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Security & Data Governance

Security & Data Governance

Security architecture and data governance should reflect the organisation's risk, regulatory obligations, operating model and technology footprint — not apply the same control set everywhere.

The framework and control guidance on this page is educational and reference material, not compliance or legal advice. Confirm applicable obligations with qualified legal, risk and compliance advisors for your organisation.

Framework selection

An architecture decision, not a shopping list

Framework selection starts with the organisation, not the product catalogue. Microsoft controls implement a chosen framework — they are not themselves the governance framework.

Organisation profile

SizeIndustryData sensitivityRegulatory obligationsThreat exposureCloud maturityGeographic requirements

Step 1

Security baseline

A pragmatic minimum control set

Step 2

Regulatory / industry overlays

Obligations layered on top of the baseline

Step 3

Microsoft control implementation

Entra, Defender, Sentinel, Purview and policy

Step 4

Continuous assurance

Evidence that controls remain effective

Framework → Controls → Microsoft implementation → Evidence

Reference guidance by organisation type

The right baseline changes with the organisation

Each example distinguishes the framework (the governance baseline) from Microsoft platform controls (how it's implemented). Expand a profile for detail.

Small / medium business

A pragmatic control baseline without enterprise-scale governance overhead.

Framework / baseline

Australian Essential Eight
CIS Controls

Microsoft implementation examples

Entra MFA
Conditional Access
Intune
Defender
Endpoint hardening
Privileged access controls
Backup / recovery
Security monitoring

General enterprise

Risk-based governance, measurable controls and enterprise assurance.

Framework / baseline

NIST Cybersecurity Framework
ISO/IEC 27001
CIS Controls

Microsoft implementation examples

Entra ID
Privileged Identity Management
Defender XDR
Sentinel
Azure Policy
Purview
Intune
Azure logging
Security operations

Australian government / high-assurance

Architecture built around identity assurance, segmentation and continuous monitoring.

Framework / baseline

ASD Information Security Manual
Essential Eight

Microsoft implementation examples

Identity assurance
Administrative separation
Privileged access
Logging
Network segmentation
Information classification
Data sovereignty
Secure configuration
Continuous monitoring

Financial / APRA-regulated

Operational resilience, third-party risk and audit-ready control evidence.

Framework / baseline

APRA CPS 234
APRA CPS 230
ISO 27001
NIST CSF (where appropriate)

Microsoft implementation examples

Information security capability
Operational resilience
Third-party risk
Incident response
Control assurance
Audit evidence
Business continuity

Payment environments

Minimising and segmenting cardholder data scope before adding controls.

Framework / baseline

PCI DSS

Microsoft implementation examples

Scope minimisation
Segmentation
Identity
Logging
Encryption
Vulnerability management
Access control

Software / digital product organisations

Securing the software supply chain from commit to runtime.

Framework / baseline

NIST SSDF
OWASP
CIS
NIST CSF

Microsoft implementation examples

Secure SDLC
Dependency management
Secrets
CI/CD security
Code scanning
Workload identity
Software supply chain
Runtime monitoring

Security architecture

A cross-cutting capability, not a perimeter

Identity through Governance is designed as a stack that applies consistently across every domain — AI, applications, integration, data, the Azure platform and Microsoft 365 — rather than a box sitting at the edge of the architecture.

Applies across every domain below — not a perimeter at the edge

AI Applications Integration Data Azure Platform Microsoft 365

Identity

Device

Network

Application

Workload

Data

Monitoring

Response

Governance

Identity through Governance — a cross-cutting capability stack, applied consistently across AI, Applications, Integration, Data, the Azure Platform and Microsoft 365.

Data governance

Data has an architecture too

Data ownership, classification, discovery, access, protection, usage, retention and disposal — each stage has an accountable owner and a defensible answer.

Data ownership

Classification

Discovery & catalogue

Access

Protection

Usage

Retention

Disposal

Data ownership

Every data domain has a named business owner and steward accountable for its quality and use.

Data domains
Business data owners
Data stewards

Classification

Data is classified by sensitivity so downstream controls can scale to the risk it carries.

Sensitivity labels
Classification schema

Discovery & catalogue

What data exists, where it lives, and how it flows is catalogued — not assumed.

Data catalogue
Lineage

Access

Access follows least privilege and is governed the same way as any other entitlement.

Access governance
Entitlement review

Protection

Sensitivity drives protection — encryption, labelling and loss-prevention controls.

Information protection
Data loss prevention

Usage

How data is used — including by AI systems — is governed by policy, not left implicit.

Data sharing
Acceptable use
Privacy

Retention

Data is kept only as long as a business or regulatory reason requires.

Retention schedules
Records management

Disposal

End-of-life data is disposed of defensibly, with an auditable record that it happened.

Defensible disposal
Auditability

Microsoft implementation

Requirement first, product second

The model runs one direction only: business requirement defines the governance capability and control needed, which is then implemented with a Microsoft technology — never the reverse.

Business requirement	Governance capability	Control	Technology
Know what sensitive data exists and where it lives	Data discovery & catalogue	Automated scanning, classification and lineage tracking	Microsoft Purview Data Map & Data Catalogue
Apply consistent sensitivity labelling across content	Classification & information protection	Sensitivity labels applied at creation and enforced on use	Microsoft Purview Information Protection
Prevent regulated data leaving an approved boundary	Data loss prevention	Policy-based content inspection, blocking and alerting	Microsoft Purview DLP
Keep data only as long as required, then dispose of it	Retention & records management	Retention labels, disposition review and defensible deletion	Microsoft Purview Data Lifecycle Management / Records Management
Ensure only authorised people and systems can access data	Identity & access governance	Conditional, least-privilege access with periodic certification	Microsoft Entra ID
Analyse data at scale without losing governance controls	Governed analytics & data platform	Access, lineage and classification carried into the data platform	Microsoft Fabric / Azure data services (where appropriate)
Detect and respond to anomalous data access or exfiltration	Security monitoring	Correlated detection across identity, data and endpoint signals	Microsoft Defender / Sentinel

AI + data governance

AI cannot be governed without data governance

Before any AI system is trusted with enterprise data, the organisation needs to know what data exists, who owns it, its classification and sensitivity, who can access it, where it's stored, whether AI may consume it, and what retention and audit requirements apply.

User

Requests a task or answer

Identity

Authenticated and scoped to least privilege

AI application

Copilot, custom agent or Azure OpenAI app

Guardrail / policy

Checks classification, sensitivity and entitlement

Retrieval / MCP / tool

Allow-listed retrieval or tool invocation only

Authorised data

Only data the user is entitled to is returned

Audit + monitoring

Every step logged for traceability and review

See how this connects to AI Adoption