Defense that moves at the tempo of AI-enabled attacks.
Machine speed intelligence is detection and response that keep pace with adversaries who now use frontier and open-weight models to find weaknesses, write exploits and operate at volume. It pairs a detection model built for security telemetry with agentic investigation and response, inside a learning loop the defender owns.
LogLM, a foundation model pretrained on security telemetry: 99% zero-shot, with 1% or fewer false positives.
Vigil agents act within declared intent and paved paths; teams extend autonomy one action at a time as evidence accumulates.
Models, verdicts and learning stay inside your environment, so each investigation strengthens your defense rather than a vendor's.
Attackers adopted AI first. Defenders need a structural answer, not more headcount.
In the post-Mythos era, capable models lower the cost of finding and exploiting weaknesses. SOCs staffed and tooled for human-paced attacks scale linearly, and linear scaling loses to adversaries operating at machine pace.
Adversaries using AI reconnoiter, exploit and move laterally faster than analysts can work an alert queue.
Malware-free and novel techniques look legitimate to a signature. Their behavior over time does not.
Agents built on a general-purpose model inherit its false positives. Detection needs a model built for telemetry.
Regulated, national and defense environments cannot send telemetry to a vendor's cloud in exchange for protection.
Five developments make machine speed defense practical today.
Telemetry pipelines
Security teams have invested in pipelines and lakes such as Cribl, Snowflake and Databricks, so rich telemetry is already in place.
Encoder models
With encoder-only transformers, teams model sequences of events at a scale and cost generative models cannot match.
Reasoning agents
Reasoning models with long-running tool use can investigate and act when bounded by declared intent and evidence.
Open communities
Through open source projects such as Vigil, defenders inspect, share and extend agents and skills.
Measurement
SOCBench gives teams an open benchmark for AI SOC efficacy, so claims give way to evidence.
Autonomy is earned, and the entity earning it is the architecture, not the agent.
An agentic SOC speeds up the approval queue. An autonomous SOC meets a declared objective and improves on its own, with people governing skills, policies and evaluations rather than approving actions one at a time.
Declared intent lives as Markdown under change control, and review moves from the action to the declaration.
Blast radius, reversibility and measured evidence decide how much autonomy each skill earns.
Explicit targets for coverage, false positives and dwell time give teams a principled basis for promotion and demotion.
Read the argument, then test it on your telemetry.
The whitepaper sets out why post-Mythos defense must move to machine speed. A detection assessment shows what that means for your environment.