𝐘𝐨𝐮 𝐜𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐞𝐝 𝐲𝐨𝐮𝐫 𝐝𝐚𝐭𝐚. 𝐍𝐨𝐰 𝐰𝐡𝐨 𝐜𝐚𝐧 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐫𝐞𝐚𝐜𝐡 𝐢𝐭?
Classification tells you what your data is.
Access governance tells you who can touch it and whether they should.
These are two different problems and most organizations are significantly further ahead on the first than the second.
Here’s where it gets complicated in the AI era:
When a human accesses data, there’s a trail. A login. An identity. A timestamp. Access patterns that deviate from the norm may surface in your SIEM or your behavioral analytics.
When an AI agent accesses data, it looks like legitimate system activity. The access pattern is broad by design. Agents need to traverse data sources to do their job. The volume of operations happens at machine speed. And the agent itself may have been granted permissions that made sense at deployment and have since become inappropriate as the data it touches has changed.
This is called entitlement drift. And it’s one of the most underdiscussed risks in enterprise AI deployments.
The practical test:
– Do you know which AI models and agents have access to your sensitive data stores right now?
– Do you know if that access is appropriate given the current classification of that data?
– Would you know if an agent was accessing data combinations that create a regulatory or privacy risk?
If any of those answers are uncertain, you have an entitlement problem that your current stack likely isn’t surfacing.
DSPM addresses this by connecting classification to access governance. When data classification changes, access rights are automatically evaluated against it. The two systems talk to each other. Policy becomes dynamic, not static.
In a perimeter security model, you locked the door and trusted what was inside. In an AI-driven data model, the “inside” is everywhere and the agents move constantly.
Access governance has to move with them.
What’s your current process for auditing AI agent permissions?
