OneLake is quietly becoming the trust layer for Data and AI

When Microsoft first introduced Fabric, much of the conversation focused on consolidation. The story was about bringing together data engineering, data warehousing, business intelligence and analytics into a single SaaS platform. OneLake played an important role within that narrative by providing a single storage layer that could be shared across workloads, reducing duplication and simplifying how data moved through the platform.

The announcements emerging from FabCon Barcelona suggest that Microsoft now has a much broader ambition for OneLake. While it remains the storage foundation for Fabric, many of the latest investments have surprisingly little to do with storage. Instead, they focus on governance, security, discovery, operational management and AI enablement. Viewed individually, features such as Governance Policies, Governance Insights, Domains, Secure and Catalog integration might appear to be incremental enhancements. Viewed collectively, they reveal a platform that is evolving beyond data storage and towards something much more strategic.

What stands out is that Microsoft appears to be tackling a different set of problems than those it faced when Fabric launched. Early adopters needed a platform capable of building data products and analytical solutions. Today's enterprise customers increasingly need a platform capable of operating hundreds or thousands of those assets while maintaining visibility, consistency and trust. The challenge is no longer simply creating a lakehouse, warehouse or report. The challenge is understanding who owns it, whether it can be trusted, how it should be governed and whether it can safely be used by both people and AI systems.

OneLake the operational centre of Fabric

One of the clearest signals from the latest announcements is the growing importance of the OneLake Catalog. Historically, data catalogues have been associated with discovery. They help users search for data, understand metadata and locate information assets across an organisation. What Microsoft is building increasingly goes beyond those traditional expectations.

The introduction of governance dashboards, governance policies, security administration and domain management within the catalog points towards a broader role. These capabilities are not helping users store data. They are helping organisations manage Fabric itself. The Govern experience provides visibility across the estate, Governance Policies introduce mechanisms for maintaining standards at scale, Domains establish accountability structures and the Secure experience provides oversight of access and permissions. These are operational capabilities rather than storage capabilities.

For organisations expanding their use of Fabric, this distinction matters. The technical challenge of creating analytical workloads is often relatively straightforward. The more difficult challenge is ensuring those workloads remain manageable as adoption grows. Platform teams need visibility. Governance teams need confidence. Administrators need control. The latest announcements suggest that Microsoft increasingly sees OneLake as the place where these concerns are brought together. This is perhaps one of the most significant shifts occurring within Fabric today. OneLake is no longer just where data resides. It is becoming the place where organisations understand and manage their data estate.

OneLake is becoming the discovery layer for trusted data

A second theme running through the announcements is trust. Most organisations do not have a shortage of data. If anything, they have the opposite problem. As data estates grow, users find it increasingly difficult to determine which assets are authoritative, which can be reused and which should inform business decisions. The challenge shifts from finding data to finding the right data.

Many of the catalog enhancements announced at FabCon can be viewed through this lens. Governance recommendations, endorsements, improved discovery experiences and governance insights all contribute towards helping users identify data that is trustworthy and reusable. Although these features appear distinct on the surface, they are addressing a common problem that affects almost every organisation pursuing data-driven transformation.

Trust has always been one of the more difficult aspects of governance to operationalise. Policies and standards can be documented, but users ultimately make decisions based on confidence. If they cannot easily determine whether data is owned, maintained, endorsed and understood, they will either avoid using it altogether or create their own alternatives. Neither outcome is desirable.

What makes the OneLake Catalog increasingly interesting is that it appears to be becoming the mechanism through which trust can be communicated. Ownership, endorsement, governance status, recommendations and business context are gradually being brought together into a single experience. Rather than simply helping users locate assets, the catalog is beginning to help users determine whether those assets should be used in the first place.

Consider OneLake as the Foundation for AI

The announcement that I find most revealing is not Governance Policies or the Govern experience. It is the decision to bring catalog discovery directly into Excel and Microsoft Foundry. At first glance this may appear to be a relatively modest enhancement. After all, users can already discover assets within Fabric itself. However, the significance becomes clearer when viewed from the perspective of adoption. Traditional data catalogues behave as destinations. Users must know they exist, remember to visit them and actively search for information. Microsoft appears to be pursuing a different approach. Instead of asking users to come to the catalog, the catalog is being embedded into the places where they already work.

For business users, that means trusted data can be discovered directly from Excel. For AI developers, it means information about governed organisational data can be surfaced within Foundry. In both cases, governance and discovery become part of the workflow rather than a separate activity.

