A useful first place to start when diagnosing policy issues.
It allows you to pass specific users, sources or files for assessment for various pre-built scenarios and let the tooling do the analysis while you make a coffee


A useful first place to start when diagnosing policy issues.
It allows you to pass specific users, sources or files for assessment for various pre-built scenarios and let the tooling do the analysis while you make a coffee


Microsoft’s Intune supports Defender for Endpoint onboarding on Android as app-driven, with a device counted as onboarded once the user opens the Defender app and completes setup.
Intune can now open Defender automatically during out of box setup on corporate owned fully managed (COBO) and corporate owned work profile (COPE) devices.
If the user doesn’t finish configuration, the Intune setup step stays available so they can open Defender again.
Note that personally owned work profile devices aren’t supported.

My next Purview video is available to watch now!
In this video, we take the next step in configuring Microsoft Purview by exploring one of the most important concepts in any data protection strategy: data classification.
Before you can apply labels, encryption, retention policies, or data loss prevention controls, you first need to understand what data exists in your environment and why it matters to the business.
In this episode, I walk through the different classification tools available in Microsoft Purview and show you how to use them to identify, organise, and understand your information estate.
In this episode you’ll learn how to:
✅ Understand the difference between classification and labelling
✅ Explore built-in Sensitive Information Types (SITs)
✅ Review how Microsoft Purview automatically classifies data
✅ Create custom Sensitive Information Types using Regex
✅ Reduce false positives with supporting elements and confidence levels
✅ Create Fingerprint-based Sensitive Information Types for structured forms
✅ Understand Trainable Classifiers and AI-powered document classification
✅ Create custom Trainable Classifiers using positive and negative training samples
✅ Review and improve classification accuracy through feedback training
✅ Understand Exact Data Match (EDM) classifiers and their use cases
✅ Trigger SharePoint re-indexing to force reclassification of data at rest
✅ Configure On-Demand Classification scans in Microsoft Purview
✅ Use Data Explorer and Content Explorer to investigate classified data
You’ll also gain an understanding of how Sensitive Information Types, Fingerprint Sets, Trainable Classifiers, and Exact Data Match classifiers work together to build the foundation for Information Protection, Data Loss Prevention, Insider Risk Management, and Records Management policies.
This video is part of a larger series covering:
🔹 Microsoft Purview Data Loss Prevention (DLP)
🔹 Information Protection & Sensitivity Labels
🔹 Auto-Labelling
🔹 Sensitive Information Types (SITs)
🔹 Trainable & Custom Classifiers
🔹 Insider Risk Management
🔹 Device Management & Endpoint Integration
Whether you’re deploying Microsoft Purview for the first time or looking to improve an existing deployment, understanding how to classify data correctly is critical to building effective protection and governance controls.
Microsoft announced its Decision-1 model last week. While this is not a new concept, it has placed a spotlight on decision models and their value.
It joins over a dozen widely used models from other vendors. How does it compare?
Anass Kartit compares Decision-1 with other cloud and local decision models in this interesting review:
Microsoft recently announced the upcoming (Jan ’27) integration of Microsoft Purview Data Loss Prevention (DLP) with Intune Mobile Application Management (MAM) protected mobile apps.
Until now, a lot of mobile data protection has focused on controlling actions such as copy, paste, save-as and data transfer between managed and unmanaged apps. This new capability takes things a step further by making those decisions content-aware.
Instead of simply asking “Can the user copy this?”, organisations can now ask “What is the user trying to copy?”
Clipboard actions in supported managed mobile apps can be evaluated against Purview DLP policies before data is shared. That means sensitive information can be identified and protected based on the content itself, helping to reduce the risk of accidental data leakage from mobile devices.

As mobile devices continue to play a larger role in modern work, bringing Purview’s content-aware protections to Intune MAM feels like a logical next step in strengthening data security wherever users work.
Read the M365 roadmap entry for this feature: https://www.microsoft.com/en-gb/microsoft-365/roadmap?id=567311
How can you protect against prompt injection attacks against local AI agents on devices? Well, Intune now has a policy template for Defender’s AI agent runtime protection (in Preview).
In Intune > Endpoint security > Antivirus, create a Windows policy and choose Microsoft Defender AI agent runtime protection. Set AI Agent Protection to Audit and assign a small test device group. Then check Effective settings on the device page in the Defender portal.
Watch out for the scope of that template. It configures agent-native event inspection. Network inspection needs a separate PowerShell platform script through Intune.
I wouldn’t tick the first setting and assume every local AI application was covered.
The prerequisites need attention too. Devices must be onboarded to Defender, with antivirus in active mode and real-time protection enabled. During preview, Microsoft directs test devices to the Beta Channel for platform and engine updates. Keep that on the test ring.
Licensing requires Microsoft Defender for Endpoint Plan 2, Microsoft 365 E5, Microsoft Agent 365, or Microsoft 365 E7, plus Intune for this deployment route.
https://learn.microsoft.com/en-us/defender-endpoint/configure-ai-agent-runtime-protection

