AegisAI Local

Detect, respond and recover — entirely on the device.

AegisAI watches the machine through signed, tamper-evident telemetry, catches threats with layered deterministic detectors and on-device machine learning, and explains every behavioural incident with two Llama language models running on the CPU. Nothing is uploaded. It works the same on an air-gapped workstation as on a connected one.

  • 100% on-device
  • Offline / air-gap ready
  • No data retention
  • Two local Llama models
  • You take part in training the AI

What AegisAI does today

This build is a working prototype of AegisAI’s core. It already runs the complete chain from kernel telemetry to an explained, contained incident on an ordinary PC. It is deliberately strict about one thing: in this prototype the rules decide, and the language models only explain.

A multi-pronged detection core

Curated SigmaHQ behavioural rules over ETW and Sysmon telemetry, YARA rules on files and in process memory, ClamAV signatures, and two ONNX scorers — a supervised behaviour model and an unsupervised novelty model. A threat missed by one layer can be caught by another, and agreement across layers raises an incident’s severity. In the final product you choose which engines examine the files during a scan, the dual-LLM reasoning pipeline among them, and the order in which they are used.

Two Llama models, on the CPU

A stock Llama 3.2 1B gives every behavioural incident a first read within seconds. A deterministic router ranks the work. A Llama 3.2 3B fine-tuned for AegisAI writes the authoritative explanation, mapped to MITRE ATT&CK. Analyst-grade narratives, produced without a byte leaving the machine.

Response with a safety floor

Malicious files are quarantined into a protected, restorable store. Malicious processes are suspended, not killed, so their memory survives for analysis. A safety floor refuses to touch critical system processes or validly signed binaries. Every action is audited, and every one can be undone.

Incident controls, checked before they are offered

Each incident offers only the actions that can work on this machine at that moment, decided by the engine and checked again every few seconds. A button for a process that has exited, a signed file, or a protected system folder is never shown. One that applies but not yet is greyed out, with the reason in its tip.

A running process can be paused, resumed, asked to close, force-killed, or force-killed with its whole process tree — children first, and the whole tree refused if any member is one Windows needs. A file can be quarantined and restored to its original path, or deleted together with the files the program wrote, and every file involved is listed with its full path. A file can also be declared safe from its incident, even one no engine can read: what was done is reversed — the quarantined copy restored, the paused program resumed — and its incidents are removed from the journal, the action audit and the logs as if they never happened. A signed program that a behavioural rule matched cannot be declared safe, so a trusted system tool can never be cleared by accident.

Sharper and faster detection

157 curated rules, including AegisAI’s own rule for an unsigned program started from a user’s AppData Roaming folder, a common staging place for malware. The 157 rules in this prototype become many more in the final release of the product, and they keep getting updated regularly. Process-tree corroboration is bounded to the incident’s own time window. A vendor-signed program in its own Program Files folder is no longer mistaken for a renamed system tool. A cache of signature verdicts makes a repeat quick scan about three times faster.

A system scan names the engine and the rule behind every finding and every file it could not examine. A file rescanned successfully or declared safe leaves those lists. A locked system file declared safe by its path is passed by while it stays unreadable, then examined like any other once it can be read.

Honest status, built-in attribution

The product reports reduced capability rather than painting a green tick over a degraded system. Every detection rule’s author is credited wherever its matches appear, as the Detection Rule License requires.

Already built — ships in the final product

AegisAI’s own kernel network-enforcement driver

AegisAI has its own Windows Filtering Platform callout driver, developed and working. It sits in the Windows network stack and makes a block-or-permit decision on every connection, so a malicious process can be cut off before its first packet leaves the machine — enforcement, not just observation.

It is left out of this prototype for one reason only. Windows loads a kernel driver only when Microsoft has signed it, and Microsoft’s attestation signing requires an EV code-signing certificate, which is not yet in place. Running the driver unsigned would mean switching the machine into test-signing mode — Secure Boot off, and a permanent Test Mode watermark on the desktop — and that is no way to present a security product. Until the driver is signed, this prototype observes network connections through Sysmon. The final product ships the driver and enforces.

  • Developed and working
  • Blocks before a packet leaves
  • Awaiting a driver-signing certificate

The same ownership continues on the fine-tuning page and on how you become part of the process. Personal Sigma, YARA and signature rules sit beside the trained models in the finished product, and no update ever overwrites them.