This development is particularly important because AI systems face many of the same challenges as human users. An agent needs trusted information, ownership information, context and some means of understanding whether a dataset is authoritative or whether a conflicting source exists elsewhere. The role of the catalog therefore becomes increasingly important as organisations move beyond analytics and into AI-driven solutions.

Seen through this lens, the recent announcements are not simply governance enhancements. They are part of a wider effort to ensure that data can be safely discovered, understood and consumed by both people and AI systems.

The Bigger Picture

The easiest way to interpret the latest Fabric announcements is as a collection of new governance features. That explanation is not wrong, but it feels incomplete. A more interesting interpretation is that Microsoft is redefining the role of OneLake within the architecture. Governance, security, discovery and AI enablement are all being drawn closer to the storage layer and increasingly delivered through common experiences. The result is that OneLake is starting to look less like a data lake and more like the operational and trust layer that sits beneath the wider Fabric platform.

Whether this vision succeeds remains to be seen. However, the direction of travel is becoming increasingly clear. As organisations invest more heavily in Fabric, Microsoft's focus is shifting from helping customers build solutions to helping them govern, manage and trust those solutions at scale. This may not generate the same excitement as the latest AI capability, but it is precisely the sort of foundational investment that determines whether enterprise adoption can be sustained over the long term.

Reading

Build, deploy, and govern Microsoft Fabric at scale

FabCon and SQLCon 2026 in Barcelona: Building the data foundation for Microsoft Copilot and agents

Bringing governed analytics into the flow of work: Fabric Analytics at FabCon Europe 2026

What’s new across Microsoft SQL at SQLCon/FabCon Europe 2026

Microsoft Fabric at Scale: Technical Announcements for Administrators, Engineers and Platform Teams

It is the European Microsoft Fabric + SQL Community ConThe ference 28 Sep – 01 Oct 2026 in Barcelona. There are several Fabric specific announcements .  Arun Ulag’s blog FabCon and SQLCon 2026 in Barcelona: Building the data foundation for Microsoft Copilot and Agents contains a full list of announcements.

At FabCon, Microsoft's Build, Deploy and Govern Microsoft Fabric at Scale session focused less on end-user analytics and more on the practical realities of operating Fabric as an enterprise platform. The announcements were aimed squarely at the people responsible for platform engineering, DevOps, governance, security, and operational management. According to Microsoft, the themes of the session were CI/CD, agentic development, cost and resource management, observability, security, and governance, all designed to help organizations operate Fabric estates at scale without sacrificing control or compliance. Rather than discussing data products or business facing features, these announcements reflect Fabric's continued evolution into a mature enterprise platform that can support large-scale deployment, operational oversight, and governed self-service.

CI/CD Becomes a First-Class Experience. 

One of the strongest themes was Microsoft's continued investment in DevOps capabilities for Fabric.  Early Fabric adopters often found themselves balancing rapid innovation with the need for release controls, environment promotion, and deployment consistency. As Fabric workloads expanded beyond Power BI into data engineering, data science, real-time intelligence and databases, these requirements became significantly more important.

Microsoft's latest investments continue to strengthen CI/CD experiences, making it easier to treat Fabric assets as deployable platform components rather than isolated artefacts. This aligns Fabric more closely with modern software engineering practices where changes are versioned, tested and promoted through controlled deployment pipelines. For enterprise platform teams, this is particularly important because governance becomes easier when deployment processes are automated and repeatable. Manual configuration drift remains one of the most common causes of operational complexity in large data estates.

Why it matters:

Increased deployment consistency

Reduced risk of environment drift

Better support for enterprise release management

Stronger alignment between data engineering and software engineering teams

Agentic Development Arrives in Fabric

Another major direction highlighted by Microsoft was agentic development. The Fabric platform is increasingly embracing AI-assisted development experiences that help users build solutions faster while still operating within governed environments. Rather than simply generating code snippets, Microsoft's vision is for AI-powered assistants that can understand platform context, automate repetitive development tasks, and improve developer productivity.

This reflects a wider industry trend where platform engineering teams are looking to accelerate delivery without continually increasing headcount. AI tooling is becoming part of the development lifecycle itself, reducing administrative burden while helping teams navigate increasingly complex environments. The challenge for many organisations will not be whether they use AI-assisted development, but how they ensure these capabilities operate within security, compliance, and governance controls.

