Why are AI agents a new security risk?
A chatbot can only say something wrong. An agent can do something wrong — send an email, change a record, issue a refund or share data — because it is connected to real systems. And unlike ordinary software, its behaviour is steered by language, including language from emails, documents and web pages that an attacker may control.
What are the main AI agent security risks?
| Risk | What it looks like |
|---|---|
| Prompt injection | Hidden instructions in an email, web page or document tell the agent to ignore its rules or leak data. |
| Excessive permissions | The agent can reach far more systems or data than its job needs, so one mistake has a large impact. |
| Data leakage | Sensitive information ends up in replies, logs or a third-party service. |
| Unsafe tools and connectors | An untrusted plugin or MCP server gives the agent harmful actions or bad data. |
| Wrong actions | The agent misunderstands a request and acts on it without a check. |
| No audit trail | Nobody can tell what the agent did, or why. |
Which controls keep AI agents safe?
- Least privilege — separate credentials for the agent, scoped to exactly the tools, records and actions its job needs; read-only wherever possible.
- Human-in-the-loop — approval for payments, deletions, external messages and anything above a set value.
- Untrusted input stays data — content from emails, files, web pages and tool results is never treated as instructions; sensitive actions are allowed only from the user’s own request.
- Limits — caps on amounts, rate limits and an allow-list of recipients and domains.
- Secrets management — keys stored in a vault and used by the tools, never placed in prompts.
- Logging and monitoring — every input, decision and action recorded, with alerts for unusual behaviour.
- Testing — attack scenarios such as prompt injection and data-extraction attempts tested before launch and after every change.
- A kill switch — the ability to pause the agent or revoke its access immediately.
What governance do companies need for AI agents?
Keep an inventory of every agent, its owner, the systems it can reach and what it may do on its own. Review that access regularly, approve new tools and connectors before use, and align the setup with recognised guidance such as the NIST AI Risk Management Framework and the OWASP Top 10 for LLM applications. For larger organisations, see AI agent development for enterprises.
How is security handled when we build an agent?
At Agentic AI Tech Hub we agree how data will be accessed, stored and processed before work starts, give the agent access only to what the project needs, and include functional and security testing before launch, followed by close monitoring after go-live. See how we work.
Frequently asked questions
Can prompt injection be fully prevented?
Not completely today. That is why the strongest defence is limiting what an agent can do: least-privilege access, approvals for high-impact actions and treating external content as data.
Should an AI agent have its own account?
Yes. A dedicated identity with its own scoped permissions makes access easy to limit, monitor and revoke, and keeps the agent’s actions separate in audit logs.
Is it safe to connect an AI agent to customer data?
It can be, with access limited to the records the task needs, encryption, logging, agreed data handling and approvals for sensitive actions.