Data protection sounds straightforward until you’re faced with a blank Microsoft Purview policy and a business that isn’t about to slow down while you figure it out.

After working with DLP across different environments, a few patterns become clear: what works well, what tends to cause problems, and—perhaps most importantly—what looks perfectly reasonable on day one but quietly creates chaos six weeks later.

After doing this a few times, here are the lessons that stuck with me.

Don’t Start with Policies. Start with Looking.

The most common mistake I see is someone opening Purview, creating a DLP policy, adding a few Sensitive Information Types (SITs), and turning it on.

I understand the temptation. You’ve been asked to “implement DLP,” so you want to show progress.

But you can’t protect data you haven’t found.

Before writing a single rule, spend some time understanding where sensitive data actually lives, who is using it, and how it moves around the organization.

Look at the environment and ask the questions nobody gets excited about:

• Where does personal data actually live?
• Who handles financial information every day?
• Is sensitive information already being shared externally?
• Which Teams and SharePoint sites contain regulated or confidential content?
• Are users moving data to personal cloud storage or unmanaged devices?
• Which business processes genuinely require sensitive information to leave the organization?

Most of what you find will probably be legitimate. Some of it won’t be.

The important thing is understanding the difference before you start blocking things.

This discovery phase isn’t glamorous, but it’s often the difference between a DLP program people trust and one they eventually learn to work around.

## Get Your Classification Right First

DLP on its own is useful.

DLP combined with Microsoft Purview Information Protection is considerably more powerful.

If you haven’t already done it, establish a sensible sensitivity-label strategy before trying to build hundreds of DLP rules.

For example:

• Public
• General
• Confidential
• Highly Confidential
• Highly Confidential – Restricted

The exact labels will depend on your organisation. Don’t copy somebody else’s taxonomy simply because it looks good in a presentation.

Your labels should reflect how the business actually thinks about information.

Where appropriate, use auto-labelling for existing content and mandatory labelling for new or modified content. Sensitivity-label inheritance can also help maintain classification as information moves between documents, emails, and collaboration workloads.

Once classification is in place, DLP becomes much more meaningful.

Instead of relying entirely on pattern matching, you can make decisions based on how the organization has classified the information.

For example, a policy that prevents content labelled **Highly Confidential** from being shared externally doesn’t necessarily need to figure out what the document contains. The classification already tells you that the organisation considers it sensitive.

That’s a much stronger foundation.

Custom Sensitive Information Types are also worth the effort, particularly when you have business-specific identifiers that Microsoft’s built-in patterns won’t understand.

Audit Mode Isn’t Optional

Please don’t deploy your first DLP policy directly into enforcement. It happens more often than you might expect.

It usually ends with a lot of angry users and a security team spending the next few days creating exceptions.

Purview gives you the ability to observe how a policy behaves before you start blocking users. Use it.

Audit and simulation phases let you understand:

• How many matches you’re actually getting
• Where the matches are occurring
• Which rules are generating false positives
• Which departments are likely to be affected
• What the expected alert volume will look like
• Whether your thresholds are realistic

A deployment sequence that has worked well for me is:

1. Discover and assess
2. Deploy in audit/simulation mode
3. Review the incidents and matches
4. Fine-tune conditions and thresholds
5. Enable user notifications and Policy Tips
6. Move to enforcement
7. Monitor and continuously tune

The important part is that this is a cycle, not a one-time project.

DLP policies need to evolve as the business evolves.

Protect the Data That Actually Matters First

Trying to protect every possible type of sensitive information on day one is a great way to create an alert queue nobody will ever clear.

Start with your highest-risk information.

Depending on the organization, that might include:

• Intellectual property
• Payment card information
• National Insurance or Social Security numbers
• Passport information
• Banking information
• Healthcare records
• Customer personal data
• Credentials and authentication information
• Highly confidential business information

Get the highest-risk scenarios working first. Once the organization trusts the process, you can expand it.