Why it matters:

Faster development cycles

Reduced platform administration overhead

Increased developer productivity

Better scaling of engineering teams

Observability Becomes a Critical Capability

As Fabric estates grow, monitoring becomes significantly more important. Microsoft highlighted continued investment in observability and operational visibility, helping administrators understand how capacities are performing, where resources are being consumed, and how workloads are behaving.

This is particularly relevant because many organizations have now moved beyond small pilot deployments. They are running production workloads, serving large user communities and supporting critical business processes. At that scale, platform teams need visibility into reliability, performance bottlenecks and resource consumption trends. Recent roadmap announcements further reinforce this direction with enhanced capacity monitoring, utilization insights, throttling visibility, scaling controls and capacity health indicators becoming part of the platform experience.

Why it matters:

Faster issue identification

Improved platform reliability

Better capacity planning

Reduced operational risk

Improved Cost and Capacity Management

Managing Fabric costs has become a major focus for many organisations. As adoption increases, conversations often move beyond functionality and towards sustainability. Platform owners need confidence that they can scale usage without unexpected capacity challenges or performance degradation.

Microsoft's announcements around cost and resource management indicate continued investment in helping organisations understand and control consumption. This includes better visibility into resource utilisation and more sophisticated capacity management capabilities. This is particularly important for organisations adopting a hub-and-spoke operating model where multiple business units share platform resources.

Why it matters:

Greater financial transparency

More predictable platform operations

Better chargeback and showback models

Improved utilisation of Fabric capacities

Security and Governance Continue to Mature

Security and governance were also central themes throughout the announcements. Microsoft continues to position Fabric as an enterprise-ready platform that integrates governance, compliance, auditing, security controls and administrative oversight directly into the platform. Microsoft's governance documentation highlights capabilities such as audit, workspace governance, tenant controls, monitoring, lineage, information protection and broader integration with Microsoft Purview.

The significance of these announcements is not simply that governance features exist. It is that governance is increasingly becoming operationalised as part of day-to-day platform management rather than remaining a separate compliance exercise. For platform teams, this means security and governance controls can be embedded alongside deployment, monitoring and operational management processes.

Why it matters:

Stronger enterprise compliance

Improved security oversight

Reduced governance debt

Better support for AI-ready data estates

The Bigger Picture

Taken individually, many of these announcements appear incremental. Together, however, they reveal Microsoft's strategic direction for Fabric.  Fabric is no longer just a collection of analytics workloads. Microsoft is steadily building the supporting capabilities required to operate a large-scale enterprise data platform. The focus on CI/CD, observability, cost management, security, governance and AI-assisted development demonstrates a platform increasingly designed for enterprise-scale operation rather than departmental adoption alone.  For administrators, platform engineers and governance teams, this is arguably the most important message from the session. The newest capabilities are less about creating another report or building another pipeline. They are about enabling organisations to run Fabric confidently at scale while maintaining reliability, security and control. In many ways, these were the announcements that move Fabric from a powerful analytics platform towards a mature operating platform for enterprise data, AI and analytics.

Further Reading

• Microsoft Fabric Governance Documentation
• Governance and Compliance in Microsoft Fabric

FabCon Fabric Governance Announcements

Microsoft's recent update, Build, Deploy and Govern Microsoft Fabric at Scale, contained several announcements focusing on a series of governance enhancements that signal a major shift in the role of OneLake within the Fabric ecosystem at FabCon. 

Historically, OneLake has been positioned as the unified data lake for Microsoft Fabric. However, the latest announcements make it clear that Microsoft's ambitions extend far beyond storage. OneLake is rapidly evolving into the place where organisations discover, govern, secure and manage their data estate at scale. This is particularly important as organisations increasingly invest in AI. Successful AI initiatives rely on trusted, well-understood and properly governed data. The announcements focus on making that trusted data easier to find, control and manage.

Announcement 1: Centralising Governance in the OneLake Catalog

The most significant announcement is Microsoft's continued expansion of the OneLake Catalog as the central hub for governance activities across Fabric. Rather than forcing administrators and data owners to move between multiple portals, governance information is being surfaced directly inside the OneLake Catalog.

The dashboard provides:

• Estate-wide governance visibility
• Capacity and workspace metrics
• Domain awareness
• Recommended governance actions
• Links to governance tooling and administration

Data governance frequently fails due to fragmentation. Data owners work in one tool, administrators work in another, and governance teams work somewhere else entirely. Microsoft's vision is that governance becomes part of the normal Fabric experience rather than a separate activity. For organisations with growing Fabric estates, a central governance experience reduces complexity and provides a single place to understand whether data is trusted, managed and reusable.

