At CyberOne, we’ve helped organisations confront a harsh truth about AI implementation: existing data governance frameworks were never designed for machine-speed automation.
“AI doesn’t just use your data, it amplifies its weaknesses. Most organisations quickly realise that their existing data governance frameworks were built for human-scale decisions, not machine-speed automation.”
"The first reality check? In traditional environments, if a piece of sensitive data is mislabelled, buried in an unprotected folder or duplicated across departments, it might go unnoticed. But plug that data into an AI model and suddenly it’s accessible, inferable and sharable, at scale.”
— Luke Elston, Microsoft Practice Leader at CyberOne
When AI Becomes a Data Amplifier
At CyberOne, we recently supported a financial services company implementing generative AI to automate its customer service. Historical support tickets and internal documents were used for training the model.
Those databases contained unstructured emails with personal and financial information, including account numbers, complaints and scanned document images.
Without proper data classification, the AI model learned from everything. Worse, it began generating responses that referenced private customer information.
The incident created a breach of possible GDPR exposure. The senior management made official apologies to affected clients. Extensive efforts were spent on information reclassification and retraining of algorithms.
However, there’s a caveat. When installing Microsoft Purview to scan and organise all information sources automatically, things went in the opposite direction. The model’s accuracy improved. Response times accelerated without compromising privacy. Internal teams gained trust in AI outputs.
Governance as Performance Multiplier
Most people hear “data governance” and think restriction. Red tape. Slowing innovation to stay compliant.
The reality is opposite. Proper governance unlocks better AI performance.
When you apply sensitivity labels and content classification through Microsoft Purview, your AI trains on verified, curated, business-aligned content. You get sharper, context-aware answers instead of vague generalisations.
Retention labels and data lifecycle policies prevent poor-quality or legacy data from contaminating AI logic. You guide models to trusted, up-to-date sources and this directly improves the accuracy and reliability of categorisation.
With role-based access and label-based permissions, more users across the business can safely interact with AI tools without exposing sensitive content.
You give each department the freedom to use AI confidently, while keeping full visibility and control at the centre. That’s how you unlock the power of AI without opening the door to risk.
5 Must-Haves for AI Governance That Works
Based on what we’ve seen in real deployments and where the market is heading, here are the essentials no responsible AI strategy should skip:
- Know Your Data Before You Touch AI
Before training a model or feeding it a single prompt, you need to understand your data thoroughly. What do you have? Where does it live? Who can see it? And how sensitive is it?
This isn’t a box-ticking exercise; it’s the foundation of trust. Microsoft Purview makes this far easier by automatically scanning and classifying your data across Microsoft 365, Azure and even hybrid environments. You get a live map of your data estate, so nothing slips through the cracks.
- Policy-First AI Usage Guidelines
Define AI tool permissions, data type restrictions, user access levels and output appropriateness before launching pilots. This prevents reactive scrambling when issues emerge.
- Workflow-Embedded Controls
Governance needs to work where work is done, in SharePoint, Teams and business applications, not as an afterthought. Data loss prevention and sensitivity labels must not disrupt but rather be seamless.
- Cross-Functional Governance Teams
Include legal, security, data owners and business leaders in AI policy lifecycle management. This ensures governance serves business objectives while managing generalisations,
- Ongoing Monitoring and Adjustment.
AI usage patterns evolve rapidly. Real-time telemetry and policy adjustment capabilities are essential for maintaining effective governance at scale.
The Proactive Governance Framework
“Most organisations treat AI governance like brakes on a fast car, only engaging them when they’re skidding towards a wall. But done right, proactive governance isn’t a brake, it’s the traction control that lets you accelerate safely.”
— Luke Elston, Microsoft Practice Leader at CyberOne
Start with a data map before building models. Microsoft Purview automatically discovers, classifies and labels data across Microsoft 365, Azure and beyond. This gives you the foundation for secure AI use from day one.
Set AI usage policies before the first pilot. Define what AI tools are permitted, what data types are allowed, who can access systems and what output is appropriate for different contexts.
Embed governance into workflows where work happens: SharePoint, Teams, Power Platform, business applications. Apply sensitivity labels and data loss prevention directly in the flow of work.
Create cross-functional AI governance groups including legal, security, data owners and business leaders. This group owns the AI policy lifecycle.
“Train your teams to see governance not as a blocker, but as the reason they can trust and scale AI safely. The conversation should shift from ‘can organisations ‘how do we use AI responsibly, confidently and at speed?’”
— Luke Elston, Microsoft Practice Leader at CyberOne
Retrofitting Without Breaking Momentum
Many organisations find themselves knee-deep in AI pilots across departments with zero governance consistency.
The key is retrofitting without wrecking momentum.
At CyberOne, we always start by mapping what’s already happening, including the AI tools in use, the data being accessed, and which departments are experimenting with what. Microsoft Purview, Defender for Cloud Apps and Entra ID insights surface shadow AI activity and ungoverned access.
