Innovation without the risk.

Secure AI & Emerging Tech

Adopt AI with guardrails. We help you put policy, governance and data protection in place before AI sees everything.

Why this matters

AI is already inside your business. The policy and posture need to catch up.

Staff are pasting sensitive data into chatbots, Copilot can surface anything a person is allowed to reach, and agents are starting to act on their own. The risk is not the technology, it is adopting it without guardrails. We put the policy, governance and data security in place so you can move fast without moving blind.

What we do

A practical programme that turns AI from an unmanaged risk into a governed capability.

  • ✓AI policy and acceptable use, written for your business and your people.
  • ✓Data privacy and model governance for the tools you actually use.
  • ✓AI risk and ethics assessments, mapped to real business consequences.
  • ✓Responsible adoption roadmaps that sequence the rollout safely.
  • ✓Training and enablement so the policy sticks.
Securing AI · the platform comparison

There's no single winner in AI-data security.

Anyone who tells you otherwise is selling. Each platform leads at a specific job. We start with what you already own, then add best-of-breed only where there is a real gap. See exactly where you are covered, and where you are exposed.

Compare by what you own
HOW WE THINK ABOUT IT

Microsoft is the foundation. The rest extend it where it can't reach.

Almost every organisation we work with already owns Microsoft 365. That makes Purview, Entra and Defender the natural foundation, and the only engine that writes a sensitivity label Copilot will honour. The other platforms aren't replacements. They reach into the places Microsoft structurally doesn't.

Principle 01

Start with what you own

Most of the foundation is already licensed under E3 or E5. We light up what you're paying for before recommending anything new.

Principle 02

Add only for a real gap

A second platform earns its place when there's a job Microsoft can't do well: data beyond M365, runtime, inline control, insider exfiltration.

Principle 03

Make them work together

Best-of-breed discovers and classifies; Microsoft enforces. A shared classification taxonomy is what turns separate tools into one system.

WHY THERE'S NO SINGLE WINNER

The AI-data problem has four planes. No one platform owns them all.

Sensitive data lives and moves across four distinct planes. Each platform was engineered for one of them. Understanding which plane your risk sits on is how you choose, not a feature checklist.

EST
1
Plane 01 · Microsoft 365 content

Where your people work every day

SharePoint, OneDrive, Teams, Exchange. The data Copilot reads, and the labels that decide what it can surface. Content-centric, at rest.

Led by
The only engine that governs Copilot from the inside.
PurviewEntraDefender
2
Plane 02 · Data at rest, everywhere else

All your other data stores

AWS, Azure, GCP, Snowflake, databases, data lakes, on-prem file shares, shadow and orphaned data Microsoft can't see.

Led by
A dedicated DSPM for breadth across the estate.
CyeraNetskopeCrowdStrike
3
Plane 03 · Data in motion

Where data goes when it leaves

Web, SaaS, cloud egress, API and container traffic, the moment sensitive data moves to an unsanctioned app or a free AI tool.

Led by
Inline (SSE/CASB) or at runtime (eBPF). Two valid architectures.
NetskopeCrowdStrike
4
Plane 04 · The human plane

The person at the keyboard

Email, the browser, the endpoint. Insider exfiltration, copy-paste into ChatGPT, BYOD and unmanaged-device access, the human moment of risk.

Led by
Email, insider risk, secure browser access and the human moment of risk.
MimecastNetskopePurview IRMCrowdStrike
AI
TWO LAYERS, NOT ONE

Securing AI is two problems, not one.

The four planes cover where your data lives. AI adds a second layer on top: the live interaction between your people, your agents and the models. A complete answer secures both.

Layer 01

The data layer

What exists, where, who can reach it
The classic posture problem: discover and classify sensitive data across every plane, fix oversharing, and govern what AI is allowed to surface. This is DSPM plus Purview, the four planes above.
Microsoft PurviewCyeraNetskope One DSPMCrowdStrike
sits beneath
Layer 02

The AI interaction layer

How AI is actually used, in real time
The new layer: securing the prompts and responses themselves, catching prompt injection and jailbreaks, stopping data that leaks through AI rewriting which pattern DLP misses, and governing autonomous agents over APIs and MCP. Inspection that reads intent, not just keywords.
Netskope AI SecurityCrowdStrikeMicrosoft (Azure AI / Prompt Shields)
THE CAPABILITY MAP

What each platform leads, complements, or leaves to others.