Announcement 2: Governance Insights

Alongside the governance dashboard, Microsoft has introduced governance insights directly within the OneLake Catalog. These insights provide visibility into the overall governance state of the Fabric environment.

These insights help answer questions such as:

• How much of our data estate is governed?
• Which domains are most active?
• Where are governance gaps emerging?
• Are governance investments improving over time?

Many organisations know they have governance challenges but struggle to quantify them. Governance insights transform governance from an abstract concept into measurable information that can be reported and improved.

Announcement 3: Governance Recommendations

One of the most useful additions is Microsoft's introduction of governance recommendations. Rather than simply showing governance metrics, Fabric now highlights specific actions administrators and data owners can take to improve governance maturity. Examples include assigning domains, improving metadata quality, increasing endorsement coverage and applying sensitivity labels.

Typical examples include:

• Increase sensitivity label coverage
• Certify trusted data assets
• Assign business domains
• Improve catalog metadata

Most governance programmes excel at identifying problems. Far fewer help organisations decide where to start. Recommendations provide practical guidance that helps teams focus effort where it will have the greatest governance impact.

Announcement 4: Governance Policies

Microsoft also announced governance policies for Fabric. Policies introduce an additional layer of governance automation by allowing organisations to define governance expectations and apply them consistently across their Fabric estate. Traditionally, governance standards often exist as documents, presentations and policies that rely on users following guidance. Policies shift governance closer to platform-enforced controls.

As Fabric estates grow, organisations need governance mechanisms that scale. Policies help reduce reliance on manual reviews and improve consistency across hundreds or thousands of assets.

Announcement 5: Domain-Based Governance

Another significant development is Microsoft's continued investment in Domains. Domains allow organisations to organise data according to business functions such as Finance, Human Resources, Operations, Sales or Clinical Services. Governance information can then be analysed and reported through these business-aligned structures

One of the biggest governance challenges is accountability. Domains allow governance ownership to sit closer to the people who understand the data and use it daily. Rather than governance being entirely driven from a central team, it becomes embedded within business functions.

Announcement 6: Secure Management in the Catalog

The final major governance announcement is the introduction of a dedicated Secure experience within the OneLake Catalog. 

This provides visibility into:

• Workspace roles
• OneLake security roles
• Access permissions
• Security administration

Security and governance have traditionally been managed separately.

As organisations increasingly share data across teams and deploy AI solutions, this separation becomes increasingly difficult to maintain.

Data cannot be considered governed unless organisations can clearly explain:

• Who owns the data
• Who can access it
• Why they have access

Bringing security and governance together creates a stronger foundation for trust and compliance.

Announcement 7: Bring Catalog Discovery into Excel and Microsoft Foundry

One of the more significant announcements is the expansion of OneLake Catalog beyond Fabric itself. Microsoft is embedding catalog discovery directly into products such as Excel and Microsoft Foundry. Rather than expecting users to open Fabric to search for trusted data, data discovery becomes available within the tools where people are already working. 

Data catalogues have existed for years, but they often suffer from a common problem. People only use them when they deliberately go looking for them. Many business users spend most of their day in Excel. Increasingly, AI developers and data scientists are spending time in Microsoft Foundry building AI solutions. These users may never open Fabric directly, yet they still need access to trusted and governed data. By bringing catalog discovery into these experiences, Microsoft is reducing the distance between users and governed data.

This announcement addresses one of the biggest challenges in governance: adoption. A data catalogue only creates value when people use it. Embedding catalog discovery into familiar tools increases the likelihood that users will:

• Reuse existing trusted assets.
• Find certified data products.
• Understand ownership.
• Discover business context.
• Avoid creating duplicate datasets.

For organisations investing in AI, the implications are even more significant. AI developers using Microsoft Foundry can discover trusted organisational data directly from their development environment rather than relying on tribal knowledge or manually maintained data inventories. In simple terms, Microsoft is moving governance closer to the point of consumption.

What This Means for OneLake

When viewed together, these announcements reveal Microsoft's broader strategy. OneLake is no longer simply the storage layer for Fabric. It is becoming the platform through which organisations:

• Discover data
• Understand data
• Govern data
• Secure data
• Share data
• Prepare data for AI

For organisations pursuing AI initiatives, this evolution is particularly important. AI systems need trusted data sources, clear ownership, strong security controls and confidence in data quality. The latest Fabric announcements move OneLake significantly closer to becoming the operational foundation for that trusted data estate. The message from Microsoft is seems is to use OneLake to discover, govern, secure and trust your data at enterprise scale.