I’m excited to share the first video of my Purview deployment series:
Deploying Microsoft Purview – Part 1 – AUDIT LOG | ANALYTICS | ONBOARDING DEVICES
In this video, I walk through the first steps I take when deploying Microsoft Purview in a new environment.
We’ll start by enabling the core foundations that Purview relies on, including Audit Logging, Data Loss Prevention (DLP) Analytics, Insider Risk Management Analytics, and Device Onboarding. These components provide the visibility and telemetry required to build effective data protection and insider risk strategies.
In this episode you’ll learn how to:
✅ Enable the Microsoft Purview Audit Log
✅ Verify audit logging is working correctly
✅ Enable Data Loss Prevention (DLP) Analytics
✅ Enable Insider Risk Management Analytics
✅ Configure Insider Risk integrations and user-level insights
✅ Connect Microsoft Defender for Endpoint with Intune
✅ Deploy Defender onboarding policies from Intune
✅ Onboard devices into Microsoft Purview
✅ Understand alternative onboarding methods including Group Policy, Configuration Manager, and scripts
Check out the video here:
Passwords have a habit of finding their way into places where they should not be.
These are not password-protected files. They are normal files where somebody has written a password, username, API key, token or another credential directly into the content.
Microsoft Purview already has built-in credential detection that can help us find this type of data.
And importantly, if the objective is simply to find these files, we do not need to start by creating a DLP policy.
We can start with Data Explorer.
Let’s take a look.
Microsoft Purview includes a number of built-in Sensitive Information Types, usually shortened to SITs, specifically designed to identify credential patterns.
A good starting point is All Credential Types.
This is a bundled Sensitive Information Type that includes detectors for things such as:
There are also individual credential SITs including:
This means we can start broad and then narrow the search down if required.
Before going any further, there is something worth understanding.
Purview is not a magic password scanner.
It does not simply look through every document and somehow know that a random piece of text is a password.
The credential Sensitive Information Types use supported patterns, keywords and contextual evidence to identify credentials.
For example, something structured like:
username=admin;password=SomePassword
is much more aligned with the documented credential detectors than somebody writing:
The password for the old system is bluechair
Microsoft also documents that the General Password Sensitive Information Type uses English-only password-related keywords.
So this approach can help identify credentials, but it should not be treated as a guarantee that every password somebody has ever written into a document will be found.
Before searching production data, it is worth seeing how the credential detection actually works.
In the Microsoft Purview portal:


I would create a few simple test files containing things such as:
Do not use real credentials for testing.
The idea here is simply to understand what Purview is capable of detecting before looking at production data.
Now we can see what Purview already knows about the data.
In the Microsoft Purview portal:
Data Explorer provides visibility into content that Purview has classified using things such as Sensitive Information Types and sensitivity labels.
Rather than creating another policy just to discover the data, we can use the classification information that Purview already has.
Within Data Explorer, search for the credential Sensitive Information Type.
A good place to start is:
All Credential Types
This gives us the broadest view of content that Purview has classified as containing supported credential patterns.
From there, we can start drilling into the results.
For example:
All Credential Types

Then: SharePoint
Then: Site
Then: Folder
Then: File

The same approach can be used with OneDrive.
This gives us a way of working from the classification down to the actual location of the file.
All Credential Types is useful for the initial discovery because it covers a large number of credential types.
But sometimes we might want to be more specific.
Instead of searching for everything, we could search for individual Sensitive Information Types such as:
General Password
or: User Login Credentials
Or perhaps we are specifically interested in secrets used by applications: Microsoft Entra Client Secret
Or development credentials: GitHub Personal Access Token
Or database credentials: Azure SQL Connection String
This is where the built-in credential Sensitive Information Types become really useful.
We can start broad and then narrow the investigation depending on what we find.
Once we have identified a file, the next step is to determine whether the match represents a genuine credential.
There are separate permissions within Purview for this.
The Data Explorer List viewer role allows an administrator to see items and their locations.
The Data Explorer Content viewer role provides additional access to the content of those items.
That second permission is obviously sensitive.
If somebody can investigate files containing credentials, they may potentially be able to see the credential itself.
Access to this capability should therefore be limited to the people who genuinely need it.
There is another limitation worth knowing about.
Credential scanning classifiers do not currently support:
That means credential matches need to be investigated carefully.
Finding the file is only the start.
If a genuine credential is discovered, a remediation process could look something like this:
The important part here is rotate or revoke the credential.
Simply deleting the password from the document does not mean the credential has not already been seen or copied.
This is where I think the distinction becomes important.
If the requirement is:
“Show me files that Purview has identified as containing credentials.”
Start with Data Explorer.
There is no need to create a DLP policy just to perform that discovery.
But once we have established that credentials are being stored in documents, the next question becomes:
How do we stop this happening again?
That is where Data Loss Prevention becomes useful.
A DLP policy can use the same credential Sensitive Information Types as conditions.
For example:
Content contains > Sensitive information types > All Credential Types
The policy could then be used to:
So I see the two capabilities as solving slightly different problems.
Data Explorer helps us understand what is already there.
DLP helps us introduce controls around what happens when that type of data is detected.
Once the initial discovery has been completed, a custom DLP policy could be created.
In the Microsoft Purview portal:
I would start any new policy in simulation mode.

