrough page showing the mapped control, test result, review state and control owner.

    Step 6: keep the detailed technical architecture

    Create a third page called Engineering Detail. Display the policy set, API gateway, application interface, audit source or other technical asset referenced by the selected traceability record. Include architecture diagram links as reviewed metadata. This is where the network topology belongs.

    For custom agents, Azure API Management can enforce token validation and request throttling. Refer to validate-jwt, validate-azure-ad-token for Entra-issued tokens, and rate-limit-by-key. The dashboard should link to tested implementation details without displaying secrets, tokens or unnecessary user data.

    Engineering detail page, with a technical component inventory

    Now do the Drill through by going to the capability traceability tabe

    And after that it must show the expected result

    Step 7: move from CSV to Microsoft Lists and security evidence

    Once the static report works, create six Microsoft Lists in SharePoint using the same fields. In Power BI Desktop use Get data → SharePoint Online List, enter the SharePoint site URL and select the lists. Microsoft documents the supported connector in Power Query’s SharePoint Online List reference.

    For a later version, enrich the Evidence list using carefully permissioned exports or APIs. Example sources include app governance reviews, Entra policy test results, Purview audit data and Microsoft Graph security incidents. Microsoft Graph exposes an incident listing API, but API permissions, retention, schema and licensing must be verified. Do not assume every Purview DLP event can be fetched through the same API or that evidence is available in every tenant.

    Store only approved aggregates and reference links in the board facing dataset. Don’t load raw prompts, secrets, individual user identities or unrestricted incident descriptions into a broadly shared Power BI semantic model. Apply SharePoint permissions and Power BI row level security or audience separation where required.

    Where ArchiMate fits

    The modeling logic can be expressed through ArchiMate’s distinct concerns: Motivation for stakeholder concerns and goals, Strategy for capabilities, Implementation and Migration for delivery work packages, and Application/Technology for the supporting services and components.

    The Power BI data relationships are a reporting model inspired by those ideas, not literal ArchiMate connector definitions. If a formal ArchiMate artifact is needed, create and validate the view in a modeling tool such as the free Archi, using the Open Group ArchiMate specification and guidance to choose appropriate elements and relationships.

    What this dashboard proves, and what it does not

    • It can show: mapped capabilities, accountable owners, planned versus implemented controls, test results, last review dates and links to technical implementations.
    • It cannot automatically prove: a real reduction in AI risk, regulatory compliance, higher capability maturity or a causal return on investment.
    • To demonstrate improvement: define the outcome KPI, establish a baseline, agree on the test method, capture time-bound results and record independent review.

    Closing thoughts

    The engineering architecture and the executive capability view are both necessary, but they serve different audiences. A board member should be able to start with what the investment strengthens. An architect should be able to drill into how it is implemented. And an auditor should be able to follow what evidence exists.

    By connecting these views in Power BI and Microsoft Lists, we make enterprise AI security architecture easier to explain without losing the underlying technical detail. The next enhancement would be a small, controlled pipeline that refreshes selected evidence from Microsoft security services, with explicit review and data access boundaries.

    Official references

  • From Guardrails to Controls: Getting Started with Azure AI Content Safety

    In my previous article, “From Prompt to Privilege Practical Guardrails for Securing Agentic AI and MCP,” we looked at Agentic AI security from a broader architectural perspective.
    The key takeaway was simple: securing AI systems cannot rely on a single control.
    As AI applications evolve from answering questions to retrieving enterprise data, calling tools, interacting with APIs and eventually performing actions on behalf of users, the security model also needs to evolve. Identity, authorization, data protection, prompt protection, content filtering, monitoring and agent behaviour controls all become part of the same security story.

    But architecture diagrams are only the beginning.

    The next question is:

    What do these guardrails actually look like when we implement them?
    That is what I want to explore in this series.
    Rather than trying to cover every AI security control in one large article, I will take them one at a time, deploy them, test them and document what happens.

    For the first control, I am starting with Azure AI Content Safety.
    Microsoft describes Azure AI Content Safety as a service for detecting harmful user-generated and AI-generated content.
    Its capabilities include text and image analysis, Prompt Shields, groundedness detection, protected material detection, custom categories and Task Adherence.

    Why start with Azure AI Content Safety?
    When we think about securing a traditional application, we normally place controls around identities, networks, applications and data.

    Generative AI introduces an additional trust boundary:

    Natural language input itself.

    A user prompt might simply ask a valid business question.
    But the same interface can also be used to attempt to manipulate the model, override system instructions, inject hidden instructions or push the model towards unsafe responses.
    So before we move deeper into areas such as agent permissions, tool invocation and MCP controls, it makes sense to first look at what is entering the AI system.
    Microsoft’s Azure AI security guidance similarly recommends a layered approach, including analyzing input for malicious content and prompt injection attempts alongside output filtering and other application controls.

    For this first hands on exercise, I therefore kept the scope deliberately small:

    Text moderation : Can Azure identify potentially harmful text?
    Prompt Shields : Can Azure recognize a prompt attempting to manipulate the model?

    (more…)
  • Designing Safe and Actionable Agents in the New Azure Foundry Portal

    In this blog we will go through the Azure AI foundry Portal and its capabilities .The new Azure AI Foundry portal brings model experimentation, agent building, data grounding, and safety controls into a single, coherent workspace. It’s designed so builders can move from idea to prototype to hardened agent without context switching.

    The first thing when we login is we need to switch on the toggle new foundry and it totally brings altogether a new interface and lands us to the dashboard.