Further Reading

OneLake catalog overview - Microsoft Fabric | Microsoft Learn

OneLake catalog Govern experience

Catalog - Search - REST API (Core) | Microsoft Learn

Govern and manage your Fabric data with the OneLake catalog - Microsoft Fabric | Microsoft Learn

OneLake for Microsoft Foundry - Microsoft Fabric | Microsoft Learn

Policies in Fabric (Preview)

Microsoft Fabric governance and administration documentation

Big Data London emerging insights

It was great to be at BigDataLDN this week and see the changing landscape. 40% of all sessions had AI in the title. This is a significant shift compared to pre 2023 programmes, where AI titled sessions were typically less than 15%.

The 2026 landscape highlighted a number of shifts.

AI is no longer a track, it is the spine of the conference embedded across every discipline.

Governance and trust are now centre stage. Multiple sessions explicitly focused on trusted AI foundations, AI governance maturity, scaling AI safely, data governance as the prerequisite for AI and observability for AI systems.

Agentic AI is emerging as a major theme. This is the first year agentic AI had a dedicated theatre signalling a shift from the previous experimentation phase to operationalisation.

AI plus organisational transformation is the new battleground asking questions like how do we scale AI safely,  modernise legacy environments,  demonstrate measurable business value, and improve governance without slowing innovation.  This is a change from asking which model to use towards to how do we run an AI enabled organisation. 

Data governance is finally being treated as strategic and it was the strongest governance representation BigDataLDN has ever had.

AI for societal impact is gaining traction with talks now covering climate change and humanitarian impact broadening AI use beyond commercial use cases.

The keynotes were AI centric which created a conference theme of AI first.

I think the BigDataLDN conference has quietly repositioned itself an AI governance, AI strategy, AI engineering and AI transformation conference. 

AI governance seems like the new cloud migration in that it requires a change of mindset. The agenda showed a pattern where

• AI adoption is assumed. 
• AI scaling is the challenge. 
• Governance is the bottleneck. 
• Data foundations are the dependency. 
• Operating models are the differentiator

The conference was packed with people and exhibitors and many people spent their time queueing to get into rooms to get to listen to talks.

 

Responsible AI in 2026: Governance Moves from Principle to Practice

Microsoft's latest article, Responsible AI in 2026: How We Are Adapting for What's Ahead, highlights something many of us working in governance have been seeing for some time: AI governance is no longer a future concern. It is becoming an operational necessity. 

As AI capabilities continue to accelerate, particularly with the rise of agentic AI, the governance challenge is changing. Traditional governance approaches were largely focused on data, systems, and applications. Increasingly, organisations must also govern autonomous actions, agent interactions, tool permissions, and dynamic decision-making processes. Microsoft describes this as a move towards more adaptive governance, where controls evolve alongside the capabilities and risks of AI systems. 

What I found most interesting is that the article places relatively little emphasis on the models themselves and much more emphasis on governance, risk management, monitoring, and assurance. Microsoft explicitly states that model capability alone will not determine AI's impact. Success will depend on whether organisations can govern AI with the rigour and adaptability needed to earn trust. 

This mirrors a trend I am seeing across the market. Many organisations are still focused on AI adoption, Copilot deployments, and proof-of-concepts. However, the harder question is emerging quickly: how do we maintain visibility, accountability, and control once AI becomes embedded in day-to-day operations?

The answer is unlikely to be found in technology alone. Microsoft's report discusses governance frameworks, risk management processes, evaluation capabilities, training, standards, and industry collaboration. These are all governance disciplines rather than purely technical controls. 

For data governance professionals, this should sound familiar. The foundations that organisations have spent years developing around ownership, accountability, quality, security, and compliance are becoming even more important in an AI-enabled world. AI governance is not replacing data governance. It is extending it.

Perhaps the most significant message from the article is that responsible AI cannot be treated as a static policy document. Microsoft describes governance as a continuous lifecycle activity that must evolve as systems learn, interact, and operate in increasingly complex environments.  That is a valuable lesson for every organisation currently exploring AI. The conversation is no longer about whether governance matters. The conversation is about whether governance can keep pace with AI's rapid evolution.

As Microsoft's latest transparency report demonstrates, the organisations most likely to succeed with AI will not simply be those with access to the best technology. They will be the organisations that can combine innovation with trust, control, and effective governance.

