Artificial intelligence (AI) agents are being deployed at pace across the Middle East as businesses face sustained pressure to produce stronger results. According to Confluent’s 2026 Data Streaming Report, 38% of organisations in both the UAE and Saudi Arabia are already running agentic AI solutions in production, placing the two markets among the global leaders in agentic AI deployment.
On the surface, AI agents promise to make life easier and simpler for workers. However, many of these agents access sensitive operational data, such as customer records, pricing logic or internal policies. This introduces significant risks, including data misuse, governance lapses and even widespread business disruption.
Just recently, an AI coding agent went rogue, deleting a software company’s entire production database in nine seconds.
Just over 66% of organisations in both the UAE and Saudi Arabia identify data infrastructure and quality as specific challenges for agentic AI deployment. They recognise that these issues must be addressed—but how can they scale governance as workers increasingly rely on an ever-growing list of AI agents to drive innovation?
Enterprises need to focus on governing AI models but cannot forget about data
Many organisations respond to the risks introduced by AI agents by deploying safeguards such as prompt filters, runtime monitoring and API-level controls within the agent itself. These controls are necessary, but they sit at the edge of the system, after data has already entered the decision flow.
AI agents depend on enterprise data to understand context, generate responses and act. When that data is clean, current and well governed, agents are more likely to produce useful outcomes. When it is inaccurate, stale or incomplete, the risk is not just a poor answer. The agent may amplify disruption. This is why governance cannot stop at the agent layer.
If bad data has already shaped the decision, downstream filtering can only do so much. Relying solely on agent-level controls is like placing a traffic officer inside every car on a highway to ensure safety – it is inefficient and a logistical nightmare.
Enterprises need to enforce governance across the entire data layer, including the data streams that agents rely on: where data comes from, how it changes, who can access it and how it can be reused.
How businesses can start governing data for agents more effectively
To do this, enterprises should start by looking at data quality. AI agents reflect the data they consume. If that data is incomplete, inconsistent or missing context, their outputs will carry the same weaknesses.
Organisations in the UAE and Saudi Arabia already face this challenge. Nearly three in four IT leaders in both markets report encountering at least three major barriers to AI adoption. The most commonly cited include insufficient infrastructure for real-time data processing; uncertainty around data lineage, timeliness and quality; and insufficient AI and data skills and expertise, according to Confluent’s research.
This requires businesses to break down silos, standardise data formats, validate inputs and make trusted data available as it changes. Platforms that can integrate data are crucial to building a strong foundation for agents.
Once enterprises have established a foundation of high-quality data, they should enhance data classification by “shifting left” with controls – deploying them closer to where data is created, processed and shared, rather than only after it reaches AI.
Enterprises should identify the core data streams that power AI use cases, such as transactions, customer events and operational signals, then apply access controls directly to those streams. Sensitive data should be classified before it enters AI workflows. Teams should also define clear rules for how data can be reused across systems, teams and agents.
When policies sit inside each individual tool, governance becomes fragmented. Governance becomes easier to scale consistently when policies are enforced through the data architecture.
Lastly, enterprises should strengthen observability to ensure agents are continuously governed as they act. Governance requires evidence, especially when agents act quickly across multiple systems. Businesses need clear records of where data came from, how it changed, which systems used it and what sequence of events led to an automated decision. This helps teams investigate incidents faster, support audits and build trust in AI-driven actions.
Modern data streaming platforms provide the foundation for an always-auditable approach to AI by helping IT and data teams connect, process, govern and share data as it moves across the organisation in real time. In both the UAE and Saudi Arabia, 95% of IT leaders believe data streaming platforms can accelerate AI adoption, while the same proportion expect data streaming to increase the impact of their AI investments.
When governance is built into the backbone of data movement, auditability scales naturally as AI agent adoption increases.
Agents will multiply and governance needs to scale with them
AI agents are evolving rapidly. Businesses across the UAE and Saudi Arabia are already putting advanced models to work. Many more are adopting multi-agent systems and highly sophisticated cross-agent automations that increase capabilities, complexity and risk.
The businesses that build governance across the entire data layer will be able to stay ahead of the competition, scale innovation and drive stronger business results – no matter how AI evolves next.