This allows the policy conditions to be evaluated before introducing enforcement.
Once the results have been reviewed, the policy could then be tuned and appropriate alerts, notifications or restrictions introduced.
Advanced classification scanning and protection must be enabled when credential scanning Sensitive Information Types are used with Endpoint DLP.
This can be configured from:
Data Loss Prevention > Overview > Data loss prevention settings > Endpoint settings > Advanced classification scanning and protection
The Devices location can then be included within the DLP policy.



The important thing is that DLP comes after we understand the problem, rather than creating a DLP policy simply because we want to search the data.
An E5 licence is required to use credential scanning Sensitive Information Types.
This is worth checking before planning the solution.
There are really two parts to this.
Use the built-in credential Sensitive Information Types with Data Explorer.
A simple starting point is:
All Credential Types > SharePoint / OneDrive > Site > Folder > File
Then investigate the matches and remediate genuine credentials.
Once we understand what is being stored and where, we can introduce Data Loss Prevention using the same credential Sensitive Information Types.
That gives us the ability to move from simply discovering credentials to alerting, educating users and potentially restricting what can happen when credentials are detected.
So the approach becomes:
Sometimes the simplest place to start is not another policy.
It is looking at what Purview already knows about your data.
Note: When configuring DLP policies, keep in mind the service limitations of Purview. Particularly the 2 million character scanning limit for file content. If your environment has many files like this, consider deploying auto labelling policies based on file criteria that is likely to occur earlier in the file, then targeting DLP policies at the labels.
Intune deployment plans are now in public preview. Why? Because repeatedly changing group assignments to move a rollout along is a faff, and this gives us a reusable way to stage it.
Start in Devices > Manage devices > Deployments. Create a plan with your test, pilot and broader groups, then set the wait between rings. The plan holds the rollout pattern; a deployment uses it to deliver one app or policy. I would begin with a harmless settings catalog change on test devices.

Watch out for existing assignments. They remain on the underlying app or policy, so a pre-existing broad assignment can defeat your carefully staged deployment plan. If a group assignment collides with an existing payload assignment (payload being the underlying app or policy), Intune places the deployment in an error state and prevents further rollout until the conflict is resolved.

The preview supports Windows Win32 apps, Enterprise App Catalog apps, settings catalog policies and endpoint security policies. For apps, it supports Required installations, not Available or Uninstall. Enterprise App Catalog auto-update is not supported with deployments.

This of course does not replace the need to test the payload changes yourself before you send it to your deployment plan! You can pause, resume or cancel deployments, but cancelling a deployment doesn’t automatically act as a rollback, so consider how you’ll recover devices that have already received the change.
https://learn.microsoft.com/en-us/intune/device-management/deployments/overview
Autopilot agents in Microsoft Foundry are digital workers that operate under two identities: an agent identity and an agent user account.
The agent identity is the standard Entra identity used for authentication and governance. The additional user account is what makes an agent an autopilot agent. It gives the agent its own email, calendar, OneDrive, Teams presence, and a place in the organisation chart. This allows the agent to participate naturally in Teams chats, access SharePoint, respond to events, and work with groups rather than only individuals.
Without this user account, an agent can only perform Microsoft 365 actions on behalf of a signed‑in user, which is limiting in collaborative or event‑driven scenarios. The autopilot model resolves this by giving the agent a persistent identity that fits into everyday organisational workflows.
The lifecycle begins with the Blueprint layer. Developers define the agent’s purpose, capabilities, and boundaries, then publish the blueprint. This becomes the template for every instance created later and is effectively the agent’s job description and technical specification.
The Instance layer is where managers hire agents from that blueprint. Each instance receives its own identity, is onboarded into a team, and is granted access to the resources it needs. This is where the agent becomes operational and able to work within Microsoft 365 surfaces such as Teams and SharePoint.
Above both sits the Fleet layer, owned by tenant administrators. This is where approval, policy, consent, licensing, and oversight are applied. The fleet view provides visibility across all agents in the tenant and ensures they comply with organisational rules.
The lifecycle follows a predictable sequence:
✅ Provision – Azure administrators set up the Foundry platform and assign developer permissions.
✅ Build and publish – developers define and test the blueprint.
✅ Approve and configure – tenant administrators review the blueprint, apply policy, and decide who can hire agents from it.
✅ Hire – managers create an instance and give it a role.
✅ Onboard – access managers or team leads grant the agent the resources it needs.
✅ Operate – the agent performs its work, with managers and teammates observing and adjusting as required.
✅ Offboard – when the agent is no longer required, the manager removes it.
✅ Retire or delete – tenant administrators or developers stop new hires or remove the blueprint entirely.

The strength of this model is in its clarity. Each role has a defined responsibility. Autopilot agents become part of the organisation’s operational structure rather than isolated experiments, making them easier to govern, secure, and scale.
Further reading: https://learn.microsoft.com/en-us/azure/foundry/agents/concepts/autopilot-lifecycle