References

https://blogs.microsoft.com/on-the-issues/2026/09/01/responsible-ai-in-2026-how-we-are-adapting-for-whats-ahead/

Governance must keep pace with AI and be embedded in every stage

Over the last few weeks, the conversation around artificial intelligence has taken an increasingly dramatic turn. Following Dario Amodei's essay, We Must Pace the Frontier, and widespread media coverage of warnings from researchers and technology leaders, discussions about AI have become dominated by questions of existential risk, cyber warfare, loss of control and the possibility that advanced systems could outpace human oversight. Amodei's central argument is that the rate of AI capability development may be accelerating faster than our ability to understand, govern and safely manage those capabilities, creating a situation where precaution needs to catch up with progress.

These AI fears made me think of pushing beyond design limits where Donald Campbell’s final attempt in 1967 on Coniston Water pushed Bluebird K7 past 300 mph far beyond its original design rating of 250 mph. This pushing technological boundaries to shatter another world record, demonstrated that accelerating past design limits without evolving the safety framework exposes fatal vulnerabilities.

While these concerns deserve serious consideration, I have been struck by how many of the proposed solutions focus on slowing AI itself. The assumption seems to be that if technology advances too quickly, the safest response is to reduce the speed of innovation until regulators, policymakers and society have time to react. However, I am not convinced that slowing AI addresses the underlying issue. The problem is not that artificial intelligence exists or that organisations are finding new ways to apply it. The problem is that governance continues to lag behind technological change, despite decades of evidence showing that this always creates unnecessary risk.

Every major technological shift follows a remarkably similar pattern. Organisations become excited by new capabilities, investment accelerates, adoption grows rapidly and governance is treated as something that can be addressed later. Eventually the consequences of that approach become visible, whether through security incidents, compliance failures, poorly understood risks or loss of trust. The discussion then turns towards regulation, controls and accountability. What is often forgotten is that governance could have been embedded from the beginning.

The current debate around AI increasingly focuses on the possibility that advanced systems may one day become difficult to control. Yet many organisations are already struggling with far more immediate challenges. They do not know who owns critical datasets. They cannot consistently identify authoritative information. They have limited visibility of the quality of the data entering analytical platforms. They have duplicated reports, conflicting definitions and inconsistent security controls. These are not theoretical future concerns. They are today's governance problems, and AI simply amplifies them.

This is one of the reasons I find the current distinction between data governance and AI governance increasingly key. AI governance is undoubtedly important, particularly as organisations begin deploying copilots, autonomous agents and decision-support systems. However, the majority of the risks associated with AI are ultimately rooted in issues that data governance has been trying to solve for years. Questions about ownership, accountability, transparency, lineage, quality, security and trust do not suddenly appear because an organisation deploys an AI model. Those questions already existed. AI merely exposes them more quickly and at greater scale.

Consider the current wave of Microsoft Copilot deployments taking place across both public and private sector organisations. There is understandable excitement about productivity gains and new ways of working, but Copilot does not create knowledge. It surfaces what already exists inside the organisation. The challenge is the state of the information environment that AI is consuming.

What concerns me most is that governance is still frequently discussed as if it were a specialist discipline owned by a single team. The reality is that the next generation of technology will make that approach increasingly difficult to sustain. As organisations move towards more autonomous forms of AI, governance decisions will need to be incorporated directly into project delivery, operational processes, architecture reviews, software development lifecycles and technology investment decisions. It will not be sufficient to maintain a separate governance workstream running alongside change initiatives. Governance will need to become a fundamental characteristic of how change is delivered.

This becomes particularly important when considering the rise of agentic AI. Much of today's governance discussion focuses on whether an AI model is accurate, fair or explainable. Those questions remain important, but autonomous systems introduce an entirely new set of concerns. Organisations will need to understand who is accountable for actions taken by an agent, what permissions it possesses, how its behaviour is monitored, when human intervention is required and how decisions are audited. These challenges cannot be resolved through model governance alone. They require broader governance frameworks that connect business ownership, risk management, security and information management.

For this reason, I believe the debate about whether we should slow AI down is asking the wrong question. The more important question is whether governance can evolve quickly enough to keep pace with innovation. History suggests that organisations are capable of managing significant technological change when appropriate governance structures are embedded from the outset. We have done this with financial controls, health and safety, privacy, cyber security and regulatory compliance. None of these disciplines emerged because organisations stopped innovating. They emerged because innovation required new forms of oversight and accountability.

