Control what AI agents change.

Mimic AI Guardrails is Mimic Networks' product for governing what AI agents change on protected systems. It checks each change an agent attempts at the kernel against the scope approved for that agent and the system's known-good state, and stops what falls outside it before it executes. It works whatever the prompt said, and it holds AI agents, administrators and scripts to the same test.

An autonomous system taking actions its operator didn't intend, usually not through malice but because it misread a task, was manipulated, compounded errors across steps or drifted from its objective. It generally holds valid credentials and uses approved tools, so its activity looks legitimate to identity controls and endpoint detection. What makes an action rogue isn't who took it but whether it was authorized.

By evaluating the change rather than the identity behind it. Credential validity is the wrong control point, because an agent with legitimate access can still take an action nobody sanctioned. Mimic checks each attempted change against the scope approved for the agent and the system's known-good state, and stops anything outside it at the kernel before it executes, whatever account requested it.

Prompt filtering is a model-layer control. It screens what goes into a model and what comes out, and it's worth having. AI Guardrails works at the system layer and governs what actually changes when the agent acts. The two work together. A filter can be talked around. A kernel-level decision judges the resulting change, however the agent arrived at it.

Your team approves the changes an agent may make before it runs. Mimic maps that scope against a known-good profile of the files, processes, registry keys and services the agent is authorized to touch, and prevents an AI-initiated change unless its intent was approved and allowed. Mimic stops a change outside the scope at the kernel, whatever credentials the agent holds or tool it used.

Blocking one action at a time is weak against an adaptive agent, which tries a different route to the same outcome. Mimic's Change Intelligence keeps a running picture of how a protected system is changing: what changed, in what order, how fast and against what baseline. That picture lets Mimic stop a harmful sequence of changes before it completes, rather than deny each step while the agent routes around them.

The difference between what an agent was asked to do and what the system looks like once it has finished. An agent can complete its stated task and still leave changes nobody asked for: altered configurations, new files, modified permissions. Comparing post-task system state against the declared intent surfaces those as violations, which action-by-action monitoring during execution tends to miss.

It addresses the consequence, not the technique. Filtering malicious prompts at the model layer is a moving target, and OWASP ranks prompt injection first in its Top 10 for LLM Applications. Kernel enforcement doesn't need to know how an agent was persuaded. If the resulting change falls outside the agent's approved scope and the system's known-good state, it doesn't execute, whether the instruction came from a user, a document or an injected payload.

It keeps its valid credentials and its approved tool access, so identity-based controls still see an authorized actor. Enforcement holds the agent to its approved scope regardless. The changes an attacker would need it to make fall outside that scope and the system's known-good state, and Mimic stops them at the kernel before they execute. The test stays the same: was this change authorized?

Applying the same authorization test to AI agents, human administrators and automation scripts alike. The question is whether the change was authorized, not who initiated it. That matters because agents commonly run on service accounts or delegated administrative credentials, which identity-based controls treat as trusted by default. Taking identity out of the decision closes that gap.

Enforcement that doesn't depend on which agent framework, orchestration platform or utility initiated an action. Organizations run agents from several vendors at once, and a tool-specific control needs its own policy for each one, so a new framework arrives ungoverned. Enforcing at the kernel means a framework deployed tomorrow meets the same test as the ones running today: was this change authorized?

From a record captured as enforcement happens, not assembled afterward from separate logging tools. Mimic logs a sequenced account of the kernel-level actions an agent took, in order and with timing, including what it tried and what was stopped. Because the record builds during operation, reconstructing an incident becomes a retrieval rather than a forensic exercise, and the record exists before anyone asks for it.

Mimic AI Guardrails and Virtual Patching are separate solutions in the Mimic Platform. Both govern threats that agentic AI presents. AI Guardrails governs what AI agents do inside the environment. Virtual Patching closes the exposure that AI-accelerated vulnerability discovery opens before a vendor patch exists. Both are part of Mimic's known-good enforcement approach.

The decision has to happen in the same operation as the change, not in a review queue. Mimic checks each attempted change against the known-good state at the kernel and either permits or blocks it there, so there's no approval step for the agent to wait on. Mimic decides inline, before the change executes. Enforcement overhead is tunable. Unauthorized actions don't run.