Trying to boil the ocean on day one usually creates more resistance than security.

Built-in SITs Are a Starting Point, Not the Finish Line

Microsoft provides a large collection of pre-built Sensitive Information Types covering things such as credit card numbers, IBANs, passport numbers, NHS numbers, tax identifiers, National Insurance numbers, and many others.

They’re an excellent starting point.

But don’t assume that because Microsoft provides a SIT, it will perfectly identify your organization’s data.

Pattern matching at enterprise scale will always produce false positives.

A sequence of numbers might look like a financial identifier but actually be an invoice number. An employee ID might resemble another type of sensitive identifier. Context matters.

Before enforcing a policy based heavily on a particular SIT, test its accuracy in your environment.

For business-specific data, consider:

• Custom Sensitive Information Types
• Exact Data Match
• Trainable classifiers
• Sensitivity labels
• Other contextual conditions

The more valuable the data, the more important accuracy becomes.

A highly restrictive policy with poor detection accuracy is not a strong control. It’s just a frustrating one.

Policy Tips Do More Work Than People Give Them Credit For

DLP isn’t just about blocking people.

Some of its greatest value comes from preventing the mistake before it happens.

That’s where Policy Tips are useful.

Imagine someone is about to send a document containing sensitive customer information to an external recipient.

Instead of immediately blocking the action with no explanation, you can tell the user what they’re doing, explain why the action is restricted, and point them towards the approved way of sharing the information.

That small interaction matters.

People are much more likely to change their behaviour when they understand **why** something is being blocked.

Over time, that means fewer repeated violations, fewer support tickets, and fewer users trying to find ways around your controls.

Good DLP should educate users as much as it restricts them.

Alert Fatigue Will Kill Your Program

I’ve walked into environments generating thousands of DLP alerts where nobody had looked at the majority of them for weeks.

At that point, DLP isn’t really a security control anymore. It’s noise. The answer isn’t necessarily to reduce security. It’s to improve the signal.

Tune your severity levels. Reduce unnecessary matches. Make thresholds realistic.

Route incidents to teams that can actually investigate them. Integrate relevant signals with Microsoft Defender XDR where appropriate. And, most importantly, review trends regularly.

Ask yourself:

Are we finding real risky behaviour, or are we simply generating events because we can?

A DLP policy nobody is monitoring isn’t providing much protection.

Don’t Forget the Endpoint

Sensitive information doesn’t stay neatly inside Microsoft 365.

• Users download files.
• They copy information to USB devices.
• They print documents.
• They upload files to other services.
• They move data between applications.
• That’s where Endpoint DLP becomes important.

Depending on your requirements, you can use it to control and monitor activities such as:

• USB transfers
• Printing
• Copying to network shares
• Clipboard activity
• Copying data between applications
• Uploading files to unapproved cloud services

If you’re building towards a Zero Trust data protection strategy, endpoint DLP shouldn’t be something you bolt on six months after deploying Microsoft 365 DLP.

Think about the endpoint from the beginning.

Your users don’t think about “Microsoft 365 data” and “endpoint data” as separate things.

Neither should your security architecture.

Talk to the Business Before You Write the Policy

One of the easiest mistakes for a security team to make is designing controls around technical requirements without understanding the business process underneath them.

Bring the right people into the conversation early:

• Legal
• Compliance
• Data owners
• Information governance
• HR
• Finance
• Security
• Business representatives

Take payroll as an example.

Finance may have a completely legitimate reason to send employee information to an approved third-party payroll provider.

If your policy simply says:

“Block sensitive information from leaving the organization.”

You’ve technically achieved your objective. You’ve also potentially broken a critical business process.

Understanding legitimate workflows before enforcement gives you the opportunity to build those scenarios into the design rather than dealing with exception requests afterwards.

Good DLP should support the business without compromising the security objective.

Make DLP Risk-Aware, Not Just Rule-Aware

Not every user presents the same level of risk.

