Run sensitive workloads “in use” without betting your threat model on CPU trust.
Logarchéon is a λ-secure runtime that can be deployed in your environment to reduce “in-use” exposure for
private LLM deployments (V2) and OS/VM workloads in cloud instances (V3).
The default posture is buyer-governed: you hold the keys, you control logging and interfaces, and the runtime
is designed to avoid routine canonical plaintext states during execution.
Run it on your hardware or inside your cloud tenancy.
Encrypted-in-use: protecting sensitive model/data states during active computation (weights instantiated, activations/latents flowing, intermediates present in memory and runtime pipelines).
GRAIL: Logarchéon’s geometry-native execution layer for operating on non-canonical (key-governed) internal representations during training and inference.
Λ-Stack: Logarchéon’s deployment and governance stack for key custody, rotation policy, interface control, and operational controls around GRAIL.
V1 — Intrinsic / λ-native GRAIL (NN/LLM): a commissioned integration path where the geometry is built into the model architecture and training dynamics (integration-first).
V2 — Exported wrapper GRAIL (NN/LLM): a compatibility path that wraps existing models/frameworks so they execute under key-governed, non-canonical internal representations (adoption-first).
V3 — Exported-wrapper posture (general-purpose compute): a wrapper-based containment posture for existing OS/VM/runtime stacks and hardware-adjacent surfaces, extending the same “in-use” discipline beyond AI (compatibility-first).
Scope boundary
This page is a buyer-facing threat model and deployment posture description. Enabling details, proofs, and benchmarks are shared under NDA for serious technical review.
Why this exists: “in use” is where leakage happens
Most security stacks protect AI at rest (storage) and in transit (network). Training and inference are different:
they are in-use operations, where intermediate representations exist in operational form inside a runtime.
This is the phase where endpoints, logs, memory scraping, and firmware-level trust assumptions become decisive.
Firmware subsystems below the OS (what they are)
Modern x86 platforms include vendor-managed microcontrollers that operate below the operating system.
They are part of the trusted computing base even when you cannot fully audit them.
This is not an allegation of intent; it is a standard high-assurance threat-modeling fact.
Intel ME / CSME
The Intel Management Engine (ME) (now typically discussed as part of CSME)
is an embedded controller inside Intel platforms. It runs independently of the host OS, participates in early boot,
and remains active whenever the system has standby power available (i.e., “plugged in” states).
Capability class: execute firmware code below the OS; participate in platform initialization and security services.
Privilege class: below-kernel trust position (outside typical host monitoring), expanding the attack surface of “in-use” secrets.
Why buyers care: if a subsystem can operate outside OS visibility, it changes how you reason about “who can see plaintext while computing.”
Intel AMT (runs on ME, on supported platforms)
Intel Active Management Technology (AMT) is an out-of-band management feature implemented on top of ME/CSME
on certain business-class systems (commonly associated with vPro platforms). It is designed for remote administration
(e.g., provisioning, recovery, power control) independent of the host OS.
Capability class: remote management workflows that do not rely on the OS being healthy or even running.
Threat relevance: out-of-band surfaces are attractive for persistence and bypassing host-based defenses.
AMD PSP / Secure Processor
The AMD Platform Security Processor (PSP) (often called the AMD Secure Processor)
is a dedicated security processor embedded in AMD platforms. It provides services such as secure boot and platform security functions
(e.g., key handling for firmware-backed features).
Capability class: platform security services that exist beneath the OS, in firmware-managed components.
Why buyers care: security processors can be essential for integrity, but they also widen the “below-OS” trust base.
Threat relevance (why attackers value this class of component)
High-assurance buyers treat “below-OS” subsystems as part of the attack surface because they may:
(i) persist across OS reinstall, (ii) operate outside host telemetry, and (iii) expose remotely reachable management planes.
The point is risk posture, not intent attribution.
Persistence: firmware-layer compromise can survive OS wipe/reimage and defeat “clean install” remediation.
Invisibility: host EDR and OS auditing can be blind to below-OS execution contexts.
Out-of-band surface: management functionality can create alternate pathways around OS firewalling assumptions.