If the concerns raised by Dario Amodei prove justified, then the answer is unlikely to be found solely through reducing the pace of technological development. The more sustainable response is to ensure that governance develops at the same speed as the technologies it is intended to support. Data governance, AI governance, security governance and risk management should not be viewed as separate initiatives competing with innovation. They should be recognised as the mechanisms that make innovation sustainable.

The future of AI will undoubtedly introduce challenges that we have not yet anticipated. However, organisations do not need to wait for hypothetical existential threats before they strengthen governance. The foundations are already well understood. Ownership, accountability, transparency, stewardship, good data quality, security and trust remain as relevant today as they were before the first large language model entered the public consciousness. The difference is that AI has transformed these disciplines from desirable good practice into essential business capabilities.

The organisations that succeed over the next decade will not necessarily be those that adopt AI first or deploy the greatest number of models. They will be the organisations that recognise governance as an enabler of innovation rather than a constraint upon it. In a world where AI is becoming embedded into every platform, every process and every decision, governance must become equally pervasive. The challenge is not slowing AI down. The challenge is ensuring that governance finally catches up.

References

We Must Pace the Frontier https://darioamodei.com/post/we-must-pace-the-frontier

The Guardian — “‘We must slow the pace’: CEO of Anthropic calls for an AI slowdown

https://www.theguardian.com/technology/2026/sep/12/we-must-slow-the-pace-ceo-of-anthropic-calls-for-an-ai-slowdown

TechRepublic — “Altman, Musk Back Amodei’s AI Warning: The Frontier May Be Moving Too Fast” https://www.techrepublic.com/article/news-amodei-altman-musk-slow-frontier-ai/

BBC Why are there concerns AI could threaten humanity, and how real are they? https://www.bbc.co.uk/news/articles/c790xvnzgnno

BBC AI 'kill switch' may need to be mandatory, Anthropic co-founder tells BBC https://www.bbc.co.uk/news/articles/cqgk5e2j0gg8o

BBC Anthropic researcher believes more than 10% chance AI 'could kill all humans' https://www.bbc.co.uk/news/articles/ckgwy1k42w4o

Microsoft Fabric Ontology: The missing layer between Data Governance and AI

The data industry has spent the last twenty years focused on one primary challenge: connecting data. We built and created increasingly sophisticated ways of moving information between systems and making it available for analytics. Many organisations today still struggle with a much simpler problem and that is clarity on terminology. Different department terms often mean different things when they use the same business terms.

A customer means one thing in CRM, another in finance and something slightly different again in marketing. Product definitions vary between commercial teams and operational systems. Employee records, supplier information, and assets frequently exist across multiple applications, each with its own interpretation and business rules.

Humans have become relatively adept at navigating these differences because they understand organisational context. AI does not understand this. As organisations increasingly adopt Copilot, AI agents and intelligent business applications, the challenge is no longer giving AI access to data but giving AI the meaning of terms.

This is where Microsoft Fabric Ontology, currently in preview as part of Fabric IQ, becomes particularly interesting. Microsoft describes Ontology as a machine understandable representation of enterprise vocabulary that defines business concepts through entity types, properties and relationships, creating a shared business context layer that can be used across teams, applications and AI agents. 

What makes this significant is not the technology itself but how important the need is for AI to have business understanding, just as much as it requires data access.

The problem we have been trying to solve for years

Anyone who has worked in data governance will recognise this challenge immediately. We have built business glossaries, data dictionaries, conceptual models and reference architectures in an attempt to create consistency across the organisation. Governance programmes have invested significant effort defining critical data elements, agreeing business terminology and establishing ownership for key information assets. The difficulty has always been turning those definitions into something operational. Many governance initiatives successfully define what a customer is, but those definitions often remain trapped in documents, spreadsheets or governance tools that sit separate from the systems actually using the data. The glossary becomes a reference point for people rather than an active component of the architecture. As a result, governance knowledge frequently exists in one location whilst operational data exists somewhere else. Both are valuable, but the connection between them is often weak.

Microsoft's vision for Ontology appears to be closing that gap. Rather than maintaining business definitions separately from data, Ontology allows organisations to define core business concepts and then bind those concepts directly to data residing within OneLake, Power BI semantic models, lakehouses and other Fabric data sources. The result is that the business definition and the physical data become connected through a common semantic layer. From a governance perspective, the business glossary stops being passive documentation and becomes a part of how information is understood and consumed throughout the platform.

