What is AI automation?
AI automation combines workflow tools with language models. A standard automation moves data from one system to another; an AI step can read an email, work out what it is about, pull out the key details and draft a reply.
For example, an invoice workflow might read each supplier invoice from a shared mailbox, extract the amount and supplier details, match them to a purchase order in your accounting system and hold the entry until a staff member approves it.
How do we keep AI agents under control?
An AI agent is an automation that can decide which steps to take and which tools to use. That flexibility is useful, and it is also the main risk. If an agent holds broad access to your email, files or finance system, anyone who can influence what it reads may be able to influence what it does.
The OWASP Top 10 for LLM Applications (2025) names this risk “excessive agency”, alongside prompt injection and sensitive information disclosure, and the OWASP Top 10 for Agentic Applications 2026 covers agent-specific risks such as tool misuse. The secure-by-design controls in each build are there to address those risks. The one choice that varies by workflow is the model: we choose it for each AI step based on data sensitivity, using a model hosted in your own cloud environment, which you control, or a private model through our local AI service.
How do we secure a self-hosted n8n instance?
When a workflow runs on self-hosted n8n, your workflow data and credentials don’t have to pass through a third-party automation cloud. That makes the n8n instance itself a sensitive system, because it holds credentials to everything it connects. We harden it like one: it is patched promptly, kept off the public internet or placed behind single sign-on, and its credential encryption key is set, stored securely and backed up separately.
If your project is mostly about connecting systems, such as linking your CRM to your accounting software, without AI steps, our integrations and automation service covers it. Our AI security and governance service can add the policy and risk register around your automations.
Who tests an automation before it goes live?
Testing is led by our security team, the same people who carry out our penetration testing. Beyond hiding instructions in test emails and documents, we try inputs designed to leak data and confirm that approval steps can’t be skipped. We keep the test cases so they can be rerun whenever the workflow changes.
What results can you expect from AI automation?
Results depend on the task, the quality of your data and how much of the work needs human judgement. We don’t promise a figure in advance. Instead, we measure how long the task takes before we start and track it after go-live, so you see the real effect in your own business. Some workflows are worth automating end to end; for others, we tell you during discovery that AI preparing a draft is the better fit.