Exploit pattern (historical): attackers and red teams target this layer for stealth, durability, and control.
Generalization
Similar “below-OS” management/security subsystems can exist in other chipsets and SoCs.
The absence of public documentation is not evidence of trustworthiness—only of visibility.
Key buyer implication
If your workload ever requires decrypting sensitive representations inside a conventional runtime,
then your threat model must account for all privileged layers that could observe that plaintext
(firmware subsystems, hypervisors, endpoints, telemetry, and operator error).
Important: scope is not “LLMs only”
V3 (exported-wrapper posture) and Von Neumann workloads
While this page uses AI training/inference as the motivating example, the exported-wrapper approach applies more broadly:
most conventional computing still runs on a Von Neumann architecture where “in-use” states
(memory, caches, intermediate values, runtime artifacts) are the primary leakage surface.
V3 (exported-wrapper posture): a deployment posture intended to wrap existing OS/VM/runtime stacks rather than requiring a new machine design.
Objective: reduce the amount of sensitive computation that ever appears in canonical, reusable plaintext form inside the host runtime.
Non-enabling policy: Logarchéon does not publish implementation details that would meaningfully assist reverse engineering or misuse; evaluation materials are shared under NDA for serious reviewers.
Practical framing
Treat this as a system-level hardening posture for in-use secrecy across AI and non-AI workloads, not a feature tied to one model family.
Why common “secure computation” approaches still disappoint buyers
FHE / MPC / TEEs vs. real-world “in-use” workloads
Buyers often hear “just use FHE” or “just use MPC” or “just use confidential computing.”
In practice, each option imposes distinct constraints in performance, compatibility, or trust assumptions—especially for modern neural workloads.
FHE (Fully Homomorphic Encryption)
FHE can, in principle, run certain neural-network operations on ciphertexts. In practice, it often requires
specialized arithmetic choices (e.g., polynomial approximations for non-linearities), careful circuit budgeting,
and substantial performance overhead—especially for large, interactive LLM pipelines.
What it protects: confidentiality during computation without trusting the host OS/hardware.
Operational constraint: large overhead, constrained operator sets, and nontrivial engineering for real-time workloads.
Common buyer outcome: feasible for narrow inference tasks; typically not a default drop-in for LLM training or low-latency serving.
MPC (Multi-Party Computation / Secret Sharing)
MPC can support ML inference/training by splitting secrets across parties and computing jointly.
The trade is that bandwidth, latency, orchestration, and trust assumptions (≥2 parties, honest-majority or similar)
become first-order constraints.
What it protects: no single machine sees the full secret by design.
Operational constraint: communication dominates, especially across WAN; multi-party ops increase failure modes.
Common buyer outcome: strong in principle, but heavy in deployment complexity for interactive or high-throughput pipelines.
TEEs / “Confidential Computing”
Trusted Execution Environments (e.g., SGX, SEV-class) often deliver the best performance, but they do so by placing
plaintext inside an enclave/VM boundary. This shifts the trust burden to vendor firmware, microcode, and side-channel mitigations.
Strength: near-plaintext performance for many workloads.
Trade: you are explicitly trusting a firmware + microcode stack and managing side-channel risk.
Buyer reality: useful when combined with strict endpoint/logging discipline, but not “zero trust.”
The buyer-facing pain point
Why “in-use” still breaks typical deployments
Even when crypto is strong in theory, real deployments often reintroduce exposure through hybrid steps:
preprocessing, tokenization, debugging/telemetry, intermediate caches, model export, or “just for performance” fallbacks.
That is where below-OS trust boundaries and endpoint mistakes become decisive.
Canonicalization creep: systems “temporarily” decode, log, cache, or export canonical representations.
Interface/oracle creation: outputs and timing can become feedback channels in adversarial settings.
Privileged layers: hypervisors, firmware subsystems, and administrative tooling expand who can observe state.
Design goal
Logarchéon is built to reduce the need for canonical “decrypt-then-compute” patterns in the first place,
so sensitive representations are not routinely instantiated as reusable plaintext artifacts inside the host runtime.