A user accidentally sending one sensitive document to an external recipient is different from someone repeatedly downloading large amounts of sensitive information, uploading it to personal cloud storage, and exhibiting other risky behaviours.

This is where the broader Purview and Microsoft Security capabilities become important.

Purview can work alongside capabilities such as Insider Risk Management and Adaptive Protection to make controls more context-aware.

Instead of applying exactly the same level of restriction to everyone, you can increase protection when the risk warrants it.

For example:

• Tighter controls for higher-risk users
• Additional restrictions when risky behaviour is detected
• Increased monitoring during sensitive periods
• More restrictive controls when multiple risk signals appear together

That moves the conversation from:

“Did the rule trigger?”

“How much risk does this activity actually represent?”

That’s a much more mature way of approaching DLP.

Test It Like It’s Real — Because It Will Be

A lab test can prove that a policy technically works.

It cannot tell you how that policy will behave when thousands of real users start using it.

Test realistic scenarios.

For example:

• External email
• Email attachments
• Teams messages
• SharePoint sharing
• OneDrive sharing
• USB copying
• Printing
• Network shares
• Uploading to cloud services
• Copy and paste
• Business-specific applications
• AI-assisted workflows

That last one is becoming increasingly important.

The way users create, transform, and share information is changing rapidly because of generative AI.

Your DLP strategy needs to account for that reality rather than assuming that traditional document and email workflows are the whole picture.

And don’t test only with your security team.

Bring in users from Finance, HR, Legal, Sales, Engineering, and other departments.

They’ll find workflows you never thought about.

Measure It, or You’re Just Guessing. A DLP program needs measurable outcomes. Otherwise, how do you know whether it’s actually working?

Some useful metrics include:

• Reduction in sensitive data exposure
• Number of policy matches
• Number of high-severity incidents
• User overrides
• External sharing trends
• Repeat violations
• Incident response times
• Top departments generating incidents
• Top data types involved
• High-risk user activity
• Number of exceptions created
• Percentage of incidents resolved within target time

Don’t just report how many alerts DLP generated.

That number alone doesn’t tell you whether your security posture improved.

The better question is:

**Are we reducing risky behaviour and protecting the information that matters?**

The Bigger Picture

DLP works best when it isn’t treated as a standalone product or policy.

It should fit into a broader data security architecture that includes:

• Microsoft Purview Information Protection
• Sensitivity labels
• Data Lifecycle Management
• Insider Risk Management
• Adaptive Protection
• Audit
• eDiscovery
• Microsoft Defender
• Entra ID
• Conditional Access
• Endpoint security
• Identity and device controls

That’s when DLP starts becoming part of a proper Zero Trust data protection strategy rather than simply another set of rules generating alerts.

The goal isn’t to create more policies.

The goal is to create meaningful controls around the organisation’s most important information.

Final Thoughts

Microsoft Purview DLP is a powerful capability.

But the technology itself isn’t what makes a DLP program successful. The design does. Start with discovery.

• Understand where sensitive information actually lives.
• Get your classification strategy right.
• Use built-in SITs, but don’t blindly trust them.
• Build custom detection where the business requires it.
• Start in audit and simulation.
• Use Policy Tips to educate users.
• Tune aggressively to reduce noise.
• Bring the business into the conversation.
• Extend protection to endpoints.
• Use risk signals where appropriate.
• And measure whether the program is actually reducing exposure.
• Most importantly, remember what DLP is supposed to achieve.

**The goal isn’t to stop people working.**

The goal is to let people collaborate, share information, and get their jobs done while keeping sensitive data where it belongs.

That balance has become much harder — and much more important — with hybrid working, cloud collaboration, generative AI, and increasingly complex regulatory requirements.

When it’s designed properly, Purview DLP can help strike that balance.

When it’s designed badly, it becomes another security control users learn to work around.

The difference usually isn’t the technology. It’s how you deploy it.