The Privacy Risks of Your Company’s AI Agent

The world has seen some pretty significant developments in artificial intelligence (AI) in the past few years. Agentic AI emerged more recently (late in 2024) and has been picking up traction in 2025. Like many of the AI developments, it promises to change the way we work. And like other AI developments, it comes with its own suite of risks that need to be managed to protect your company.  

In this post, we delve into what Agentic AI is, how it can help, and what risks it poses to your business, as well as how to reduce them.  

What is Agentic AI? 

Agentic AI refers to AI systems that are designed to achieve a specific goal, taking multiple steps autonomously to do so. This is different from traditional AI models that perform a single, pre-defined task – like answering a question or categorizing an image.  

An AI Agent is capable of reasoning, planning, executing and iterating on a series of actions – in other words, performing multi-step reasoning. Examples of this are asking a smart assistant to ‘turn on the lights and play music’ or using complex accounting AIs to reconcile an organization’s monthly books and flag transactions for human review. To achieve these tasks, the AI Agent must perform a series of actions without additional prompting from the human user.  

IBM has a really detailed post on what Agentic AI is that you can read for further information.  

How It May Change The Way You Work 

We’ll be frank, there’s a lot of hype around large-language-model-based AI systems – from Shopify’s CEO reportedly telling departments to prove AI can’t do the job before receiving approval for future hires to Microsoft recently revealing an AI system that outperforms human doctors at complex diagnoses.  

The reality is likely a little duller. Agentic AI is well-placed to perform routine and repetitive multi-step tasks, like:  

• Booking appointments and organizing calendars to help workers operate more efficiently.  

• Providing automated customer support, from initial query to troubleshooting issues, processing refunds, or scheduling service appointments.  

• Creating project timelines and dynamically adjusting schedules.  

• Gathering and analyzing data to generate executive reports.  

• Personalizing employee onboarding experiences and training schedules. 

• Maintaining real-time threat detection and 24/7 monitoring of networks, plus isolating compromised devices and preparing incident reports for human security teams.  

The Privacy Risks of Agentic AI 

Clearly, the appeal of automating common, rote tasks like certain accounting processes and project and calendar management is appealing. But there are some pretty significant privacy risks that are worth considering before you launch any Agentic AI infrastructure. 

Quick note that we’re only considering the privacy risks here. There are ethical, environmental, and other business risks you may also wish to consider before implementing Agentic AI in your business.  

Agentic AI will collect volumes of personal information, including location data  

Generally speaking, the access an AI Agent will need to be effective is incredibly broad. For project management, it will likely access your browser, calendar, email, potentially banking systems, messages, and location data (historic and ongoing). For customer service, it may need access to purchase and browsing history, support tickets, demographic information, and potentially credit card information or similar payment details to provide a refund. 

Given the volumes of information, it will collect and potentially retain for future purposes, it could make an attractive target for hackers. Moreover, it can also become a point of failure for causing inadvertent data breaches which we’ll cover in more detail below.  

Corporate data will likely be shared with the AI Agent 

Similar to the point above, it’s likely that you will share immense amounts of corporate information with an AI Agent deployed in your organization. From financial records to internal company performance documents, to strategic goals and plans, it’s highly likely that your team may feed the AI Agent confidential corporate information that you need to keep secret except from trusted individuals and organizations with a need-to-know in order to support your operations.  

This makes AI Agents  a potential point of failure for inadvertent disclosures. It also means that you’ll want to keep a careful eye on the AI Agent’s settings to make sure that the data isn’t being used for broader training purposes outside of your organization. 

Inadvertent disclosures and data breaches 

As we mentioned above, there’s a risk of inadvertent disclosures in AI outputs. What this means is that in practice, the AI Agent can inappropriately include data – including sensitive personal information or confidential corporate information – in its outputs.   

Some examples of this could be an AI Agent:  

• Sending sensitive personal information to an external analytics service via its log files,  

• Copying and pasting a company’s strategic documents into a third-party generative AI tool, or  

• Inadvertently disclosing private employee information to an unauthorized colleague. 

