The recent dispute between Amazon and Perplexity highlights AI agents as a new and emerging challenge in the AI push. AI-powered agents, capable of logging into a user’s account and carrying out sensitive transactions on their behalf, can lead to a bunch of security and privacy issues. Adding to that, the low visibility into these agents and their appeal for non-technical users is a perfect recipe for enterprises to brew up legal liability.

The inherent high risk of deploying AI agents in organizations need not outweigh their benefits. However, their autonomous behavior needs to be monitored for potential data and privacy threats. Let’s have a look at what risks AI agents pose for organizations and how they can deal with them.

The Governance Gap

AI agents have evolved from passive tools into autonomous actors capable of making decisions and executing tasks independently. However, their capabilities can easily go beyond copilot roles and lead to sensitive data breaches. Here are some of the inherent risks that they bring:

• Lack of visibility: Most organizations can’t confidently answer three basic questions: Which AI agents are deployed, what can they access, and what are they doing right now? Without a centralized view, “shadow agents” can operate unnoticed across teams and tools. That makes it hard to enforce policy, detect misuse, or respond quickly when something goes wrong. In practice, poor visibility turns every other risk.
• Unauthorized access: There’s a reason Amazon doesn’t want AI agents accessing their portal even if the user willingly allows them to. Once the agent has required access, it can pull data or trigger workflows that might be sensitive for the organization and may require higher-level clearance in any other scenario.
• Unclear accountability: When an AI agent takes a risky action, ownership gets blurry fast. Is the user responsible because they enabled it? Is the company responsible because it allowed deployment? This ambiguity slows incident response, complicates compliance reporting, and increases legal uncertainty.
• Hidden data exposure: AI agents need access to data to be useful, but that access is often broader than expected. They may touch customer records, internal documents, credentials, or third-party systems without clear oversight. If permissions are excessive or behavior is not monitored, agents can unintentionally leak sensitive data, move information across systems, or expose confidential content in prompts and outputs.

Visibility is the Foundation of Agent Governance

As AI agents become more autonomous and deeply integrated into enterprise workflows, governance too must evolve alongside their capabilities. Here are some steps businesses need to take:

• Automated tool for AI Agent Use Case Scanning: A tool like Truyo AI Agent Use Case Discovery helps maintain a live inventory of every agent in your environment, internal, third-party, and embedded in SaaS apps. This gives you immediate visibility into what exists, who owns it, and where it is deployed, eliminating “shadow agents” before they become security or compliance blind spots.
• Enforce access guardrails with least-privilege controls: Give each agent only the minimum permissions it needs, and restrict high-risk actions (like downloading sensitive data or initiating transactions) behind approval workflows. This reduces the chance of unauthorized access, even when users willingly connect agents to systems.
• Define accountability before deployment: Assign a clear owner for every agent (business owner + technical owner), document what it is allowed to do, and create escalation paths for incidents. When something goes wrong, teams can respond quickly because responsibility is already mapped, no finger-pointing, no delays.
• Continuously monitor agent behavior, not just setup: Track what agents actually do in real time: what data they access, what actions they trigger, and how often. Behavioral monitoring helps detect anomalies early, like unusual data pulls or off-hours activity so you can intervene before risk escalates.
• Classify and segment sensitive data access: Require agents to use scoped credentials, redact sensitive fields where possible, and block access to regulated datasets unless explicitly approved. This limits hidden data exposure and prevents agents from moving confidential information across systems unintentionally.
• Audit and review agents on a recurring schedule: Set a monthly or quarterly review to validate whether each agent is still needed, whether permissions are still appropriate, and whether behavior aligns with policy. Regular audits keep your controls current as agents evolve and new use cases appear.

Control Starts with Knowing Your Agents

AI agents are quickly becoming powerful actors inside enterprise environments, but without proper oversight, they can just as easily become sources of risk. As seen in emerging cases, the challenge isn’t just what agents can do, but whether organizations truly understand and control their actions. Avoiding these risks isn’t about slowing down adoption but about enabling it responsibly. With the right visibility, access controls, and continuous monitoring in place, enterprises can confidently harness the benefits of AI agents without exposing themselves to unnecessary legal and compliance challenges.