Imagine an AI agent responsible for processing vendor payments. It receives an invoice, verifies the details, checks the amount, and completes the transaction. The vendor gets paid, records are updated, and the task is marked as successful.
There's just one problem. The payment required human approval, and the agent skipped this step. Still, the money went to the right account, the invoice was legitimate, and nothing in the final result suggests that anything went wrong.
(Illustration: Shutterstock)
So why is this a problem? Because a critical safeguard was bypassed. If the payment had been fraudulent, the company might not have discovered it until the money was gone.
An AI agent doesn't have to become malicious, do something out of its intended scope, or fail at its assigned task to put an organization at risk. It can complete its task while taking dangerous actions along the way. And sometimes, the problem isn't what the agent did, but rather what it didn’t do.
When the result tells only half the story
Knowing that an AI agent completed its task isn’t the same as knowing it did so safely.
AI agents can access company systems, retrieve sensitive information, execute commands, modify records, and interact with external applications, even without any human intervention.
Consider a coding agent tasked with fixing a software issue. It identifies the problem, updates the code, and passes all the tests. From the developer's perspective, mission accomplished. But what if, during this process, the agent also sent secret credentials to an external service? The software issue may be resolved, while the company now faces a potential security incident.
Or consider an agent that generates an accurate customer report but skips a required step to remove sensitive personal information before sending it. In both cases, the agent reached its goal. The risk lies in how the agent got there.
Exposed customer information, compromised intellectual property, unintended changes to production systems, and financial transactions that bypass required controls can lead to substantial financial, operational, and reputational damage.
Why autonomous AI is harder to predict
Traditional automated workflows follow a deterministic approach. If a payment requires approval, the workflow can be designed to stop until approval is received. Pretty simple.
Autonomous AI introduces a different operating model. Agents can behave non-deterministically, taking different paths to achieve the same goal. An agent may select different tools, access different information or change its approach depending on its context.
This flexibility is precisely what makes AI agents so useful, but it also entails new and complex security challenges.
An agent may have legitimate permission to read financial records and communicate with external services. Each action, considered independently, may be authorized. But using these permissions to transfer confidential financial information outside the organization is a different matter.
The danger isn't necessarily in an individual action. It's how the action fits into the entire agentic process. Security teams need to adjust to the new reality where successful outcomes may include unauthorized actions, sensitive data exposure, or risky behavior.
New risks require a different approach to security
Autonomous AI follows a process: interpreting a goal, gathering information, making decisions, and taking actions across different systems. Security risks can emerge at any stage.
An agent may misunderstand its objective, use sensitive information inappropriately, or execute an unauthorized action through a connected tool. It may also skip a required step while still achieving the intended result.
This is why monitoring individual actions or evaluating just the final outcome isn't enough. Security teams need to understand the agent's original objective, the context in which it operates, and how its actions connect throughout the entire process.
This requires a different approach to security. Process-level visibility makes it possible to reconstruct the full sequence of activities and identify where an agent's behavior deviates from its intended purpose or required procedures.
By combining this visibility with policies that consider both the agent's intent and the context of its actions, security systems can detect risky behavior and enforce controls in real time – before potential damage occurs.
Giving AI autonomy without losing control
Restricting AI agents to the point where they lose their autonomy pretty much defeats their purpose. However, giving them greater freedom without the ability to monitor and control their behavior exposes businesses to risks they might not even know exist.
The challenge is to establish clear boundaries without limiting what makes autonomous AI valuable. This requires visibility into the entire agentic process, understanding what agents are authorized to do, tracking their behavior against their boundaries, identifying skipped safeguards, and stopping risky actions in real time.
Organizations that can maintain this level of control will be better positioned to adopt AI safely and remain competitive in the autonomous AI era.
- The author is the Co-Founder & CEO of Ovalix, a company providing a unified AI security platform that helps enterprises discover, govern and protect AI activity across their entire AI ecosystem.