Don’t Just Watch What Employees Share. Watch What Agents Find.

In the past year, AI has moved from chatbots to autonomous agents that can retrieve information, make decisions and take action. That shift creates a new data risk beyond employee oversharing: agents may access more information than they should or act on data that is not appropriate, necessary or governed. The next challenge is ensuring autonomous agents can only discover, use and act on information within clear enterprise controls.

Organizations are still building the maturity needed to close that gap. According to research from the Ponemon Institute, only 41% of enterprises have AI-specific data privacy policies in place.

But autonomous agents do not create governance problems out of thin air; they expose and accelerate the ones already inside the enterprise. They act freely within the information environment they are given, which means outdated permissions, poorly classified data and inconsistent retention policies can quickly become AI security risks. If an agent can find sensitive customer data, confidential business records or obsolete information, it can also summarize it, route it or act on it.

That makes data hygiene practices that have long supported privacy and compliance the control layer for secure and trustworthy agentic AI.

Agentic AI Amplifies Existing Data Problems

Historically, issues like poorly classified information, improper data governance, excessive permissions and inconsistent access controls create operational inefficiencies and security risks. With agentic AI, these problems become even more consequential.

Unlike traditional AI assistants that respond only to user prompts, autonomous agents can retrieve and read documents, compile information, and generate outputs without human oversight and at lightning speeds.

Without proper data security in place, autonomous agents can turn unmanaged information – like outdated records, sensitive customer data or PII – into enterprise risk. Agentic AI will not create chaotic environments, but it will operate at the speed and scale of the information environment its controls allow it in. Proper data governance and controls is now essential to AI adoption, and therefore innovation.

Clean Data, Controlled Agents

Agentic AI should be approached as an information readiness challenge. Security controls must be in place, but that does not account for the entire problem.

Organizations must first understand and have clear oversight over what information is stored, where it’s stored, and which agents are allowed to access it. Security leaders should ensure classification of both structured and unstructured data across the enterprise to effectively govern AI access, enforce policies and ensure responsible use of enterprise data.

Just as modern privacy programs begin with data discovery, effective AI governance must begin with understanding and managing the underlying information environment.

Four Data Hygiene Practices for the Agentic AI Era

Organizations looking to reduce oversharing risk without slowing innovation should focus on implementing proven data hygiene principles into the control layer of agentic AI environments.

1. Discover and Classify Sensitive Information

Agents need context to make informed decisions within a given information environment. Data classification provides that context by identifying sensitive information and communicating how it should be handled. Maintaining visibility over both structured and unstructured data across the enterprise is critical to managing data privacy properly.

Without classification and metadata, agents cannot distinguish between public information, regulated customer data and proprietary intellectual property. In order to minimize oversharing, proper classification provides visibility across systems and agentic workflows.

2. Minimize Data Before Agents Access It

An effective and simple way to reduce data privacy risks is to reduce agent exposure to unnecessary data.

As organizations accumulate more data, security leaders should evaluate whether certain information should be retained at all, or if it is obsolete. Over time, dormant files and redundant content can collect and require unnecessary governance controls and compliance concerns. The more data an organization has, the more data is at risk — for both human users and AI systems.

Data minimization practices help identify and eliminate trivial information while ensuring retained data remains aligned with business needs and regulatory requirements. Obsolete data, once properly disposed of, cannot be overshared or exposed by a rogue AI agent.

3. Apply Lifecycle Management and Retention Controls

Data lifecycle management allows organizations to set up retention and disposition schedules for data that is collected and stored. For organizations looking to expand privacy and security programs, this is a necessary step. Putting automated processes in place to identify and dispose of unnecessary data greatly minimizes security and compliance risks while improving the quality of the data AI systems rely on.

An agent retrieving outdated information can be just as problematic as an employee accessing the wrong information. Lifecycle management helps ensure that agents operate on current, business-approved information rather than historical clutter.

4. Extend Governance Policies to AI Agents

Once organizations establish strong information governance practices, they can bake them into the foundation of their systems. Any controls enforced on human employees should also extend to agentic AI systems. Proper governance will make clear what information an agent is allowed to access, what actions they can perform with that data, and how teams can monitor agentic workflows for threats.

Better Governance Creates Better AI

The goal is not to restrict innovation. Organizations should aim to balance governance with efficiency by ensuring agents operate within clearly defined policies and access boundaries. Information governance is often viewed primarily as a risk management procedure, but in reality, the same practices that reduce oversharing risk can also improve agentic AI performance.

Well-governed information environments provide agents with higher-quality data, improving the relevance, accuracy and trustworthiness of AI outputs. Strong governance controls also support visibility, accountability and user confidence in AI systems.

Access Is the New Oversharing

Oversharing risk is more than what an employee enters into a prompt. Risk now depends on what autonomous systems can find, use and act on. And these agents move far faster than any team of humans can.

Data hygiene is the control layer that leads to better AI: faster insights, more trustworthy outputs and stronger business outcomes.

About the author: Greg Clark is Senior Director, Product Management for OpenText. His team’s focus is to deliver analytics-driven applications that deliver sensitive data insights and remediation capabilities for key data security, data privacy and protection challenges including data discovery, risk assessment, data access governance, data protection and preservation.

Greg has been a practitioner in Enterprise Information Management space for the past 20 years. Holding strategic roles in Product Management and Product Marketing at OpenText, CA, Autonomy, HP, Hewlett Packard Enterprise, Micro Focus and now OpenText Cybersecurity, Greg has overseen industry leading products across Data Security, Enterprise Content Management, Archiving, Compliance, and eDiscovery markets.

 

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