Breaking free from data gravity: enabling scalable, compliant AI across Asia-Pacific

It's time to rethink the application delivery controller in a world where data brings its compute infrastructure with it

Partner content AI is no longer a pilot project; it's a board-level priority. From automated fraud detection and predictive logistics to large language models that reshape how internal knowledge is accessed, organizations across Asia Pacific are accelerating AI adoption. Yet a hard truth is emerging: Scaling AI is far more difficult than starting it. One of the biggest culprits? Data gravity.

As AI models become more advanced, the volume, velocity, and distribution of the data they depend on are becoming structural bottlenecks. The deeper challenge isn't just storing or accessing this data. It's moving and governing it, and scaling its use across hybrid infrastructure in a secure and compliant manner.

The strategic risk of data gravity

Data gravity occurs when large datasets attract compute and services to their location. It increases latency, cost, and rigidity, which AI amplifies. Training and inference workloads depend on high-volume unstructured data log files, user interactions, image repositories, cloud environments, edge devices, and telemetry streams scattered across on-premises systems.

In the Asia-Pacific region, sovereign cloud policies, fragmented regulatory regimes, and data residency mandates make it even harder to centralize this data. The result? Models are either bound to jurisdictions where data can legally reside, or forced to run across disjointed infrastructure with suboptimal performance and high compliance risk.

For enterprises scaling AI regionally, this becomes more than a technical issue. It's a question of how to balance control, compliance, and cost across an increasingly complex architecture.

Rethinking infrastructure with ACDs as AI enablers

To address these challenges, F5 has reimagined the role of the application delivery controller (ADC). No longer just a tool for traffic management, the modern ADC has become the intelligent control plane for AI data flow. It safeguards, accelerates, and optimizes the movement of data across clouds, edges, and colocation points.

Our Application Delivery and Security Platform acts as the connective tissue between AI data sources and compute environments. Whether it's inferencing close to the user at the edge, or routing high-throughput training traffic across hybrid environments, the platform provides:

• Low-latency, policy-enforced routing between distributed environments
• Dynamic traffic shaping for AI and LLM workloads
• Encryption and segmentation for sensitive data in transit
• Observability into AI data flows for compliance and governance

This is critical in Asia Pacific markets where organizations must operate AI under heterogeneous policy frameworks, balancing data locality, latency sensitivity, and infrastructure availability.

Beyond data flow: managing shadow AI and rogue workloads

As AI usage grows, so too does the risk of shadow AI, which is the unauthorized or unmanaged use of external models, APIs, and tools that expose sensitive data. Organizations increasingly need visibility into what models are being called, which data is being sent, and whether inference endpoints meet compliance standards.

With centralized policy control, the ADC can enforce allow/deny rules for AI traffic, inspect outbound API calls, and apply consistent encryption and telemetry requirements. This mitigates risk without impeding innovation.

In sectors like financial services and government, where auditable AI governance is non-negotiable, this level of control is a prerequisite for sustainable deployment.

Finops meets AI to tame the cost of innovation

Another growing concern is cost. While AI promises exponential productivity gains, the infrastructure cost of training, storing, and moving data can spiral quickly. Enterprises are now integrating FinOps disciplines into their AI strategies, treating data movement and compute usage as first-class budget concerns.

F5's platform helps address this through traffic optimization and workload placement, reducing unnecessary data egress, avoiding overprovisioning, and ensuring AI workloads execute in the most cost-efficient region or environment available. This becomes especially important in multi-cloud settings where inter-region transfer charges or underused GPUs can quietly derail ROI.

Architecting for scale in the real world

Scaling AI requires more than compute horsepower. It requires an architecture that supports:

• Distributed inference without compromising latency

• Real-time compliance enforcement across geographies

• Consistent observability into AI data flows

• Secure edge processing to the reduce backhaul of sensitive data

F5's AI reference architecture provides a pragmatic blueprint for this. It abstracts complexity across cloud and edge environments while aligning with zero-trust principles, scalable telemetry, and infrastructure agility.

Infrastructure is policy: enabling AI with confidence

In the end, enterprises across Asia Pacific need AI that is not just powerful, but governable, compliant, and economically viable. The infrastructure behind it must deliver more than connectivity. It must act as a policy engine, traffic optimizer, and security enforcer all at once.

That's what the F5 Application Delivery and Security Platform is built for. It helps businesses unlock the promise of AI, not in a lab but in the real world, where compliance matters, budgets face scrutiny, and customer trust is earned through operational discipline.

In the AI era, your ability to move, protect, and govern data at scale is what will define your enterprise advantage.

Contributed by F5.