GRAIL (definition): the geometry-native execution layer that maintains protected semantics during computation.
Λ-Stack (definition): the productized runtime stack (key custody, policy gates, operational controls) that makes GRAIL deployable.
V3 (definition): wrapper-based containment posture for existing OS/VM/runtime stacks—applicable beyond AI to conventional Von Neumann workloads.
Public materials remain intentionally non-enabling. Serious technical review is supported via NDA-gated briefs and evaluation builds.
Bottom line: The practical failure mode is not “weak crypto” — it is in-use exposure through runtime reality.
Logarchéon’s posture is to minimize canonical in-use exposure by design, without requiring buyers to trust every firmware layer.
Why this exists: the “in-use” trust boundary problem (AI + cloud instances)
Most security stacks protect compute at rest (storage) and in transit (network).
Modern workloads are different: the decisive exposure happens in use,
when secrets must exist in operational form inside a runtime—on a workstation, on-prem server, or a cloud instance.
Definition
What “in use” means (beyond “training”)
“In use” means active computation: weights instantiated, prompts/token streams processed, activations flowing,
embeddings cached, intermediate states manipulated in memory (CPU, GPU/VRAM, accelerators, drivers, and their runtimes).
This includes inference on cloud GPUs, fine-tuning on rented instances, and
non-AI workloads whose sensitive state lives in memory and runtime artifacts.
Encryption at rest and TLS do not, by themselves, address this phase.
Cloud reality
Why “we’re in our own cloud account” is not the whole boundary
In cloud deployments, the workload runs inside a VM/container boundary, but the hypervisor layer,
host firmware, provider management plane, and observability pipeline
can expand who can potentially observe in-use state. This is not a claim of malice—only the standard high-assurance posture:
define the boundary by capability, not by organizational comfort.
Hypervisor / host control: privileged layers outside guest OS visibility can exist by design.
Snapshots, crash dumps, and diagnostics: operational tooling can unintentionally capture sensitive memory state.
Telemetry and logging: “helpful defaults” can create durable artifacts (debug logs, traces, profiling outputs).
Accelerator stack: drivers, kernel launches, and VRAM buffers are part of the in-use surface.
Buyer implication
If your threat model includes powerful operators (malware, insider risk, supply-chain exposure, or privileged cloud surfaces),
then “encrypt at rest + TLS” does not close the in-use gap.
Why “trust the CPU” is not a neutral assumption
Modern platforms include vendor-managed subsystems designed for management and security.
These components can operate below the OS and expand the trusted computing base.
This is not an allegation of intent; it is a standard threat-modeling reality for high-assurance buyers—on-prem and in cloud hosts.
Vendor firmware subsystems (examples)
Intel Management Engine (ME / CSME) — a dedicated microcontroller present in Intel chipsets since ~2008,
capable of running code independently of the host OS and participating in early boot and platform services.
Intel Active Management Technology (AMT) — an out-of-band remote management capability running on ME
(e.g., remote power, provisioning, recovery) on supported platforms.
AMD Platform Security Processor (PSP / Secure Processor) — a dedicated security processor embedded in AMD systems
(since ~2013) used for secure boot, key handling, and platform security services.
This class of subsystem is designed to operate below OS visibility.
Historically, attackers and red teams value such layers for persistence, invisibility, and control.
Below-OS persistence: firmware-layer compromise can survive OS reinstallation.
Out-of-band surface: remote management features can bypass host-based defenses.
Vulnerability history: critical bugs have been documented over time.
Example signals (non-exhaustive):
Intel advisories
SA-00075,
SA-00086,
and CVE-2017-5689
(NVD).
Generalization
Similar management/security subsystems may exist in other SoCs, including less-documented or foreign chipsets.
Lack of documentation does not imply trustworthiness.
Why common “secure computation” methods still disappoint buyers
FHE, MPC, TEEs vs. real-world in-use workloads
FHE: powerful in principle, but overhead and operator constraints often block low-latency or large-scale neural pipelines.
MPC: strong confidentiality properties, but multi-party orchestration, bandwidth, and latency dominate operational reality.
