Know what your AI agents touch. Stop overreach.
Arrakis Security watches every autonomous AI agent inside your enterprise and enforces the permission boundary you set for each one.
Autonomous agents don't follow implicit rules. You need explicit ones.
Agents call APIs they were never intended to call
Multi-step autonomous agents resolve tool chains at runtime. Without explicit scoping, a task-planning agent can reach HR systems, billing APIs, or partner endpoints far outside the original assignment.
Data leaks across context boundaries
Context windows don't respect data classification. An agent summarizing a legal document can inadvertently pass PII from one document into a response that flows to a separate, less-controlled downstream system.
No audit trail when something goes wrong
When an AI agent causes an incident, "what exactly did it do" is unanswerable without purpose-built logging. Standard application logs capture HTTP requests, not tool decisions, context reads, and agent reasoning steps.
Four layers, one coherent view of your agent fleet.
Each layer handles a distinct control problem. Together they give you observability, enforcement, evidence, and incident routing for every agent running in your enterprise.
Behavioral monitoring
Arrakis instruments each agent session at the tool-call level, recording every API invocation, data read, file access, and external call in order. You see a timestamped action sequence, not just request logs.
Policy enforcement
Define per-agent permission rules: which APIs it can call, which data sources it can read, which output destinations it can write. Policies evaluate in real time, before the action completes.
Audit trail
Every agent action, policy decision, and session outcome is written to an append-only log. Records include the triggering user, the agent identity, the tool called, the resource affected, and the policy result.
Alert routing
Policy violations fire webhook alerts with full context: agent name, violated rule, affected resource, session ID. Route to Slack, PagerDuty, or any endpoint your incident response workflow already uses.
Connect once. Define policies. Watch continuously.
Connect via SDK or proxy
Install the Arrakis SDK (Python or TypeScript) or route your agent traffic through the transparent HTTP proxy. Instrumentation is additive, no changes to your agent code or deployment process.
Define permission boundaries per agent
Use the policy builder or a config file to declare what each agent is allowed to do. Set scope by API, data source, and tool type. Enable default-deny mode to block any uncovered action automatically.
Monitor live, receive alerts on violations
The Arrakis console streams every agent event in real time. Policy violations surface with full context and route to your incident response stack immediately, no polling required.
What early-access teams are finding.
Numbers from our design partner deployments and internal testing. We state our sources inline, not in fine print.
Building on Arrakis alongside the product.
We deployed three AI agents into our claims processing workflow before realizing we had no way to know which document stores they were reading. Arrakis gave us a session-level view in two days. Now every agent has an explicit scope and we can show our risk team exactly what touched what.
The thing that sold us was the audit log format. We needed to show a customer what our AI agent had accessed during a support session and we could pull an exact record: agent ID, tool calls, data sources, timestamps, outcome. That kind of traceability changes how we're willing to deploy agents externally.
Your agents are running right now. Do you know what they are doing?
Instrument your first agent in under a day. See exactly what it touches, set the boundaries it must stay within, and get alerted the moment it steps outside them.