In the Age of Agentic AI, Data Control Is Power

As companies rush to deploy agentic AI, a new consensus is taking shape around the data problem underneath the hype. More autonomous AI systems will raise the stakes for how data is created, governed, accessed and protected. Synthetic data needs clearer standards. Real-world data needs tighter minimization. And the systems tying it all together need a stronger foundation of trust, security and control.

Some of that data will be synthetic. Tech Policy Press argues that synthetic data is moving from a niche tool to a core input for AI systems, especially as developers run into limits on the supply of human-created data online. The article says this matters even more in the age of agentic AI, where autonomous systems can pull from many data sources, make decisions, use tools and create new outputs with little or no human review. In that setting, flawed synthetic data can do more than produce a bad answer. It can shape an agent’s reasoning, distort its actions and spread errors across connected systems.

According to the story, synthetic data can help protect privacy, fill gaps in scarce datasets and expand representation, but it also can hide bias, weaken traceability and make it hard to know whether an AI system is acting on sound information or on a manufactured version of reality.

Current legal and policy frameworks are not ready for that shift. The article points to limited guidance in places such as the EU AI Act, California’s new disclosure law for generative AI training data, U.K. statistical guidance and Singapore’s privacy work, but argues that these early steps do not fully address how synthetic data should be governed at scale. Its core recommendation is for clearer standards on how synthetic data is created, documented, tested and used. That includes disclosure of who generated it, what models and assumptions were used, where the limits or risks lie and how quality, fairness and privacy were assessed.

Tech Policy Press frames this as a trust issue. As agentic AI systems take on more consequential tasks, the article argues that organizations will need far better rules for judging whether the data feeding those systems reflects the real world closely enough to support reliable decisions.

Data Protection

All this data needs to be protected. The IAPP argues that data minimization needs to move to the center of agentic AI design because these systems pull information from many places, keep context over time and can act on that information without constant human review. That creates a higher risk that agents will collect or reuse more data than they actually need for a task.

In an article released last week, the IAPP says companies should start with a narrow purpose, then decide which exact data fields an agent truly needs to complete that job. In practice, that means asking whether an agent needs full records or only a limited signal, score or summary. The article’s core point is that better agentic systems will depend on tighter limits around what data they can access, combine and retain.

The IAPP also focuses on the operational side of data control. It says organizations should set clear rules for what agents can see, which outside services they can call, when human oversight is required and how quickly data access should expire after a task is complete. The article also highlights cross-border data flows as a major data issue, since agentic systems often rely on outside cloud tools and third-party APIs that may move information across jurisdictions.

Its recommendation is to keep identifying data local when possible and share only the minimum needed externally, such as a token, a confirmation or an aggregate summary. Framed this way, the IAPP presents data minimization as both a privacy obligation and a practical design discipline for making agentic AI safer and easier to govern.

Building a Foundation

If the IAPP piece argued that agentic AI needs tighter limits on what data systems can access, SiliconANGLE’s Oracle story makes the next point in that chain: companies also need a data foundation they can trust. In the article, SiliconANGLE says Oracle is making the case that the database is becoming the control center for agentic AI because that is where trust, accuracy, security and reliability have to be enforced.

Oracle executive Juan Loaiza argues that fast AI-generated work is not enough on its own. The bigger issue is whether enterprises can trust what those systems produce and whether the data underneath them is sound enough to support real decisions.

SiliconANGLE says Oracle’s answer is to bring more data types and agent memory into one converged database engine rather than spread them across separate systems. The company argues that this setup can help agents work from a shared, current view of information instead of relying on disconnected stores that can drift out of date. The article also highlights security as a central part of Oracle’s pitch.

As AI agents move closer to the data itself, Oracle says controls can no longer depend mainly on the application layer. Instead, access rules need to be enforced at the data tier so an agent acting for a user can see only the data that user is allowed to access. Framed through a data lens, SiliconANGLE presents Oracle’s strategy as an effort to make agentic AI more dependable by tightening the link between trusted data, shared context and governed access.

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