TEEs / confidential computing: often the best performance, but they shift trust to firmware/microcode stacks and require side-channel discipline.
Runtime reality: many deployments still reintroduce plaintext through debugging, telemetry, caches, exports, or “temporary” decode steps.
Buyer-held keys: governance, instantiation, and blast-radius control remain under customer custody.
Interface discipline: export, logging, telemetry, and debug are policy gates—not “helpful defaults.”
Non-canonical in-use posture: reduce the need for canonical “decrypt-then-compute” patterns that create reusable plaintext artifacts.
V3 (MIA exported-wrapper posture): extends the same in-use containment logic to cloud instances and
general-purpose compute across OS/VM/runtime boundaries (not just “training AI”).
Bottom line: The practical failure mode is not “weak crypto” — it is in-use exposure through runtime reality
(firmware layers, hypervisors, telemetry, and operator error). Logarchéon is structured to reduce canonical in-use exposure by design,
with evaluation claims bounded by written scope and acceptance criteria.
Residual risk & buyer responsibility
What this does not eliminate
No software system eliminates physical, RF, or side-channel risk.
High-assurance deployments may still require controlled environments (SCIF/EMSEC discipline),
power isolation, and strict endpoint governance—especially when the adversary model includes sophisticated operators.
Responsible deployment reminder
Logarchéon reduces in-use exposure and artifact reuse. It does not replace physical security, emissions control, or operational discipline.
Why CPU Trust Is a Hard Assumption — and How V2 vs V3 Change the Boundary
High-assurance buyers model “in-use” risk by capability: which layers could observe or preserve sensitive state
while computation is active. This is true on a laptop, on-prem server, and cloud instances
(where hypervisors, host tooling, and observability pipelines expand the privileged surface).
Threat-model premise
“Below-OS” subsystems expand the trusted computing base
Modern platforms include vendor-managed subsystems designed for management and security.
They can operate below the OS and are often not fully auditable by the workload owner.
This is not an allegation of intent; it is a standard threat-modeling reality.
Representative examples (publicly documented)
Intel Management Engine (ME / CSME) — an embedded controller in Intel platforms since ~2008,
participating in early boot and platform services, operating independently of the host OS.
Intel Active Management Technology (AMT) — out-of-band management features implemented on top of ME/CSME
on supported platforms (e.g., remote provisioning, recovery, power control).
AMD Platform Security Processor (PSP / Secure Processor) — an embedded security processor in AMD platforms since ~2013,
used for secure boot and platform security functions (including key-handling for firmware-backed features).
Bottom line: “Trust the CPU firmware” is not a neutral assumption. On cloud instances, you typically add hypervisor/host
layers and provider operational tooling to the privileged surface. A credible posture reduces the need for canonical plaintext “in-use” states,
and constrains interfaces that turn artifacts into an oracle.
Cloud-specific extension
Why cloud instances amplify “in-use” exposure if you rely on canonical plaintext
Hypervisor/host privilege: guest OS controls do not fully govern the host/hypervisor layer.
Snapshots and diagnostics: crash dumps, snapshots, and support workflows can preserve memory-adjacent state.
Observability pipelines: tracing/profiling/logging defaults can create durable artifacts from transient computation.
Accelerator stacks: GPU drivers, queues, and VRAM surfaces are part of the in-use boundary.
Interpretation
Cloud is not “insecure,” but it forces explicit answers to: who can snapshot, inspect, or persist in-use state—intentionally or accidentally?
Comparison: secure computation approaches vs. Logarchéon’s V2 and V3 posture
Method
What’s Protected “In Use”
Trust & Leakage
Canonical Decryption Inside Host Runtime?