This would typically occur if an AI Agent lacks good reasoning or has been given overly broad permissions (like those mentioned above). These risks are magnified if there isn’t sufficient human oversight of the outputs of the Agentic AI before they are distributed – which can be more commonplace for internal uses, like employee onboarding.  

A single source of failure 

The broad access that an AI Agent has means that it can be a significant vulnerability. Additionally, if it’s hacked or otherwise experiences a security breach, the ripple effects can be broadly felt across many systems. It also means that it’s likely an attractive target for hackers, since they can gain access to a broad range of systems by breaching just one.  

Picture this: a hacker could trick an AI Agent managing a company’s financial accounts into making a series of transfers across multiple bank accounts. Because the agent is a trusted authority, its actions would be executed without raising red flags until it’s too late. 

From a security perspective, this means that Agentic AI systems should be carefully protected, consistently monitored, and fail-safe technical measures should be implemented to ensure data cannot be easily exfiltrated or unusual financial transfers easily made. 

Right to data deletion may be a challenge 

Finally, one practical issue that may come up with Agentic AI is that data deletion can be challenging. While the exact rules may differ by region, most jurisdictions offer individuals some right to deletion of their personal data. This means that if someone you have collected data about asks for that to be deleted, you may need to delete most or all of it.  

This is tricky in the AI Agent context, since it’s not always clear how the system is using data. It may have duplicated the data into multiple datasets, or used that data in a new dataset, complicating efforts to fully identify and delete all instances. Agentic AI also is very sophisticated at learning from datasets, which means a person’s information may have been transformed into a statistical pattern or aggregated data trend. Deleting these may involve the AI Agent ‘unlearning’ something, which can be technically challenging. But, if the person is able to be re-identified from that data, then it could be a compliance issue.  

Steps to Reduce Risk Stemming from Agentic AI 

We’re going to broadly introduce some ways to reduce the privacy risks associated with Agentic AI here but note that this approach may not be sufficient for your needs. Depending on your precise use case and the sensitivity of the data you collect, you may need to introduce more comprehensive protections in your company.  

Practice data minimization  

Data minimization involves collecting only the minimum amount of personal data necessary to achieve a specific purpose. In today’s privacy landscape, this can be a compliance and risk management superpower.  

Learn more about data minimization.  

Prioritize transparency 

Companies should look for AI Agents that prioritize transparency. In practice, this might look like:  

• Explainable AI (XAI) Tools: Developers should use XAI tools to help interpret the decisions of their AI models. This allows auditors and compliance officers to understand the data points and logic that influenced a decision. 

• Audit Trails: Every action an AI agent takes should be logged and auditable. This provides a clear record of what data was accessed, when it was used, and why, enabling post-incident analysis and compliance checks. 

• User Dashboards: Users should be provided with simple, clear dashboards that show what data the AI Agent has access to and how it is being used. This empowers users to revoke permissions and provides greater control. 

Implement adequate access controls 

Security really should be a top priority for companies using AI Agents, given the volume of sensitive personal and corporate information it may access. One key tactic is to introduce dynamic access controls, so the AI Agent only accesses the data it needs to complete a task, as opposed to having broad access all the time.  

Zero-trust architecture is also a good idea here, which means creating technical infrastructure where requests for access or to complete certain tasks must be verified and authenticated, even if it comes from an internal system.  

Update your policies to reflect the AI Agent’s access to data 

You should update both your internal and external privacy notice and process documents to reflect your company’s use of the Agentic AI. Your privacy notice should indicate that data from your customers, employees, and other stakeholders may be shared with the AI Agent, and it should respect the wishes of those who opt out. 

Similarly, your internal policy documents and processes should identify the AI agents your company has vetted and approved for business use, along with their appropriate use cases. These documents should also identify what information (or categories of information) can (or conversely, should not) be shared with the AI Agent.  

If you’re planning on introducing Agentic AI in your company and you need help managing the privacy risks, reach out. Our privacy attorneys are available to work with you.  

• Privacy