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Most AI security is passive — detect, alert, log. BETHANY is the first deterministic AI security architecture with zero false positives that enables autonomous response. Because you can't automate a system that's wrong 5% of the time.
The commercial generation of AI-assisted security tools shares three failure modes that make them unsuitable for regulated or OT deployment:
Probabilistic false positives. Cloud-based ML models routinely produce false alarms. In OT environments where downtime cascades into operational failure — fuel distribution, warehouse automation, logistics infrastructure — false-positive-driven automatic response is unacceptable.
Cloud dependency. Most commercial AI security agents require cloud connectivity for model inference. This is incompatible with air-gapped OT segments, classified networks, and any environment where data sovereignty is non-negotiable.
Black-box decisioning. Probabilistic LLM-based detection cannot be audited at per-decision granularity. For environments where every authorization decision must be defensible to oversight — OIG, GAO, DoD — black-box detection is a compliance liability.
"The unsolved problem: an AI-assisted security system that is deterministic, air-gappable, auditable per-decision, and precise enough to enable autonomous response."
Not a better version of what exists. A different architecture entirely — deterministic where others are probabilistic, local where others are cloud-dependent, auditable where others are black boxes.
| Property | Commercial AI Security (CrowdStrike, SentinelOne, Darktrace) |
BETHANY |
|---|---|---|
| Detection type | Probabilistic ML — can't be audited per-decision | Deterministic mathematical cascade — every decision attributable to specific layers, parameters, and threshold values |
| False positives | 5%+ industry baseline — operationally disruptive in OT contexts | 0.0% across 1,700+ assessments — the precondition for autonomous response |
| Cloud dependency | Required for inference, signature updates, telemetry aggregation | Zero cloud dependency — fully air-gappable on commodity hardware |
| Audit trail | Log-level event records — no per-decision parameter attribution | Per-layer, per-parameter SQLite audit trail — every block decision fully attributable and locally stored |
| Learning model | Periodic model retraining — cloud-dependent, delayed | Jean learns continuously — every encounter hardens detection for the next one, with no retraining, no cloud update cycle, and no external dependency |
| OT deployment | Limited — cloud dependency and FP rate incompatible with OT operational tolerance | Designed for OT — sub-2ms p50 latency, air-gappable, protocol parsers for Modbus, DNP3, OPC UA, BACnet |
The reason AI security has remained passive — detect and alert, never autonomously respond — is that no system has been precise enough to trust with automatic action. BETHANY changes that equation.
Detect anomalies. Generate alerts. Route to human analyst. The analyst decides. The attacker moves faster than the workflow.
Deterministic detection enables autonomous response. Zero false positives means you can trust the system to act — because it has never acted when it shouldn't have.
We don't report the runs that went well and ignore the ones that didn't. Every run is documented — block rates, false positive rates, latency, and honest boundary conditions named explicitly.
| Run | Requests | Block Rate | False Positives | Latency p50 |
|---|---|---|---|---|
| Stress 1 | 510 | 65.0% | 0 (0.0%) | 92ms |
| Stress 2 | 510 | 64.6% | 0 (0.0%) | 130ms |
| Stress 3 | 510 | 63.3% | 0 (0.0%) | 149ms |
| SIL 4 (APT) | 178 | 92.3% APT-class | 0 (0.0%) | ~2ms |
| SIL 5 (SE Layer) | 32 | 60% SE/turn | 1/17 (5.9%)* | ~1ms |
*Run 5 tests Jean's social engineering detection layer (Phase II scope) — not included in Runs 1–4 zero-FP baseline
View full methodology →Two DLA SBIR Phase I proposals submitted June 24, 2026. NV005 — BETHANY adapted for OT and IT/cloud network and endpoint defense. NV006 — CARVER-RMF: AI-assisted Risk Management Framework pre-adjudication, compressing ATO timelines from months to hours. One engine. Two federal bets. Contact Barry Humphrey, Ph.D. at DLA for topic verification.
Full briefing →The cascade reads structural threat parameters, applies domain-invariant mathematical detection, and emits auditable per-layer decisions — on your hardware, with nothing leaving the device.
Network packets, endpoint events, or conversational traffic — BETHANY's substrate-agnostic architecture reads structural threat parameters from any input class. The cascade logic doesn't change substrate to substrate. Only the traffic adapter changes.
Each layer evaluates specific threat parameters against calibrated mathematical thresholds. Every layer firing, every parameter value, every decision is written to a local SQLite audit trail. Nothing is probabilistic. Nothing leaves the device.
Every threat Jean encounters hardens its recognition for future encounters — automatically, locally, without retraining or cloud dependency. The more Jean operates, the better it gets. All of that learning stays on your hardware.
Every block decision is attributable to specific cascade layers and parameter values. When the board asks why something was blocked, the answer is a printout, not a probability score.
Zero external transmission, local SQLite audit trail, air-gappable on commodity hardware. BETHANY's privacy architecture was designed for environments where data sovereignty is non-negotiable.
The same cascade architecture covers IT enterprise, hybrid cloud, and OT networks. One codebase, one audit pattern, one deployment team — across DLA's full network spectrum.
DLA, Army Materiel Command, Navy/Air Force logistics components, and defense industrial base contractors. The same Rust binary deploys across DoD, civilian federal, and commercial critical infrastructure.
We're not taking purchase orders. We're talking to the organizations and investors who understand what deterministic, air-gappable AI defense means — and want to be part of building it.
Tell us your environment, your current stack, and what problem you're trying to solve. We respond personally — no sales process, no auto-responder. If there's a fit, we'll know quickly.
Write to us →contactus@myasolutions.org · we respond to every serious inquiry personally
Evaluating the defense vertical as an investment opportunity? The full BETHANY methodology, five-run validation data, and architecture briefing are on the investor page.
Investor briefing →