Governance rarely feels urgent while an agent is still in demo mode.
That changes quickly once it touches a shared drive, a client record, a finance workflow, or a board pack. At that point, ownership, approvals, rollback, and logging stop sounding theoretical.
That was my main reaction reading OWASP's State of Agentic AI Security and Governance 1.0. Not that the frameworks are missing. They are not. The uncomfortable bit is the gap between the frameworks people can point to and the operating models most organisations actually have.
Too many teams still have two things: an agent that does something useful, and some vague idea that security or compliance probably looked at it. That is not much of a governance model.
A governance model is not a binder on SharePoint. It is an operating model for what the agent can do, what it can access, who owns it, what gets logged, and how you stop it when it goes wrong.
The pattern is predictable because the incentives are predictable.
An agent starts in a sandbox. It summarises documents, triages support, reviews tickets, helps with internal search. People like it. Then it gets connected to one real system because that makes the demo more useful. Then another. Then someone says it would be great if it could take an action instead of just making a recommendation. Somewhere around that point the governance conversation should begin.
In practice, it usually begins later.
After the integration. After the access grant. After the first awkward output. After someone realises the tool is now touching data or processes nobody explicitly approved.
That is why the governance gap matters more than the framework choice.
Most organisations do not need a grand agent strategy first. They need answers to a few plain questions that should already exist before production access is granted:
- What is this agent actually allowed to do?
- What data can it see?
- Which systems can it call?
- Who owns the decision when it is wrong?
- What gets logged and who gets alerted?
- How do we disable it, roll it back, or narrow its scope quickly?
If those answers are vague, the organisation does not have agent governance. It has a tool in production without clear operating boundaries.
This is also where a lot of governance talk becomes largely symbolic. Teams write principles. They draw a RACI. They create a policy with the right nouns in it. Then the real controls are still missing: broad API keys, unclear approval paths, no meaningful separation between pilot and production, and logging that captures the tool call but not the surrounding context that made the tool call seem sensible.
And because the agent output often looks polished, people give it the benefit of the doubt far earlier than they would give the same latitude to a new staff member or contractor. That is the part I find most frustrating. We talk about responsible AI at the exact moment we stop applying normal operational discipline.
You do not need to solve every governance question on day one.
You do need a baseline.
For me, that baseline is simple:
That is not overkill. That is how you treat any system with real access and partial autonomy.
The OWASP report is useful because it helps frame the space. But frameworks will not save a team that still treats governance as paperwork that happens after the build. Governance is the thing that decides whether the build belongs anywhere near production in the first place.
If your agent can already touch real systems and you still cannot answer who owns it, what it can access, and how you shut it down fast, you are not late to the governance conversation. You are already operating without one. But no moment like the present to remediate this.
Related
- Five Things to Get Right Before Deploying AI Agents for a practical pre-deployment checklist
- Securing AI Initiatives for mapping AI risk to established security frameworks
- Autonomy Is the Threat Model for the OWASP agentic top 10 and least agency