Throughout this series, we’ve explored how security has become a strategic imperative for media companies rolling out AI at scale, from protecting IP against unauthorised third parties to architecting systems that preserve competitive control, as media companies navigate distribution partners that accumulate intelligence and infrastructure dependencies that create vulnerability. Yet within these challenges lie substantial opportunity. One of the areas a number of companies are now exploring is fine-tuning and building their own models with proprietary data.
This approach is already delivering competitive differentiation across the industry, as enterprises fine-tune models to their own data and context. For example, Youtube fine-tuned deep neural networks on platform-specific engagement data to power its recommendation system, processing billions of user interactions to surface content tailored to individual preferences. The capabilities enabled by this kind of fine-tuning create advantages that generic models cannot replicate, built on unique datasets spanning audience behaviour, content performance, and user engagement that are specific to the organisation.
For media organisations sitting on years of proprietary research, audience insights, and content intelligence, fine-tuning represents a significant opportunity: the ability to build AI capabilities that competitors are unable to easily match, while maintaining control over the data and intelligence that creates competitive advantage.
However, realising this opportunity requires understanding what fundamentally changes when proprietary data is used to train a model, and why that shift introduces architectural considerations most organisations haven’t yet needed to confront.
When data becomes behaviour
Understanding this shift begins with recognising what happens during the fine-tuning process itself. Fine-tuning a model on internal data alters not just how an organisation uses its data, but what that data becomes. Instead of residing in databases with familiar access controls, proprietary intelligence is converted into model behaviour, becoming patterns, relationships, predictions, and decision rules that may not have been explicitly programmed but emerged through training.
Transformation of this nature creates both value and complexity. The value lies in the model’s ability to generalise from proprietary intelligence in ways humans cannot, identifying patterns and making predictions that would be impossible to code explicitly. Alongside this significant value, complexity emerges because once intelligence becomes behaviour, it no longer exists in a structure that traditional security or governance frameworks were designed to protect.
This is where many organisations underestimate the scope of change. Fine-tuning is often imagined to simply be a discrete workflow step or technical feature, when in reality it introduces system-wide implications that impact how proprietary intelligence is governed, protected, and leveraged across an organisation.
The system-level shifts and considerations
Therefore, this transformation from data to behaviour requires organisations to reconsider core assumptions about how proprietary intelligence is managed, governed, and deployed.
- The shift from static data to dynamic behavioural IP
When proprietary data trains a model, the value no longer resides in tables or documents. It becomes encoded in how the model responds, through its predictions, recommendations, and outputs. This shift requires organisations to rethink fundamental assumptions: where does competitive value now sit? And equally, where does exposure exist? As models are updated or fine-tuned further, how does both that value and exposure evolve across versions?
Traditional approaches to protecting proprietary data focus on controlling access to the data itself. But when that data has been transformed into model behaviour, protecting value means understanding and controlling what the model reveals through its responses, not just who can access the training data. This isn’t theoretical: OpenAI’s 2025 investigation into whether DeepSeek used distillation techniques to systematically query its models illustrated how behavioural intelligence can be extracted through interaction patterns, even when training data remains inaccessable.
- The shift from governance at rest to governance in motion
Such a shift in where value resides necessitates a corresponding shift in how it is governed. Traditional data governance is built around static concepts: where data is stored, how it’s classified, who can access it, and how it moves between systems. But when proprietary intelligence is encoded in model behaviour, governance must account for fundamentally different dynamics.
Model-centric governance introduces dimensions that static frameworks weren’t designed to address: understanding how models arrive at outputs, tracking how intelligence evolves as models are retrained or updated, maintaining visibility into usage patterns that might indicate extraction attempts, and continuously monitoring what models reveal through their responses.
Organisations fine-tuning models need governance frameworks designed for intelligence that expresses itself through behaviour rather than residing in structured storage, using protocols that can adapt as models learn, change, and interact with users over time.
- From platform features to bespoke architecture
Beyond governance, organisations need to also consider how these systems must be architected for the specific context in which they operate. Generic platform settings and standard configurations cannot fully address the considerations that emerge when proprietary intelligence is encoded in a model. The appropriate architecture must account for the specific context in which fine-tuning occurs considering factors such as the sensitivity of the underlying data and how the model will be used, whether serving internal teams or external users. It should take into consideration the organisation’s risk tolerance, and the strategic importance of the intelligence being embedded.
This is why fine-tuning cannot be treated as a plug-and-play capability. Instead, it represents an architectural decision that must be tailored to the organisation’s competitive position, data sensitivity, and operational requirements. What works for one use case may be entirely inappropriate for another. For example a model serving internal analysts with aggregated insights has a completely distinct array of requirements to a customer-facing recommendation engine fine-tuned on viewing behaviour to surface personalised content. For this reason, the architectural approach must be designed around the specific combination of data value, exposure risk, and business context that defines each implementation.
Looking ahead
Fine-tuning AI models on proprietary data represents one of the most significant opportunities for media companies seeking differentiated capabilities and greater strategic control. But unlocking this value extends beyond data preparation and training pipelines. It requires recognising that fine-tuning is a systems change, one that affects governance, architecture, competitive strategy, and operational design in equal measure.
The organisations that realise the full advantage of this opportunity will be those that understand once proprietary intelligence converts to model behaviour, the architecture surrounding that behaviour becomes as critical as the data itself. This architecture determines both how models function today, and importantly whether organisations can protect competitive advantage, scale capabilities with confidence, and embed AI deeply and securely across the business as systems evolve.
As this capability matures, the organisations that invest early in purpose-built AI architecture, and recognise the specialised full-stack expertise required to to execute it securely, will be the ones positioned to extract lasting competitive value from proprietary data, ensuring proprietary intelligence remains a strategic asset rather than becoming a source of unmanaged exposure.
