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Most IT teams have logs. Far fewer have evidence. The two may sound alike, but they answer two very different questions. A log records that something happened. Evidence adds context to the action, such as the identity that authorized it and the policy that allowed it. When someone asks why an AI agent took an action, you need evidence. Not just an activity log.
Keep reading to learn what separates a log from evidence, what’s blocking many teams from building that context for their AI agents today, and how to tell where you stand.
Evidence Connects an Action to Who Authorized It
A log tells you a record changed. Evidence tells you which agent changed it, on whose authority, and whether it should have.
Simply setting up permissions doesn’t fill the gap between logs and evidence. A permission is a setting. It says what an identity is allowed to do, it doesn’t record what it actually did. Permissions are an important part of how you mandate access, but they don’t actually collect the context you need to prove how and why actions are being taken across your environment.
Here’s an example. Say an agent had edit access to customer records during a pilot. The permissions granted to the agent tell you what it’s allowed to change. But only a record tied to that agent, including details like its level of authorization and the policy that applied at the time, tells you what it did.
Most Teams Can’t Yet Make That Connection for AI
We call the distance between what your AI is permitted to access and what you can prove it accessed the proof gap. In our Q4 2026 Pulse Report, we surveyed 250 IT decision-makers and found that 72% have a moderate-to-large gap. Only 4% reported no meaningful gap.
The report also asked organizations about evidence across seven AI activities. Just 34% reported complete evidence of whether sensitive data was shared with AI. Only 38% said they could fully show what data AI accessed, and the same share reported that they could see which identity authorized the activity.
Audit readiness follows a similar pattern. In the same report, 37% said they’re fully prepared with the records, logs, and evidence an AI-related audit would require. Another 52% said they’re mostly prepared, with evidence gaps. The other 11% would need manual work or evidence pulled from multiple systems.
Mostly prepared is a reasonable place to be. It also means some answers won’t be ready when questions about agent auditability and risk management arise.
Why Logs in Separate Systems Don’t Add Up to Evidence
Most teams aren’t short on logs. Identity lives in one system, permissions in another, device context in a third, and the application records the action in a fourth. Each record can be accurate. None of them, on its own, directly and easily ties the action to who authorized it and which policy allowed it.
For human employees, you were likely able to match records at audit time and mostly keep up, because access changed when people joined, moved, or left. But AI agents work at machine speed, and their access can change between one audit and the next. Records matched months later give you a point-in-time snapshot of an environment that has already moved.
That’s the shift continuous compliance asks for: evidence that builds and connects as actions happen. More logs won’t close the proof gap on their own. Connected ones will.
How to Tell Whether You Have Logs or Evidence
Pick one recent action an AI agent took in your environment, and try to answer three questions about it:
- Which identity authorized it?
- What data or systems did it touch, and with what permissions?
- Was the action within policy at the moment it happened?
If the answers are already clearly connected via a unified audit trail, you have evidence. If you need a second system, a ticket, or someone’s memory to line them up, you have logs. And a complex rebuild project. That’s where most organizations in the Q4 2026 Pulse Report sit today, and it’s a fixable position.
Closing this gap matters for more than your next audit. When you can show what an agent did on the day someone asks, you can say yes to the next AI project without slowing down to find out.
We built a full guide around this: what the proof gap looks like across four distinct areas of your environment, why it persists even in well-run IT teams, and what closing it actually requires. Download the eBook to see where your evidence stands, and what continuous compliance looks like in the agentic era.
Continuous Compliance for AI Agents
Point-in-time compliance isn't enough. Learn why AI agents demand continuous compliance.