Prioritise high-risk areas first. Sensitive data in AI training, outputs touching customers or regulators and compliance-regulated datasets.
Apply sensitivity labels and access policies on top of existing systems without asking teams to move data or stop using current tools. Restricted data can’t be used in AI prompts because role-based access limits who can generate or see AI content.
Build governance by design into existing tools. DLP policies in Teams and SharePoint. Approval flows for model training data are categorised based on data type and user role.
“Retrofitting AI governance is entirely possible. And with the right tools like Microsoft Purview and the right partner like CyberOne, it can be done quickly, pragmatically and without drama. Governance becomes your multiplier, not your handbrake.”
— Luke Elston, Microsoft Practice Leader at CyberOne
The Trust at Scale Challenge
Current text-based generative AI governance challenges will look simple compared to what’s coming.
Multimodal AI, which combines text, images, voice, video and code natively, significantly expands the risk surface. Data lineage, consent and context tracking become essential when AI can read contracts, draft emails and generate video explanations from the same data pool.
Autonomous agents will book meetings, trigger workflows, place orders and file requests without human review. You’ll need governance covering authorisation, delegation and reversibility for AI actions.
Models learning continuously through reinforcement learning require real-time monitoring of drift and bias.
“Your policy can’t just sit in a SharePoint site; it needs to live inside the AI pipeline itself.”
— Luke Elston, Microsoft Practice Leader at CyberOne
This represents a fundamental shift from documentation-based compliance to instrumented governance.
Regulatory enforcement is accelerating. Organisations’ AI Act ( Source: Eur-Lex) and UK guidance frameworks are no longer theoretical. Companies must demonstrate how decisions were made, which data trained models and who approved usage in specific scenarios.
Policy as Code Reality
Governance is becoming part of AI architecture itself: policy-as-code and instrumented governance operating in real time.
Consider a legal department using Microsoft Copilot to draft contracts. All templates and case files in SharePoint carry sensitivity labels. Copilot automatically restricts access to content not labelled “Approved for AI use.”
DLP policies monitor input prompts in real time. Attempts to use sensitive personal data get blocked and logged before reaching the model. Copilot-generated drafts inherit the highest sensitivity label of sources used. They trigger approval workflows before external sharing.
Every interaction is logged in the central audit dashboards, where governance teams can query by user, document, data label or AI output type. Conditional access disables AI features from personal devices or when users are flagged as high-risk.
Governance controls what data AI sees, how it interprets content, how outputs are labelled and who can act on them. Automatically, at scale.
Beyond Microsoft Boundaries
Most environments are hybrid by design: third-party SaaS platforms, legacy systems, multi-cloud sprawl.
Governance must be boundaryless and enforceable everywhere data flows.
Microsoft Purview provides centralised classification and labelling. Once data is properly classified, those labels travel regardless of destination. Even to Google Workspace, Dropbox, Salesforce or third-party AI chatbots.
Defender for Cloud Apps gives API-level control across hundreds of non-Microsoft services. You can discover unsanctioned AI tools, block uploads containing sensitive data and implement authorisation controls.
Label-aware DLP policies are data-specific, not platform-specific. Rules follow content across any environment.
For truly rogue environments, governance enforces at the browser and endpoint level. Prevent data copying into prompts, stop downloads from unapproved AI systems and alert on unmanaged AI activity.
Microsoft Sentinel pulls everything into unified governance observability, providing cross-platform audit trails, custom alerts and analytics on usage patterns.
“Microsoft is your control plane, but your governance doesn’t stop there. With the right instrumentation, Purview for classification, Defender for control, Sentinel for oversight. You can govern AI and data risk across any vendor, any cloud, any tool.”
— Luke Elston, Microsoft Practice Leader at CyberOne
The Performance-Led Future
The statistics tell the story. Security teams report 67% concern about AI tools exposing sensitive information (Source: Microsoft.com Work Trend Index). Business-critical data shows 16% oversharing rates (Tech Guru IT) with 802,000 files at risk per organisation (Securitytoday.com)
Yet commercial entities are rushing to adopt governance solutions, with Microsoft Purview usage growing 400% since launch (Microsoft.com)
The market recognises this truth. As Gartner predicts, by 2026, organisations implementing AI governance frameworks will experience 40% fewer AI-related security incidents. AI scales trust or scales risk.
Organisations getting governance right early move fastest with confidence as AI evolves. They treat governance as a growth enabler, not a compliance drag.
The future belongs to those building smarter systems of accountability and control. Microsoft Purview provides the instrumentation to govern AI across any vendor, any cloud, any tool.
“In the world we’re heading into, trust doesn’t stop at your stack; it travels with your data. And if you can’t enforce policy everywhere your AI operates, you’re not governing. You’re hoping.”
— Luke Elston, Microsoft Practice Leader at CyberOne
Ready to operationalise AI with trust at scale?