    This dashboard is the Foundry project home for a developer or team building AI agents. It surfaces the project endpoint and API key for integration, shows the project region, and highlights recent model and tooling updates so teams can stay current. The page also lists recent projects and provides quick links to documentation and community resources, making it a practical launchpad for both prototyping and production work.

    In the coding quick start we have the option coding quick start. We can open in vs code for the web.

    (more…)
  • The Future of Account Recovery: Microsoft Entra Verified ID Powered by AU10TIX for Account Recovery (Preview)

    Introduction

    Microsoft Entra Verified ID transforms identity verification by enabling secure, privacy‑preserving, verifiable credentials. Whether you’re implementing Account Recovery (Preview) or integrating a trusted identity verification partner like AU10TIX, the onboarding process requires a few key steps: enabling the provider, provisioning resources, activating the integration, and validating the recovery flow.

    This blog walks through the full, end‑to‑end Verified ID setup using AU10TIX as the identity verification provider. You’ll find prerequisites, detailed configuration steps, screenshots, and a summary to help you complete the setup smoothly

    Prerequisites

    Before you begin, ensure the following:

    1. Verified ID Setup :

    You need to have the Verified ID setup properly configured in your tenant first and the foremost thing to do before proceeding to any steps. If this one is not setup then the account recovery (Preview) is not going to work . Especially your domain needs to be a verified domain here.

    Also in the Authentication method you must have the Temproary Access Pass Enabled

    (more…)
  • Exploring Microsoft Entra Agent ID (Preview): Identity, Governance & Zero‑Trust for AI Agents

    Note: Features are in Preview and may change.

    As organizations lean into AI assistants and autonomous workflows, one challenge keeps coming up in every SOC and IAM conversation: agent sprawl. Agents show up in multiple teams and builder platforms, and before you know it, you’ve got non‑human actors touching sensitive data without a clear inventory, lifecycle, or policy boundary.

    Microsoft Entra Agent ID and the Agent Registry (Preview) are designed to solve exactly that bringing identities, governance, and Zero Trust controls to AI agents, so you can securely discover, organize, and manage them easily in your directory.


    What Agent Registry Adds (and Why You’ll Care)

    Agent Registry is an Microsoft Entra integrated metadata repository that gives you a unified view of agents built on Microsoft platforms (e.g., Copilot Studio, Azure AI Foundry) and those from other ecosystems. It separates operational records (Agent Instances) from discoverability metadata (Agent Card Manifests) and introduces Collections to govern which agents can discover and collaborate with each other. Think discovery before access a crucial shift for reducing exposure.


    A Quick Look at the Tenant Experience

    Agent ID Overview (Preview) dashboard showing agent counts, status, types, and blueprints: high-level posture of agents, identities, blueprints, and collections

    (more…)
  • Top 10 Browser Security Controls that can be Enforced with Microsoft Intune

    In today’s enterprise landscape, most applications are accessed through modern browsers like Microsoft Edge and Google Chrome, especially on Windows devices. While these browsers come with built-in security features, organizations must go a step further to enforce consistent and robust browser security policies across all endpoints.

    If your organization uses Microsoft Intune, you have powerful tools at your disposal to configure and enforce browser security settings. In this blog, we’ll walk through 10 essential browser security controls you can implement using Intune’s Settings Catalog to enhance protection against web-based threats.

    1. Enable Windows Defender SmartScreen

    SmartScreen helps protect users from phishing attacks and malicious websites or downloads.

    Recommended Settings:

    • Enable Windows Defender SmartScreen
    • Don’t allow SmartScreen warning overrides for unverified files
    • Don’t allow SmartScreen warning overrides
    (more…)
  • What’s New in Azure Firewall: Draft & Deploy, Selective Logging, Explicit Proxy, Security Copilot & More

    Azure Firewall continues to evolve with powerful new features that enhance security, governance, and operational efficiency.

    Whether you’re managing complex enterprise environments or hybrid architectures, these updates offer greater control, visibility, and automation.

    Here’s an overview into the latest innovations:

    Draft and Deploy – Azure Firewall Policy Changes (Preview)

    Managing firewall policies just got smarter.

    With the Draft and Deploy feature, administrators can now:

    • Clone active policies to create editable drafts.
    • Collaborate on bulk changes without impacting live environments.
    • Stage deployments to minimize disruption.
    • Apply all changes at once, improving governance and reducing human error.

    This is a game changer for environments requiring frequent policy updates, such as dynamic cloud workloads or multi team operations.

    (more…)
  • First Look at Microsoft AI Hub: What You Need to Know

    Prioritize comprehensive security and compliance measures before integrating AI into your IT ecosystem. This video showcases what the Microsoft AI Hub (preview) brings to the table in this context.

    Regards
    Sathish Veerapandian

  • Getting started with securing AI with Microsoft CSPM, AI Threat protection and Purview

    Protecting Gen AI apps requires a comprehensive strategy that encompasses data privacy, input validation, API security, and monitoring for malicious use of the outputs. Take a look at this video to get started with the options available to explore within the Microsoft Ecosystem

    Take a look at this video to know more about the same.

    Regards
    Sathish Veerapandian

  • Utilize the Azure WAF to secure your applications from Cyber Attacks

    Azure WAF is a critical security service that safeguards web application from common threats and vulnerabilities. Take a look at this video to know more about its features and utilize them in your environment.

    Hope you enjoyed this video.

    Regards
    Sathish Veerapandian