AI agents are expanding their boundaries by not just accessing crucial information within the organization but outside it as well. For the second time in less than a year, Amazon has blocked another company’s AI agent from shopping on behalf of its customers, flagging privacy concerns. The apprehension extends to businesses other than e-commerce too. HR systems, healthcare portals, financial accounts, and even calendars and inboxes are being accessed by AI agents, notetakers, and chatbots, among others.

Privacy laws were largely built around people and organizations exchanging personal data. Agentic AI is inserting an autonomous delegate into the middle, and the rules of that relationship are far from settled. Therefore, let’s understand the privacy concerns that businesses might face with AI agents and how privacy-aware Agentic AI governance can help.

Privacy Rules and AI Actors

As I’ve explained earlier as well, the most alarming part about AI agents is their ease of use even to non-technical users. In Amazon’s case, for instance, a customer may have explicitly authorized an AI agent to access their account or review their information, but they have limited understanding of what sensitive data it might access and even expose. Even the organization holding that customer’s data may have no relationship with the agent, no visibility into what it is doing, and no opportunity to determine what information it can access or where that information goes. Here are some privacy-related risks for Agentic AI:

• PII Access: AI agents may autonomously read inboxes, attachments, contacts, calendars, CRM records, or device data to complete a task. They can also combine otherwise ordinary information like location, purchases, browsing activity, employment history, and public posts to infer health, religion, political opinions, sexual orientation, or other sensitive characteristics. This can expose not only the user’s PII but information about employees, customers, and other third parties who never directly interacted with the agent.
• Data Minimization: An agent asked to accomplish an objective may decide for itself which information could be useful. A customer-service agent, for example, could access purchase history, support tickets, CRM notes, emails, and other databases even when only a fraction is necessary to resolve the request.
• Purpose Limitation: A CRM agent instructed to “retain this customer” could independently examine complaints, transaction history, correspondence, and other available information to determine what action to take. The organization therefore specifies the goal, while the agent may effectively determine the processing path, creating difficulty in defining specific processing purposes beforehand.
• Consent: Personal-assistant or shopping agents may dynamically decide that completing a task requires retrieving additional information or sharing it with another service. This makes it harder to establish meaningful consent beforehand when neither the individual nor organization necessarily knows every processing operation the agent will undertake.
• Automated Decision-Making: Recruiting, lending, insurance, housing, or employee-management agents could autonomously collect information, evaluate an individual, and initiate or influence a consequential decision. That can trigger privacy-law requirements surrounding automated decisions, transparency, contestability, and meaningful human intervention.
• Profiling: Sales, marketing, or personal-assistant agents can continuously combine behavior, communications, transactions, and preferences to determine what an individual is likely to want or do. Because agents can generate new personal information and inferences on a scale, an apparently simple task can evolve into extensive profiling.
• Third-Party Disclosure: Shopping, travel, scheduling, and procurement agents may send personal information to external services while completing tasks. The EDPS specifically notes that agents interacting with external services could disclose personal data to third parties whose separate processing practices the user may not know about.

Privacy Perimeter for AI Agents

Privacy-aware Agentic AI governance is necessary to place boundaries around the agent that define what data it can access, which systems it can enter, what purposes that data can be used for, what information it can disclose to third parties, and which actions require human approval. Here’s how businesses can prepare:

• Maintain an AI Inventory: Organizations need a continuously updated inventory of AI agents, including third-party agents, the systems they can access, the data available to them, their intended purposes, permissions, integrations, and owners. Agentic AI makes “what AI are we using?” insufficient; businesses also need to know what each agent is capable of doing and touching.
• Maintain an Audit Trail: Record what data agents accessed, systems they interacted with, actions they took, information they disclosed, approvals they requested, and decisions they influenced. When an agent dynamically determines how to accomplish a task, logs become critical for reconstructing what actually happened rather than what the agent was expected to do.
• Control Data Access: Apply least-privilege access so agents can reach only the personal information and systems necessary for their approved use cases. Permissions should be granular enough to prevent a narrow task from becoming an open door to inboxes, CRM records, employee files, or other sensitive repositories.
• Define Purpose Boundaries: Establish what an agent is authorized to accomplish and which processing activities are acceptable in pursuing that objective. An agent should not be free to repurpose accessible personal information simply because doing so could help achieve its goal.
• Govern Agent-to-Agent and Third-Party Disclosures: Identify when agents can communicate with external services, other agents, vendors, or platforms and control what information can leave the organization. Authorization to access data internally should not automatically become authorization to disclose it externally.
• Build Consent Into Agent Workflows: Determine where an agent’s actions exceed the permissions originally provided by an individual and require additional authorization. Agents should be capable of stopping and requesting consent rather than treating an initial instruction as unlimited permission for every subsequent processing activity.
• Enforce Data Minimization: Give agents contextual restrictions on what information they may retrieve and process for a particular task. Governance should prevent an agent from exploring every available data source merely because additional information might improve its chances of completing an objective.

Truyo helps organizations build privacy-aware governance around AI agents without treating Agentic AI as an isolated compliance problem. Truyo AI Governance provides continuous AI discovery and inventory, use-case and vendor risk assessments, and AI agent scanning and assessment to help establish what agents are permitted to access and do. Truyo’s established privacy compliance capabilities for consent and preference management, website tracking and signals and defensible records help organizations connect what an AI agent is doing with the privacy obligations surrounding the data it touches.

Boundaries for Autonomy

Amazon blocking another shopping agent is unlikely to be the last dispute of its kind. As AI agents become more capable businesses will increasingly find agents operating on both sides of their privacy perimeter. The challenge is to know which agent accessed what data, under whose authority, for what purpose, what it did with that information, and where the information went next. Existing privacy principles around consent, minimization, purpose limitation, disclosure, and accountability remain relevant, but Agentic AI makes enforcing them considerably more dynamic. As agents gain greater freedom to decide how work gets done, privacy-aware AI governance provides the visibility, controls, and evidence businesses need to ensure that autonomy does not quietly become unrestricted access.