Twelve jobs to be done, twelve honest answers per platform. Tell us what you already own and the map shows where you're covered, where it's partial, and where you have a genuine gap.

What do you already own?
Toggle the platforms in your stack. We'll highlight your coverage and flag the gaps.
Nothing selected yet. Most clients start with Microsoft already in place.
Primary fit · leads this job
Strong complement
Partial · with limits
Not its job, by design

Ratings reflect each platform's GA positioning and are kept defensible against vendor and Microsoft documentation. Click a job to see the best-fit call. Click a platform name to focus its column.

THE RIGHT FIT, BY SITUATION

Five common starting points. Five honest recommendations.

The discovery tells us which of these you're closest to. Here's how we'd sequence the fix in each case, leading with the platform that genuinely fits.

HOW THEY WORK TOGETHER

Discover broadly. Enforce centrally. Monitor continuously.

The platforms aren't rivals in a good design, they're a relay. Best-of-breed tools discover and classify across the planes Microsoft can't reach, then feed that classification into Purview, which enforces it for Copilot and DLP inside Microsoft 365. The loop closes with continuous monitoring.

Step 01
Discover & classify
Across every plane Microsoft can't reach, at speed.
CyeraNetskopeCrowdStrike
Step 02
Shared taxonomy
One agreed meaning for "sensitive" across tools.
Step 03 · Microsoft
Purview enforces
Labels, DLP and Copilot controls, centrally.
Step 04
Copilot & AI
See only governed, permitted data.
Continuous monitoring drift and new exposure feed back to discovery, continuously.
RESOURCES

Go deeper on AI data security.

Two practical resources to help you move from awareness to action, no vendor pitch required.

Watch the webinar replay
On-demand webinar

Your data is already exposed.

A board-level look at how Copilot and everyday AI tools quietly surface sensitive data, with real Microsoft 365 examples. No vendor pitches, no product demos.

Watch the replay
AI Policy Template
Free template

AI Policy Template

A customisable acceptable-use and governance policy for AI: usage guidelines, privacy and data-handling rules, and a governance framework you can adapt in an afternoon.

Get the template
SharePoint Document Library Architecture guide
Free guide

SharePoint Document Library Architecture

The future-proof plan to secure data management: a central library, a three-layer folder model, and access controlled by Entra ID groups.

Download the guide
Explore related
Netskope AI ProtectionCyeraData Protection & Governance
AI security

Securing AI before it surfaces what it should not.

Plain answers on Copilot, Skope AI guardrails and shadow AI.

Why does AI need different security?

Generative AI inherits the permissions of the person using it. Microsoft 365 Copilot can read every file the user can read, so any over-shared SharePoint folder or stale permission becomes a one-click discovery.

What is Microsoft 365 Copilot exposing?

Not new data, but data your users could already find through search if they looked hard enough. Copilot makes that latent exposure trivial and instant. The fix is sensitivity labels, data access governance, and tightening permissions before you turn it on.

What does Netskope Skope AI do?

It sits inline between users and AI apps, both sanctioned (Copilot, ChatGPT Enterprise) and unsanctioned (public ChatGPT, Gemini, Claude). It applies DLP to prompts and uploads, blocks risky use, and gives visibility into what your people feed AI.

Do we need to label data before using Copilot?

Strongly recommended. Sensitivity labels drive downstream protection: Copilot honours labels in queries and outputs, and Purview DLP can stop labelled content from being shared inappropriately.

What about shadow AI (ChatGPT, Gemini, Claude in browsers)?

Netskope sees and controls it. We can block, coach (warn the user) or apply DLP to public AI tools, so people are not pasting client data into a free model.

How do we start an AI readiness program?

A short AI Readiness assessment: where your data lives, what is labelled, what Copilot would see today, and what your people are using already. From that we build a costed roadmap to safe rollout.

Not sure which fit is yours? That's what the discovery is for.

A short, fixed-scope assessment shows what you already own, what it covers, and the one or two gaps worth closing, with a costed plan and no vendor agenda. You get the right stack for your risk, not the biggest bill.

START MY AI READINESS CHECK TALK TO AN EXPERT