Agentic systems are evolving rapidly, but governance is struggling to keep pace. This gap creates a breeding ground for real risks that many organizations still underestimate.
Here are the seven key security risks to address before deploying your AI agents, based on what we're seeing in practice with our clients and in recent events.
1. API Key Theft: How an Exposed Key Can Blow Up Your Budget
There is a lot of discussion around AI agents, but very little about one of the simplest and most costly risks. Every agent that uses a model (OpenAI, Anthropic, Gemini, etc.) relies on an API key. That key is essentially direct access to your spend. If someone gets hold of it, they can launch thousands of requests at your expense.
Documented incidents are increasing. Some companies suddenly discover bills in the thousands of dollars after leaving an API key exposed in a code repository or on their workstation. For example, in March 2026, a Mexican startup incurred $82,314 in charges within 48 hours after a Gemini API key was stolen, while their usual monthly usage was around $180.
This is only the beginning, as stolen API keys are already being resold on the dark web, much like banking credentials, except monetization here is immediate.
Basic practices to avoid this:
- Never expose an API key in code, communication tools like Slack, or documentation
- Use a secret manager such as AWS Secrets Manager or GCP Secret Manager
- Limit permissions to what is strictly necessary
- Set up usage alerts
2. The Agent That Drives Up Your Costs
AI agents are designed to be autonomous, fast, and efficient. But without proper guardrails, these qualities can quickly become a financial risk. Unlike traditional software, every interaction with an AI model incurs a cost. This cost is often low per request, but can become significant at scale.
A common scenario: a script or user sends repeated requests to your agent. Each request consumes tokens, and the agent processes them continuously. Even if the unit cost is low, uncontrolled usage can quickly result in thousands of dollars in charges within hours or days.
The risk is even higher because these systems are often deployed without clear financial controls. An agent does not know it costs money. It simply executes what it was designed to do.
That is why guardrails must be built in from the start.
How to avoid it:
- Define daily and monthly cost limits for each agent
- Estimate expected usage before deployment. For example: "This agent should not exceed $10 per day."
- Set up automatic alerts before critical thresholds are reached
- Include an automatic shutdown mechanism if limits are exceeded
In practice, managing an AI agent is not just about optimizing performance, but also about controlling costs. Without this discipline, even a well-designed project can quickly become an unexpected expense. In this context, mastering the use of a language model becomes a critical capability for any AI project.
3. Agents without Their Own Identity
When multiple agents share the same account or identity, you lose all traceability. If something goes wrong, it becomes impossible to know which agent performed which action. It is like having all your employees log in with the same credentials. In case of an incident, there is no audit trail.
How to avoid it:
- Assign a dedicated account to each agent, with its own credentials
- Apply the same security standards used for human accounts (password length, rotation, etc.)
- Ensure that every action can be traced back to a specific agent
- Document which agent has access to which systems and why
4. The More Permissions, the Higher the Risk
Take a concrete example: you deploy an agent to manage employee onboarding. It needs to create accounts in your HR systems. So far, everything is fine. But does it also have permission to delete all accounts? If yes, you have a problem.
Too often, agents are given overly broad permissions to speed up deployment, without taking the time to restrict access to the minimum required. Over time, this becomes a real ticking time bomb.
How to avoid it:
- Explicitly define allowed and forbidden actions for each agent
- Limit access to only the data and actions strictly necessary for its role
- Test how the agent behaves when attempting unauthorized actions. It should return an explicit refusal, never a silent failure
- Regularly review permissions, especially as the agent's scope evolves
5. Uncontrolled Autonomy: When an Agent Puts Your Data at Risk
The concept of a human-in-the-loop is not new, but it takes on a new dimension with agentic systems. Imagine an agent designed to capture leads at an event and create them in your CRM. At first, everything works well. It scans business cards, creates records, and assigns statuses.
Then, because it performs well, it is given more autonomy. It starts merging accounts it considers similar, updating existing opportunities, and sending personalized follow-up emails, all without human validation.
Within a few weeks, it could overwrite data on existing clients because two "J. Martin" entries appear identical, create duplicates at scale, send follow-up messages with the wrong tone or unapproved commitments, or re-engage prospects that were intentionally removed from the pipeline.
Without clear boundaries on what an agent can decide autonomously versus what requires human validation, control is quickly lost.
How to avoid it:
- Clearly define which decisions the agent can make independently
- Identify control points where human validation is required
- Set up monitoring mechanisms to detect deviations from its intended role
- Document the expected process and regularly compare it to actual behavior
6. Biased Data Leads to Biased Outcomes
Poor-quality input data inevitably leads to poor decisions. This is the classic "garbage in, garbage out" principle, amplified by AI.
With AI agents, the risk increases. Biases in the data combine with biases in the models themselves, along with hallucinations and variability across sources.
An agent fed with unvalidated or biased data can produce outputs that seem convincing but are fundamentally wrong or even discriminatory.
How to avoid it:
- Validate and certify the data sources your agents rely on
- Identify data that contains personal information and ensure compliance with applicable laws (Law 25, GDPR, etc.)
- Regularly test outputs to detect bias and hallucinations
- Do not blindly trust outputs. Maintain human validation for critical decisions
Did you know that implementing supervisory agents is a highly effective and secure strategy?
7. Who Is Accountable When an AI Agent Causes an Incident?
Your recruitment agent makes an onboarding error. Your marketing agent sends the wrong message to the wrong segment. Your finance agent produces an inaccurate report. Who is responsible?
Without a clearly assigned owner for each agent, incidents can easily fall through the cracks. IT assumes it is the responsibility of the business teams using the agents. Business teams shift responsibility back to IT. Meanwhile, the issue persists.
How to avoid it:
- Assign a single accountable owner to each agent, not a department
- The owner can delegate technical management but remains responsible for the agent's actions and access rights
- Implement accountability mechanisms such as regular reporting, incident reviews, and access audits
- Document the chain of responsibility: who created the agent, who approved it, and who monitors it
Secure before you deploy, not after.
The most important lesson? Do not deploy agents first and secure them later. Organizations that take this approach are constantly playing catch-up, fixing issues that could have been prevented.
Before deploying any agent, go through this checklist:
- Does the agent have its own identity and credentials?
- Are its permissions limited to only what is strictly necessary?
- Are cost limits in place?
- Are API keys securely stored?
- Is there a clearly defined accountable owner?
- Are human validation points clearly defined?
- Are data sources validated and certified?
Agentic systems offer tremendous potential. But that potential only materializes within a clearly defined security framework. It is better to invest a few days in governance now than weeks in crisis management later.