CHAPTER 4/8 Next Steps to Secure and Accelerate Your AI Journey

Engineering Roadmap for 2026. Hard-edged. Technical. No sugar-coating.
0. Prologue: “Accelerating AI is easy. Doing it securely is an elite sport.”

Almost every organisation today is doing three things:

1. Stuffing “smart” features everywhere.

2. Giving Copilot to anyone with a pulse.

3. Trying to stretch Zero Trust over LLMs the same way they stretched VPNs over the cloud in 2014.

The result?

• chaos in the data estate

• AI-driven leaks

• uncontrolled permission sprawl

• zero visibility

• AI agents that “go wherever they fancy”

And Microsoft politely says, in the Ignite/Build tone:

“You must secure AI before you accelerate AI.”

But the honest version is far sharper:

“If you accelerate AI without security — you accelerate your own death.”

Let’s move to the roadmap.

1. Architectural Model 2026: The AI Security Layer Cake

To accelerate AI without killing your company, you need a five-layer security architecture.

Here it is — in ASCII, just how you like it:

Each layer must be secured before AI even touches it.

2. Step 1 — Build the data map AI is allowed to see

AI sees everything the user sees (and often more — unless you restrict it).

So Step 1:

2.1. Implement Full Data Estate Discovery

Purview DSPM must scan:

• SharePoint

• OneDrive

• Exchange attachments

• SQL

• Azure Data Lake

• On-prem NAS/SMB

• File servers

• Git repositories

• Confluence

• ERP/CRM storage

And build a Data Map:

datasets → sensitivity → owners → access → lineage → risk

Why does this matter?

Because:

• AI is the perfect hunter of sensitive data

• It will find everything you forgot existed

• It will correlate it semantically

• It will surface it through summaries, tables, JSON

As Microsoft states in the MDDR:

“AI is extremely effective at discovering patterns that human operators overlook.”

Meaning: if you have a forgotten password in a 2019 document, Copilot will find it.

3. Step 2 — Establish the Data Sensitivity Foundation (Purview Labels)

AI does not understand “confidential”.
Purview does.
And Purview can pass that knowledge to the AI Security Plane.

3.1. Enable auto-labelling across all data

Auto-labelling must cover:

• content

• metadata

• attachments

• AI-generated output

• derivative documents

• tables

Minimum label set:

3.2. Turn on propagation

This is the crown jewel.

If Copilot reads a Confidential file,
→ the new output must also be Confidential.

Without propagation, AI will shred your security labels into confetti.

4. Step 3 — Deploy the AI Request Interception Layer

This is where it gets scientific.

Purview ARIL must evaluate every AI request:

• what data the AI wants

• from where

• for what purpose

• whether it has rights

• whether the context matches

• what the user is doing

• what the token is doing

• the risk level

• whether output violates policies

Real log example:

Without this layer, AI outputs everything.

5. Step 4 — Deploy the Output Firewall (LLM Scrubbing Engine)

Protection from:

• hallucination leakage

• oversharing

• data remix

• sensitive inference

Output firewall performs:

• masking

• redaction

• truncation

• sensitivity filtering

• PII/PHI removal

• JSON sanitisation

• table scrubbing

Example:

Request:

“Give me a summary of employee performance reports.”

Raw AI Output:

• employee names

• bonuses

• complaints

• termination plans

• KPIs

Scrubbed Output:

• aggregated KPIs

• trends

• general issues

• anonymised data

Without output-firewall, Copilot = “leak-as-a-report”.

6. Step 5 — Identity, Token, and Access Hardening

Microsoft keeps repeating a brutal truth:

AI follows the token — not the human.

If the token is stolen, the AI becomes your enemy.

6.1. Enable Token Protection (Entra ID)

Bind the token to:

• device

• key

• session

• geolocation

• network context

6.2. Enforce phishing-resistant MFA

FIDO2, Passkeys — non-negotiable.

6.3. Conditional Access with AI-context signals

CA must evaluate:

• what the AI agent is doing

• with which data

• which product

• sensitivity level

• which tool is invoked

• what the refresh token is doing

6.4. Session Attestation 2026

A quietly introduced new layer:

• device integrity

• context integrity

• token integrity

7. Step 6 — Restrict AI Tools (Plugins, Tools, Skills)

The most dangerous thing is giving AI too many tools.

Mini-table:

8. Step 7 — AI Agent Isolation & Sandboxing

Here comes the real R&D.

8.1. Execution Sandbox (Azure / Fabric)

AI agents must run in:

• isolated containers

• limited tokens

• no full Graph access

• memory sandbox

• output size constraints

• strict DLP

8.2. Tools-per-Agent Isolation

Each agent must have its own tools, not a global pool.

9. Step 8 — Deploy AI Behaviour Analytics

AI behaves differently from humans.

Purview + Defender analyse:

• request patterns

• data types

• frequency

• agent types

• MITRE correlations

• anomalies

• “non-human-scale” behaviour

Example:

10. Step 9 — Shadow AI Discovery & Control

Shadow AI includes:

• unauthorised models

• custom LLMs

• private endpoints

• HuggingFace scripts

• local agents on dev laptops

• research servers

Purview AI Governance detects via:

• port scans

• network traffic

• API logs

• token patterns

• anomaly signatures

11. Step 10 — Build the AI Security DevOps Pipeline

AI control must enter CI/CD.

Pipeline must include:

11.1. Prompt Tests

• injection

• harmful manipulation

• cross-context extraction

11.2. Tool Access Tests

• privilege boundaries

• token misuse

• RCE attempts

11.3. Model Behaviour Tests

• hallucination leakage

• sensitivity bypass

• DLP evasion

11.4. Data Exposure Tests

• synthetic PII injection

• fuzzing

• chain-of-thought leakage detection

12. Step 11 — Create the AI Incident Response Plan

Must include:

• token rotation

• plugin revocation

• agent blocking

• audit of all AI endpoints

• temporary Data Lake isolation

• lineage debugging

• forensic output analysis

13. Step 12 — Enable AI Drift Detection

Models:

• degrade

• shift

• lose sensitivity awareness

• hallucinate more dangerously

• bypass DLP unintentionally

Monitor:

• leak frequency

• error types

• response length

• “too precise” answers (danger!)

• pattern anomalies

14. Step 13 — Governance-as-Code (GaaC)

Purview + Entra + Defender policies must be IaC:

• Terraform

• Bicep

• Pulumi

Why?

• versioning

• rollback

• CI/CD

• scalability

• risk control

15. Step 14 — Security Telemetry Fusion

The strongest defence = correlation across:

• Purview AI logs

• Defender identity logs

• Entra token logs

• Microsoft Graph logs

• endpoint telemetry

• AI output samples

• lineage graph

Fusion Engine identifies:

• token replay

• anomalous output

• leaks

• plugin-based attacks

• prompt injection chains

16. Step 15 — Executive Reporting (AI Risk Posture Dashboard)

A 2026 CISO must see:

• AI–data interaction heatmap

• AI exposure score

• DLP bypass attempts

• token replay attempts

• drift score

• shadow AI detections

• plugin risk scoring

• classification trends

• behavioural anomalies

Fabric + Purview + Defender deliver this.

Final Takeaway of CHAPTER 4:

“Accelerating AI is only possible once you’ve built the walls and the electric fence.”

AI without oversight → catastrophe.
AI with the right architecture → a business accelerator.

rgds,

Alex

… to be continued…