Overhead vs plaintext (plaintext = 1×)
Speed vs FHE (FHE = 1×)
Fit for modern workloads
FHE (CKKS, TFHE)
Data remains encrypted during computation (within supported operator sets)
No hardware trust; often leaks access patterns unless paired with ORAM
No
~103×–106× (workload-dependent)
1×
Often impractical for low-latency, large neural pipelines
MPC / Secret Sharing
Secrets split across parties; compute jointly
Trust split across ≥2 parties; communication dominates
No
~10×–100× (common regimes)
10–100×
Strong security; heavy orchestration and latency for interactive pipelines
ORAM / Garbled Circuits
Obfuscates data and/or access patterns under circuit models
Bandwidth/latency overhead; strong privacy with padding
No
~10×–100×
10–100×
Useful for circuit-style tasks; less natural for large NN training
ZK / zkML
Verifiability (proof of correct execution)
Does not protect live runtime confidentiality by itself
Yes (for proving, not privacy)
N/A
2–10× (verification context)
Useful for audits and claims, not “in-use secrecy”
Workload-dependent (bounded by acceptance criteria)
N/A (posture + wrapper, not an FHE competitor)
AI + non-AI: VM/container workloads, conventional computation, and cloud/on-prem deployments
Clarification: V2 and V3 address different layers.
V2 (GRAIL) concentrates on the NN/LLM execution path and its interfaces.
V3 (MIA) extends the same discipline to OS/VM/runtime boundaries and cloud instance realities
(snapshots, diagnostics, observability, and artifact reuse). They are designed to compose under a single governance model:
buyer-held keys and policy-gated interfaces, with claims bounded by written scope and acceptance criteria.
Why this avoids the “CPU trust trap” as a consequence of the design
The goal is not to make philosophical claims about CPUs; it is to reduce the amount of sensitive state that ever appears
as reusable canonical plaintext during active computation, and to prevent “convenience interfaces” (logs, exports, debug endpoints,
snapshots) from becoming an oracle. In cloud environments, this means treating hypervisor/host and operational tooling as part of the boundary.
Note: This page is intentionally non-enabling.
Detailed substantiation, benchmarks, and coverage specifics (including cloud placement and accelerator interactions)
are provided under NDA for serious technical review.
Who this is for
Strategically, Logarchéon is built for high-assurance missions: national security, defense and intelligence,
critical infrastructure, and systemic finance. Tactically, early deployments also fit environments where a
small team can execute quickly: law firms, privacy-first startups, and a small number of high-confidentiality
civil organizations.
Tier I · Core high-assurance missions
US natsec, defense, IC, and systemic risk
US national security & defense: IC agencies, DoD components, and R&D programs that need encrypted-in-use AI, twin deployments, and audit-grade interpretability—plus hardening at the VM/OS boundary.
Defense & intel industrial base: primes and niche defense AI vendors embedding hardened AI into operational systems with explicit threat models and constrained interfaces.
Systemic finance & markets: exchanges, SIFIs, and elite risk/quant shops where model/IP protection, auditability, and sovereign execution environments are first-order requirements.
Critical infrastructure / OT: grid, pipelines, rail, aerospace manufacturing, and industrial control where failure modes are physical, costly, and sometimes irreversible.
Strategic alignment
These actors have the strongest overlap with GRAIL/Λ-Stack/MIA: mission risk, appetite for rigorous review,
and budgets for non-commodity IP and system-level hardening.
Tier II · Regulated expansion
Healthcare, regulated enterprise, and platforms
High-security healthcare / clinical AI: PHI-heavy workflows that require tenant isolation, auditability, and strict interface control (including VM/OS posture for cloud/on-prem deployments).
Regulated enterprise: energy, pharma, aerospace, and advanced manufacturing that need IP protection, governance, and controlled “in-use” exposure across both AI and non-AI workloads.
Cloud & hardware vendors: longer-horizon licensing of encrypted-in-use engines, twin-model infrastructure, and invariant-first accelerator strategies.
Academic / non-profit labs: collaboration partners for science-grade AI security and interpretability—often for credibility and research leverage more than near-term revenue.
Role
Expansion segments once the core stack is proven; they benefit from the same λ-secure foundation while
pushing deployment breadth (cloud instances, VM stacks, and regulated operational envelopes).
Tier III · Sandbox & civil
Law, founders, and high-confidentiality civil orgs
Law firms & in-house legal: cannot upload client files to generic LLM APIs; need on-prem or BYO-cloud private AI with a stronger story than “trust our logs,” plus VM/OS boundary discipline.
