Launch Deck

Pre-IoC Frontier AI Threat Intelligence

LOGFORCE turns local AI and cyber telemetry into Modulations of Compromise before traditional indicators emerge.

Why Now

The Detection Window Is Collapsing

Frontier AI workflows move across prompts, agents, tools, retrieval layers, endpoints, cloud and OT before stable indicators exist.

IoC Arrive Late

Classic indicators are strongest after behavior has stabilized. AI-speed attacks need a signal layer before that moment.

LLM Guardrails Are Not Enough

Semantic review helps, but it is slow, bypassable and not designed to correlate distributed attack motion.

Pre-IoC Is the Missing Layer

LOGFORCE targets measurable convergence before a rule, signature, IOC or complete semantic class exists.

Gartner Gap

A New Quadrant Will Be Needed

Gartner has a 2026 Magic Quadrant for Cyberthreat Intelligence Technologies. LOGFORCE targets an earlier layer: pre-IoC frontier security intelligence between telemetry, CTI and autonomous security operations.

Cyberthreat Intelligence

Threat data, vendor intelligence, external risk and operationalization after threat evidence is collected.

Pre-IoC Frontier Security

Predictive signal intelligence that detects attack motion before conventional indicators are ready.

From IoC to MoC

Modulations of Compromise

MoC describes how hostile behavior moves over time: phase, amplitude, recurrence, rhythm, drift and cross-channel correlation before traditional indicators emerge.

Traditional IoC

Artifacts after classification: IP addresses, domains, URLs, hashes, process names and payload fragments.

LOGFORCE MoC

Predictive pre-IoC signal intelligence before the attack becomes semantically complete or classifiable through the methods currently used.

Not an LLM

A Signal Primitive for LLMs

LOGFORCE is not an LLM judge, not a chatbot firewall and not an agentic security workflow. It creates compact signal evidence that LLMs, SOC copilots, SIEM and EDR can consume.

Deterministic Core

Signal processing, reproducible feature generation and MoC fingerprinting remain the detection foundation.

LLM-Compatible Output

LLMs can reason over MoC evidence without receiving raw prompts, private logs or completed incident artifacts.

Less Theater, More Telemetry

The value is measurable signal movement, not a model pretending to understand every attack narrative.

LOGFORCE FRONTIER SIGINT

LFS-01

A local-first signal model designed to run inside the LOGFORCE Sensing Layer, operate on MoC fingerprints and support prediction, labeling, ranking and explainability.

Local AIRuns near critical workloads

Not LLMSignal model, not language model

768DMoC fingerprint space

0Remote raw PII objective

Architecture

Local-First Sensing Layer

Authorized local sensing feeds the LOGFORCE Sensing API. LOGFORCE Sensing Nodes aggregate approved streams, form MoC fingerprints and run LFS-01 where appropriate.

01

Authorized Local Sensing

Browser, AI, endpoint, cloud, IoT, OT and workload signals.

02

Sensing API

SDK-friendly normalized packets enter the LOGFORCE pipeline.

03

Sensing Node

Local aggregation, MoC formation and evidence references.

04

Compressed Export

Signal intelligence moves; raw evidence remains local.

Prompt PressureAI Boundary91%

Tool-Call CadenceAgent Runtime86%

Retrieval DriftRAG Layer78%

Endpoint BurstAsset Boundary82%

Inspectable MoC

3D Spectral Reconstruction

The playground shows temporal motion, DFT decomposition, MoC Signature Spectrum and TOON export in one explainable inspection surface.

3DSpectral chamber

DFTFrequency evidence

TOONStructured export

MoCDetection artifact

Initial API Families

Frontier Security Starts Here

LOGFORCE targets attack convergence across AI systems and cyber telemetry before a completed event exists.

Prompt Injection

Pressure and policy-boundary motion against AI interfaces and agent workflows.

Credential Discovery

Local evidence patterns around secret access and authorization surface probing.

Supply Chain Poisoning

Package, CI/CD, registry and dependency motion before a classic compromise indicator exists.

Adaptive Exploitation

Changing sequences correlated across authorized observation surfaces.

Privacy-Preserving Intelligence

Collective Learning Without Raw Evidence Movement

LOGFORCE can create environment-tailored intelligence locally and future opt-in collective intelligence from compressed MoC statistics and model updates.

Raw Evidence Local

Prompts, logs, page content, user context and private evidence remain under enterprise control.

Compressed Intelligence

Fingerprints, scores, references and campaign correlations move through approved paths.

Data Network Effect

The moat is outcome-labeled signal intelligence, not centralized private content.

Why Invest

Category Creation + Local AI + Privacy Moat

The opportunity is not another SIEM, EDR, CTI feed or LLM wrapper. It is the pre-IoC signal intelligence layer for frontier AI security.

01

New Category

AI-speed attacks create a market gap before traditional intelligence categories fully respond.

02

Technical Moat

MoC feature space, 768D fingerprints, DFT inspection and patented technology positioning.

03

Local AI

LFS-01 can improve prediction and labeling while preserving deterministic evidence paths.

04

Enterprise Pull

Critical workloads need privacy-preserving intelligence without moving sensitive evidence.

Start the Evaluation

Test the Pre-IoC Signal Layer Before the Market Names It

hello@logforce.com

Request access to the LOGFORCE enterprise evaluation and investor technical walkthrough.