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Governance, access, and the trust behind your tools

Good morning. On a day when one of the most prominent voices in enterprise software warned that the companies building frontier AI are not yet trustworthy enough to run inside a business, several productivity vendors shipped features that pull in the same direction. The thread is governance: the unglamorous question of who is allowed to act, whether you can trust the machine doing the acting, and how you check what happened afterwards.

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What we're tracking

  • The trust question sits above the tool When you buy software with AI inside it, you are not only buying a capability. You are also buying a relationship with whoever built and runs the model behind that capability. Those are two separate things, and it is worth keeping them separate when you compare products. A feature can be genuinely useful while the vendor supplying the underlying model is still an open question for your risk team.
  • Governance is becoming a product feature For most of the past two years, the AI conversation among software vendors was about capability. Could the tool write the email, summarise the call, generate the image. The newer conversation is about control: can you set rules for what the automation is permitted to touch, and can you see what it did. That shift matters because an automation with real reach into your data and your customer records is only as safe as the limits you put around it.
  • Access control is the boundary that does the work Role-based access control, usually shortened to RBAC, is the mechanism most of that governance rests on. The idea is simple. Instead of granting each person a bespoke set of permissions, you define roles, attach permissions to those roles, and assign people to roles. A new hire in support inherits exactly what a support person should see, and nothing more. When someone changes jobs or leaves, you change one assignment rather than hunting through a dozen tools.
  • Agents and generators are not the same purchase A lot of confusion in the market comes from putting two different kinds of AI in one bucket. Generative AI produces something when you ask: text, an image, a summary. Agentic AI is meant to pursue a goal across several steps, deciding and acting along the way rather than returning a single output. The distinction changes what you are buying and what can go wrong. A generator that produces a poor draft costs you a rewrite. An agent that takes a wrong action costs you the action.
  • A clearer record of what was shown and said Governance is not only about permissions before the fact. It is also about the record afterwards. Most decisions in a business are made in meetings and reviews, and the record of those decisions is often thinner than the decision deserves. A transcript captures the words but loses the slide that was on screen. A comment on a video says "the bit near the middle" and leaves the reader scrubbing to find it.
  • What to take from the day Read together, Monday's announcements describe a market growing up. The questions are moving from "can the tool do this" to "who is allowed to make it do this, can I trust what sits behind it, and can I see what happened." When you next compare tools, treat those as first-order questions rather than fine print. 360REV is built around keeping the conversation, the records, and the audit trail in one place precisely so those questions have answers. The tools that will still serve you in a year are the ones that took governance seriously before you had to ask.

From the blog

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