Privacy-first founders & indie teams: treat their data as the moat; want local or tenant-isolated LLMs without leaking core IP into third-party model stacks.
High-confidentiality civil / religious / humanitarian organizations: religious orders, diocesan structures, professional bodies, and select NGOs requiring sovereign control over internal archives and workflows—often with mixed AI and non-AI workloads.
Why they matter
These segments are ideal early sandboxes: shorter cycles, unclassified workloads, and strong privacy instincts that
stress-test the stack (including MIA/V3 posture) before it enters more sensitive or systemic domains.
Short version: law firms and startups are not the final destination; they are the proving ground.
The long-term home for Logarchéon is high-assurance environments where encrypted-in-use AI and hardened VM/OS boundaries
are mission-critical—not marketing.
Bring your own models — and keep your stack.
Logarchéon does not ask you to throw away the models or infrastructure you already use.
The stack is designed to (i) wrap existing NN/LLM runtimes for encrypted-in-use posture (V2),
(ii) support commissioned λ-native models when architecture-level integration is required (V1),
and (iii) extend the same “non-canonical in-use” discipline to VM/OS/runtime boundaries for general-purpose workloads (V3).
Version map:V2 (GRAIL wrapper) = adoption-first for existing models ·
V1 (λ-native) = commissioned integration ·
V3 (MIA posture) = wrapper posture for VMs/OS/runtime interfaces (AI + non-AI).
Model-agnostic (V1/V2)
Works with modern model families
Start with compact and mid-scale models for single-GPU or modest-server deployments.
Scale to larger models using your orchestration as hardware allows.
Keep preferred tooling (HF, vLLM, llama.cpp, etc.); the posture is built around your ecosystem.
λ-secure wrapper (V2)
A geometry-native shell around existing NN/LLM runtimes
Models execute under customer-held secrets (keys) and explicit policy gates.
Training and inference operate in a non-canonical internal representation, reducing “artifact reuse” risk when storage is stolen.
Tenant/team separation is supported under the same governance model (keys, rotation, interface control).
Boundary
V2 focuses on the NN/LLM execution path and its interfaces; it is designed to compose with V3 for system-level hardening.
VM/OS wrapper posture (V3 · MIA)
Harden the runtime boundary for AI and non-AI workloads
Constrain high-risk surfaces: logging/telemetry, debug dumps, snapshots, exports, and admin endpoints that can become an oracle.
Designed for cloud instances and on-prem deployments where hypervisor/host tooling and observability pipelines expand the privileged surface.
Why it matters
Cloud is viable for high-assurance work only when the VM/OS boundary and artifact lifecycle are governed as first-class security controls.
λ-native path (V1)
Commission λ-native models when architecture-level integration matters
For some missions, you may prefer models trained from the ground up with geometry integrated into architecture and dynamics.
This preserves the same governance principles while targeting tighter semantics and reduced “canonicalization creep.”
Best suited when the model is a long-lived sovereign asset and the mission justifies deep integration.
Operational reality
Governance is the product
Buyer-held keys: default posture is customer custody, with rotation and blast-radius control.
Policy-gated interfaces: exports, telemetry, and debugging are controlled—not convenience defaults.
Composable deployment: V2 (model path) + V3 (system boundary) combine to reduce “in-use” exposure across the stack.
Plain language: Logarchéon is a λ-secure posture for your AI portfolio (V2/V1) and your runtime boundary (V3).
You choose the models and deployment envelope; Logarchéon supplies the geometry-native wrapper, governance stack, and VM/OS posture so the system
behaves as an encrypted-in-use engine under your control.
Segment map & priority
A concise view of the option space. Segments cover Logarchéon’s plausible buyers and are ranked by strategic fit,
ability to pay for deep IP, confidentiality needs, and feasibility for a one-person IP lab.
Rankings assume the current stack is versioned: V2 secures NN/LLM execution paths; V3 (MIA posture)
extends the same “in-use” governance to VM/OS/runtime boundaries in both on-prem and cloud instance deployments.