Why AI changes everything

Inconsistencies in business terminology are often frustrating but manageable. Analysts learn the system nuances and data engineers write transformation logic to reconcile differences. Often reporting teams spend their time explaining why the numbers vary between departments.

AI fundamentally changes the scale of the problem. When an AI agent is asked a question such as Which customers are most at risk of churn? it needs more than access to customer records. It needs to understand what a customer is, which systems contain authoritative information, how related concepts connect to one another and which business rules should be applied during analysis. Without that context, even a highly capable model can produce inconsistent or misleading outcomes. 

Microsoft specifically highlights Ontology as a shared business context layer that can be consumed by Fabric agents and AI-driven workflows to support reasoning and actions across domains. Rather than asking questions against individual tables, users and AI agents can query business concepts that already carry organisational meaning.

As organisations move beyond simple AI assistants and towards agentic architectures where AI systems are expected to make decisions, execute processes and reason across multiple business domains having this tool is important. The quality of decisions will depend heavily on the quality of the organisational context provided to them.

More than another Semantic Model

Ontology is not simply another version of a semantic model. Semantic models primarily exist to simplify analytics and reporting. They provide a business friendly representation of data designed to support measures, calculations and reporting experiences.

Ontology aims to tackle a much broader challenge. It introduces concepts such as entity types, properties and relationships that represent how the organisation understands the world. Customer, Supplier, Product, Contract and Asset become business entities that exist independently of any particular source system. Relationships become explicit rather than buried inside data models and joins.

Microsoft also introduces a graph representation that allows relationships between entities to be stored and queried directly. In practical terms, this means understanding not just what something is, but how it connects to everything around it. Customers place orders. Suppliers provide products. Employees manage projects. Assets support services. These connections become part of the semantic model itself rather than logic recreated repeatedly by individual development teams. This kind of contextual understanding  for AI is enormously valuable because reasoning is often driven by relationships as much as by data values.

Is Ontology replacing Microsoft Purview?

One of the most common questions I have seen since the announcement is whether Ontology makes Microsoft Purview less relevant. Ontology and Purview address different layers of the same challenge.

Purview focuses primarily on understanding, governing and protecting information assets. It discovers data, provides lineage, manages classifications, supports compliance activities and enables organisations to establish trust in their information landscape.

Ontology focuses on meaning. 

Where Purview helps answer questions such as Where is this data?, Who owns it?, How sensitive is it? and Where did it come from?, Ontology helps answer questions such as What does this represent?, How does it relate to other business concepts? and How should AI reason about it?

The two capabilities compared.

Capability	Microsoft Fabric Ontology	Microsoft Purview
Primary objective	Create shared business meaning	Govern and manage enterprise data
Key focus	Business concepts and relationships	Data assets and metadata
Business glossary	Operational semantic layer	Governance glossary and terminology
AI support	Grounding and business reasoning	Trusted metadata and governance controls
Relationships	Business relationships between entities	Data lineage and technical relationships
Graph capabilities	Native graph-based business context	Metadata and lineage visualisation
Data discovery	Bound Fabric data sources	Enterprise-wide discovery
Classification	Limited focus	Core capability
Compliance	Not a primary objective	Core governance capability
Security and risk	Relies on platform controls	Governance, risk and compliance controls
Typical audience	AI teams, business architects, domain experts	Data governance, security and compliance teams

What this means for the Future of Governance

For me, the most significant aspect of Ontology is what it says about the future direction of governance. Historically, data governance was largely created for people. Policies were written for humans, glossaries were maintained for humans. with data standards interpreted by humans. Increasingly, we need governance artefacts that machines can understand directly. AI agents, Copilots and autonomous systems cannot read a governance policy and infer organisational meaning in the way people do. They need structured, machine readable context. They need agreed definitions and relationships. Also business vocabulary they can reason over consistently is required.

Ontology appears to be Microsoft's recognition that the next generation of governance must serve both humans and machines. As AI becomes embedded within business operations, organisations will increasingly discover that trusted data is only part of the equation. Equally important is ensuring that AI understands what that data actually means. Data governance professionals have argued for years that data without context has limited value. In the era of enterprise AI, that statement feels more true than ever. Trusted AI requires trusted data, but it also requires trusted meaning. Fabric Ontology is a significant step towards delivering that meaning at scale.

Reference

What is ontology (preview)?

Microsoft Tools for Making Data AI-Ready