APAC’s Hybrid Data Environments Complicate Security as Agentic AI Expands

As enterprises deploy autonomous AI across public clouds, private infrastructure and on-premises systems, maintaining consistent visibility, access controls and governance is becoming increasingly challenging.

Data security, governance and compliance are becoming major factors shaping how enterprises across Asia Pacific redesign their AI infrastructure. Cloudera’s Great AI Re-Architecture survey found that 46% of APAC organisations identified these concerns as the leading driver of AI infrastructure change. The challenge is becoming more complex as businesses move beyond conventional AI applications towards autonomous agents capable of accessing data, interacting with applications and executing tasks across enterprise systems.

Unlike traditional software applications, AI agents can initiate actions and interact with multiple tools with limited human intervention. When these systems operate across public clouds, private infrastructure and on-premises environments, maintaining consistent oversight becomes more difficult. Different environments may have separate identity systems, access policies and monitoring mechanisms, creating gaps in visibility over how agents interact with enterprise data.

The growing adoption of agentic AI is therefore raising a broader infrastructure question for APAC businesses: can existing cybersecurity frameworks maintain control over autonomous systems operating across increasingly distributed environments?

Hybrid infrastructure expands the security challenge

Enterprises across APAC increasingly rely on hybrid infrastructure to balance cloud scalability with control over sensitive data, regulatory requirements and existing technology investments. Financial institutions, healthcare providers and other data-intensive businesses may distribute workloads across public cloud platforms, private infrastructure and on-premises systems.

However, operating across these environments introduces complexity when organisations attempt to apply consistent security policies. An AI agent may retrieve information from an internal database, use an external model to process it and interact with another application to complete a task. Each connection introduces a point at which permissions, data access and activity need to be governed.

Remus Lim, Senior Vice President, Asia Pacific and Japan at Cloudera, said while conversing with AsiaTechDaily that the growing autonomy of AI systems requires organisations to reconsider how they approach cybersecurity.

“With organizations ramping up the adoption of agentic AI, cybersecurity must evolve to account for a new kind of actor — one that can process vast amounts of data, operate across multiple systems, execute actions at machine speed, and act independently. Limiting access controls alone is no longer enough. Organizations also need visibility into where agents can move, what data they can reach, and what actions they can take.”

The infrastructure challenge is particularly relevant across APAC, where businesses operate across multiple cloud providers, jurisdictions and regulatory environments. Data residency requirements and sector-specific regulations can influence where information is stored and processed, making a unified approach to security and governance more difficult to implement.

AI connectivity creates new pathways to enterprise data

The expansion of AI into existing business applications is adding another layer of complexity. Organisations are no longer managing only standalone AI tools used directly by employees. AI capabilities are increasingly embedded in enterprise applications and connected to internal data sources and external services.

Netskope’s October 2026 Threat Labs Report: Asia found that active AI users increased from 55% to 74% over the preceding year. It also found that 98% of employees used applications with embedded AI features, while regulated data accounted for 61% of AI-related data policy violations observed in the region.

The report also recorded a 398% increase in users interacting with remote Model Context Protocol (MCP) servers and a 259% increase in related events. MCP enables AI applications to connect with external and internal tools and data sources, expanding the ways agents can interact with enterprise systems.

These connections can improve operational efficiency, but they also create additional pathways through which sensitive information can move. Organisations must understand not only which AI applications are being used, but also what systems they connect to and what permissions those connections carry.

Lim said: “Across Asia Pacific, this challenge is heightened by increasingly distributed data and AI environments spanning public clouds, private infrastructure, and on-premises systems. Cloudera’s Great AI Re-Architecture survey found that 46% of APAC organizations cite data security, governance and compliance as the leading driver of AI infrastructure change. Organizations therefore need a consistent approach to securing agent activity across these environments. That means extending zero-trust principles beyond initial access to include continuous monitoring, clear accountability, tightly scoped permissions, and mechanisms that can detect and contain unexpected behavior.”

Security frameworks must account for autonomous behaviour

The gap between agent deployment and security readiness is becoming more apparent. SailPoint’s October 2026 Horizons of Identity Security report found that 79% of surveyed organisations were running AI agents in production, while only 2% had deployed identity security tools specifically designed to govern them. The report also found that 85% continued to rely on legacy identity tools that were not designed for agentic identities.

The findings highlight a challenge for security teams: authentication and access management alone may not provide sufficient control once an autonomous system begins interacting with enterprise applications.

Cybersecurity authorities are also examining this issue. At Singapore International Cyber Week in October 2026, the Cyber Security Agency of Singapore and Google convened policymakers, critical infrastructure leaders and AI security experts for a tabletop exercise on agentic AI security. Participants highlighted the limitations of relying solely on perimeter defences or manual human review, and discussed safeguards including continuous runtime monitoring, hardened execution environments, strict access controls and real-time guardrails for high-impact actions.

Singapore’s Monetary Authority of Singapore has also issued AI Risk Management Guidelines for financial institutions, covering AI inventories, lifecycle controls, cybersecurity, monitoring and third-party accountability. The guidelines will take effect in October 2027, with further consultation on agentic AI expected in 2027.

Together, these developments indicate a shift towards security models that monitor AI activity throughout its operation, rather than relying primarily on controls applied when a system first gains access.

Making agentic AI secure across enterprise environments

For APAC enterprises, securing agentic AI will require coordination between infrastructure, data governance and cybersecurity teams. Organisations need to establish clear ownership of AI agents, restrict permissions to the tasks they are authorised to perform and maintain visibility into their interactions with data and applications.

Lim said: “Cybersecurity Awareness Month should serve as a checkpoint for leaders to assess whether their security frameworks are keeping pace with autonomous AI systems. As adoption accelerates, enterprises will need the visibility and safeguards to understand how agents interact with their data and systems, and to intervene when those interactions move beyond their intended purpose.”

The ability to deploy AI across hybrid environments will remain important for enterprises balancing scalability, data control and regulatory obligations. However, expanding infrastructure without extending governance across every connected system could leave organisations with fragmented oversight of increasingly autonomous technologies.

As agentic AI becomes more deeply integrated into business operations, enterprise security will depend not only on protecting where data resides, but also on controlling how autonomous systems access, process and act on it. For organisations across APAC, maintaining that control across distributed infrastructure will be central to moving AI from experimentation into production with greater confidence.

Quick Takeaways

• The growing adoption of agentic AI is exposing security gaps as enterprises deploy autonomous systems across hybrid data environments.
• 46% of APAC organisations cite data security, governance and compliance as the leading drivers of AI infrastructure change, according to Cloudera’s survey.
• AI agents introduce risks beyond conventional access control, requiring visibility into the data they access, systems they interact with and actions they execute.
• Netskope found that regulated data accounted for 61% of AI-related data policy violations, highlighting the risks of AI interacting with sensitive enterprise information.
• A Singapore CSA and Google exercise highlighted the need for runtime monitoring, verifiable agent identities, strict access controls and human oversight.
• MAS’s AI Risk Management Guidelines reinforce the importance of AI inventories, lifecycle controls and accountability for AI-driven actions.
• Enterprises need security frameworks that extend zero-trust principles to autonomous agents, with continuous monitoring and safeguards across cloud, private and on-premises infrastructure.