Rank
Segment
Role & why
1
US NatSec / Defense / IC
Core strategic home. Highest mission alignment, deep need for encrypted-in-use AI and strict “artifact lifecycle” control.
Strong fit for V2 (model path) plus V3 (VM/OS boundary) in controlled clouds and on-prem enclaves.
2
Defense & Intel Industrial Base
Primary route into real systems (ISR, EW, C2, secure analytics). Embed runtime posture via primes and niche integrators:
V2 for AI execution and V3 for VM/container/hypervisor boundary discipline in production stacks.
3
Systemic Finance & Markets
High budgets and strong incentives for audit, privacy, and IP protection. Natural fit for encrypted workflows and
governed “in-use” execution, including cloud instance deployments where VM snapshots, logging, and ops tooling are major risk surfaces.
4
Critical Infrastructure / OT
Physical consequences and long-lived hardware. Long-term home for hardening at the control-plane boundary and
invariant-first compute; V3 (MIA posture) is especially relevant because many workloads are not “LLM-shaped.”
5
High-security Healthcare / Clinical AI
PHI-heavy and safety-critical decisions. Benefits from encrypted-in-use execution and strict interface governance
(V2 + V3), but timing is later due to procurement and regulatory complexity.
6
Regulated Enterprise (non-financial)
Energy, pharma, aerospace, advanced manufacturing. Strong need for IP protection and sovereign execution
(often BYO-cloud), but crowded with incumbents; differentiation relies on explicit threat-model posture (V2/V3) rather than generic “private GPT.”
7
Law Firms & Legal Ecosystem
Near-term sandbox. Clear confidentiality norms and visible pain around public LLM APIs.
Good early pilots for tenant-isolated BYO-cloud instances with strong logging/export controls (V2) and VM/OS posture (V3).
8
Privacy-paranoid Startups / Indies
Developer sandbox. Excellent feedback for single-GPU and BYO-cloud workflows, but low budgets.
Useful to stress-test usability of V2/V3 governance without making it the long-term revenue core.
9
High-confidentiality Civil / Religious / NGOs
Opportunistic and curated. Aligns with ethical/humanitarian constraints; useful testbeds for sovereign document AI and
strict artifact governance—often with mixed AI and non-AI workloads where V3 matters as much as V2.
10
Cloud & Hardware Vendors
Long-term platform licensing: hardened runtimes, invariant-first accelerators, and “twin-model” infrastructure.
High upside once patents, reference deployments, and independent review exist; not a first customer segment for a one-person lab.
11
Academic / Non-Profit Research Labs
Collaboration and credibility. Valuable for science, external review, and λ-Stack research leverage—typically not the direct revenue driver.
Fit improves when evaluation harnesses are mature and NDA workflows are streamlined.
What you actually get from Logarchéon
This is not generic “AI consulting.” The offerings are opinionated: they assume high-assurance threat models,
single-GPU or modest-server constraints for early deployments, and a zero-knowledge vendor posture by default.
Sandboxes and Tier I missions run on the same λ-secure backbone—versioned as V1/V2/V3.
Track 1 · V1/V2 · λ-secure private models
Private GPT-style models, wrapped or λ-native
V2 (wrapper): start from a compact or mid-scale base model and tune it inside the λ-secure runtime on your data,
preserving your existing tooling and model family where feasible.
V1 (λ-native): commission a model trained from scratch for your domain where the geometry and learning dynamics are
built into the architecture (integration-first, maximal control).
Designed to run on your hardware or in your cloud tenancy (BYO-cloud instances),
with a clear scaling path as your GPU footprint grows.
Outcome
A concrete, working λ-secure assistant on your hardware or cloud instances—either a wrapped model you already trust,
or a new λ-native model—demonstrating encrypted-in-use AI under buyer-held governance.
Track 2 · V2/V3 · Runtime & governance
λ-secure runtime for your whole stack: models + instances
A deployment-ready runtime envelope designed to host multiple models under a single governance posture.
V2 (models): wrap NN/LLM execution paths so sensitive representations are less likely to appear as reusable canonical artifacts.
