Roles assigned up front
IAM grants access based on who you are. An agent’s “who” and “why” change with every task, so static roles either over-grant or block real work.
Securing the agentic world
Your AI agents don’t wait for permission. Your security shouldn’t wait for them.
AI agents are non-human workloads that decide what data they need at runtime. RBAC, IAM, and DLP were built for people with roles assigned in advance. Datafence governs what your agents can reach — and what they can take.
The gap
Built for people. Overrun by agents.
Traditional access control assumes a knowable actor with a fixed role. AI agents break every one of those assumptions — they act on behalf of many users, chain tools together, and improvise their data needs in the moment.
Runtime, not design time
An agent’s data needs emerge as it reasons. Controls that live in a config file can’t see a decision made mid-execution.
Exfiltration by capability
An agent that can read your data can also summarize, transform, and send it. The risk isn’t just access — it’s what leaves the perimeter.
The vision
Ring-fence your data.
The most durable way to control what leaves your organization is to make sure it never has to. Datafence’s direction is data sovereignty: classify and process sensitive content where it already lives — inside your perimeter.
We believe the answer to agentic risk isn’t another gateway that inspects traffic on the way out. It’s keeping the sensitive work inside the fence in the first place.
Our approach runs open-source LLMs on-premises to classify and reason over content locally, so raw data doesn’t have to be shipped to a third party to be understood. From that foundation we extend into content classification and integration with your existing business processes — the fence follows the data, not the network boundary.
Our approach
What we’re building.
Datafence is early. Here’s the ground we’re building on — and the order we’re building it in.
Agent-aware access governance
Policy that understands a non-human actor: what it’s acting on behalf of, what task it’s running, and what data that task legitimately requires — evaluated at runtime, not pre-assigned.
Local classification with open models
Open-source LLMs running on your infrastructure to classify and understand content in place, so sensitivity can be judged without sending raw data outside your walls.
Exfiltration boundaries
Controls focused on what an agent can move, transform, and send — treating egress, not just access, as the thing to govern.
Fits your existing processes
Designed to sit alongside the identity, data, and workflow systems you already run, rather than replacing them.
Talk to us
Bringing agents into your enterprise? Let’s talk about the fence.
We want to hear from security and platform teams thinking about how AI agents touch their data. If that’s you, tell us how.
security@datafence.coOr email us directly — we read everything.