Many organisations have now written an AI acceptable use policy. A policy sets expectations, but it gives security teams no record of which tools people are using or what information goes into them.

Staff paste customer details into a chatbot to tidy up an email, upload a contract to get a summary, or install an extension that reads every page they open. AI agents connect to mailboxes and shared drives and open their own connections out of the organisation. Most of that activity never registers with controls designed for an earlier generation of web traffic.

For security leaders, and partners advising customers on AI adoption, that raises an important question: can you enforce the AI policy you have written?

Join Renaissance and iboss for a practical look at how organisations can turn an AI policy into something they can enforce and evidence, while keeping the AI tools their people rely on.

Simon Eappariello, SVP at iboss, will work through a three-stage framework: seeing AI activity across users, devices and locations, controlling which tools and which data are permitted, and preventing sensitive information leaving at the moment it is shared.

Who Should Attend?

This webinar is designed for IT and security leaders, CISOs and security architects shaping how their organisation uses AI, alongside MSPs, MSSPs and resellers helping customers adopt AI securely. Whether you are writing your first AI policy, trying to understand what AI tools are already in use, or advising customers on how to protect sensitive data, the session will offer a practical framework you can apply straight away.

Register now to find out how to close the gap between your AI policy and your ability to enforce it.

Key Takeaways

During this session we will cover:

1. See: How to identify the AI applications, browser extensions and agents in use across your organisation, approved or otherwise.
2. Control: Setting guardrails by user, group and application so approved AI use carries on while risky use is restricted, including keeping staff in enterprise AI accounts instead of personal ones.
3. Prevent: Stopping sensitive data leaking through prompts, uploads and copy and paste actions, with in-the-moment coaching for users.
4. Why traditional controls miss AI activity: Where web filtering, proxy and label-based DLP fall short when data moves into AI tools.
5. How it fits together: How AI security, data security and app visibility and control work from one policy engine alongside your wider web and cloud security.

Agenda

11:00 – Welcome and Introductions

11:05 – Where AI Meets Your Data: Closing the Gap Between AI Policy and AI Control

11:45 – Q&A

12:00 – Close