Department · Security

Give agents only the access they need, at the moment they act

Prevent AI agents from turning legitimate access into unauthorized action.

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The problem

Traditional security tools can show that an API call occurred. They often cannot explain which agent initiated it, whose authority it used, why it took the action, or what sequence of model decisions and tool results led to it.

Agents also change the threat model. A compromised prompt, malicious document, faulty model decision, or overly broad integration can cause an otherwise authorized identity to perform an unsafe action.

Agentic Fabriq checks each agent action at the moment it happens, rather than relying only on standing access. Policies can weigh the agent, user, resource, action, workflow, environment, chain of hand-offs, and risk level.

The stakes

Why this is hard today

  1. 01Your SIEM shows an API call happened. It can’t tell you which agent made it, whose access it used, or why.
  2. 02A poisoned document or a clever prompt can turn a well-behaved agent into an attacker with legitimate credentials.
  3. 03An employee connects an agent to their accounts, and it silently inherits everything they can touch.
  4. 04Containing a compromised agent today means disabling the employee it runs as — taking a person offline mid-incident.
Capabilities

How Fabriq helps security teams

Identify every agent and associate it with an owner and approved purpose.Unowned automations disappear: anything acting on your systems has a registered identity, a responsible human, and a stated purpose to check behavior against.
Grant only the access each action actually needs.Grants are expressed as specific actions on specific resources, so an agent that reads tickets cannot also edit them just because the API allows it.
Prevent an agent from using a user’s full access simply because the user connected it.Delegation is scoped: connecting an agent hands it a defined slice of your authority, never your whole token.
Restrict high-risk actions based on amount, destination, data sensitivity, or environment.Policies read the content of the action — a transfer amount, an external domain, a data classification — not just who is asking.
Require human approval when an action exceeds policy.Out-of-bounds actions pause rather than fail silently or proceed; a named approver decides, and the decision is part of the record.
Detect unusual behavior across users, tools, and workflows.Because every action routes through one layer, deviations from an agent’s normal pattern stand out against a complete baseline.
Reconstruct the chain of events behind an incident.From any suspicious action, walk back through the tool calls, approvals, delegation, and triggering input that produced it.
Contain a compromised agent without disabling the underlying user.Revoke the agent’s identity and grants while the employee keeps working — containment no longer means locking a person out of their job.
In practice

Example workflows

logged

Blocking an agent from sending sensitive files to an external domain.

Destination-aware policy stops the send at the tool-call layer, even if the agent was manipulated into attempting it.

approval: human approval

Requiring approval before an agent changes production infrastructure.

Infrastructure-touching actions pause for the on-call engineer’s sign-off, turning a possible outage into a review step.

logged

Preventing a support agent from reading customers outside its assigned region.

Resource scoping pins the agent to its assigned region’s customers, so a crafted request cannot widen its reach.

approval: human approval

Limiting a finance agent to approved counterparties and transaction thresholds.

Allow-listed counterparties and amount ceilings are enforced per transaction, independent of what the model decides to try.

Business value

Smaller blast radius, tighter access, and incident evidence designed for autonomous systems rather than traditional API traffic.

Questions

Common questions

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