V3 (MIA posture): extend “in-use” discipline to VM/OS/runtime boundaries in both on-prem and cloud-instance deployments
(snapshot risk, logging surfaces, admin planes, and artifact lifecycle control).
Supports multi-agent workflows, red/blue analysis, and specialty models (code, search, domain LMs) under consistent policy gates.
Outcome
A unified λ-secure control plane for your AI portfolio and runtime placement (on-prem or BYO-cloud),
with explicit keys, policy gates, and interface discipline suitable for high-assurance review.
Future · Hardware
MIA appliance — λ-secure compute box (AI + non-AI)
A hardened appliance path targeting invariant-first compute on FPGAs and/or mature-node silicon.
Designed for OT, forward-deployed, and controlled environments where “cloud” is not an option
(or is an unacceptable trust boundary).
Maintains the same governance and “in-use” posture so policies, evidence, and operational controls transfer from software deployments.
Outcome
A physical λ-secure endpoint you can rack, power, and integrate where hardware-grade assurance and controlled operations are mandatory.
Plain language: V2 secures the model execution path; V3 secures the instance boundary (VM/OS/runtime) where cloud and ops risk often live.
V1 is the deeper “from-scratch” option when architecture-level integration is worth it.
Beachhead: law & privacy-first founders
Early deployments focus on environments where confidentiality, single-GPU footprints, and fast iteration matter:
law practices and privacy-first teams. These stress-test the λ-secure stack in real deployments and generate the references
needed for higher-assurance programs in national security, defense, critical infrastructure, and systemic finance.
Sandbox A · Law firms & in-house legal
“We can’t upload privileged material to random LLM APIs.”
Client confidentiality, privilege, and bar ethics make public LLM APIs a non-starter.
You need tools that live on your hardware or in your cloud tenancy,
with a defensible story about logs, exports, and operational interfaces.
You want more than “we trust the vendor” when explaining risk to partners, clients, and insurers—especially when workloads run on cloud instances.
Why this segment first
Law has crisp, high-stakes confidentiality constraints and can adopt a single-GPU λ-secure assistant quickly.
It also surfaces V3-style risks early (cloud snapshots, admin planes, logging defaults), producing cleaner case studies for regulated enterprise and government.
Sandbox B · Privacy-first founders & small teams
“Our data is the moat; we refuse to send it to Big Tech models.”
You want local or self-hosted LLMs that run on a single GPU or small server, not a sprawling cluster.
You are willing to rent cloud GPUs—if the accounts, keys, and runtime posture remain under your control
(BYO-cloud instances, buyer-held keys, explicit logging/export policy).
You need an engine that constrains artifact reuse and reduces accidental “canonicalization” in debug/telemetry steps.
Why this segment first
Privacy-first founders move fast and are close to tooling. They are ideal partners to harden the
V2 (model wrapper) and V3 (instance boundary posture) before presenting the same primitives to higher-assurance programs.
Staged strategy: prove V2/V3 in fast sandboxes (law + privacy-first teams). Expand into regulated enterprise and infrastructure.
Then carry the same governance primitives into missions that demand hardware-backed encrypted-in-use compute and cryptomorphic twins at scale.
How it works: customer-governed secure execution across AI and conventional computing
Logarchéon is organized as a versioned deployment stack so buyers can choose adoption speed versus integration depth.
All versions share the same governance principle: customer-held secrets, explicit policy gates,
and constrained interfaces that reduce accidental “in-use” exposure.
Version map:
V1 = intrinsic GRAIL for NN/LLM · V2 = exported wrapper for NN/LLM · V3 = exported-wrapper posture for OS/VMs and Von Neumann workloads (compatibility-first).
Common control plane (all versions)
Customer custody + policy enforcement
Key custody: customer generates and retains operational secrets; vendor does not hold production keys by default.
Policy gates: export, logging, debug, and telemetry are controlled interfaces, not “helpful defaults.”
Rotation posture: keys can rotate per policy cadence to bound correlation and blast radius.
Interface discipline: reduce oracle creation through timing, verbose errors, embedding export, and admin endpoints.
Procurement language
This is a customer-governed execution framework with explicit assumptions and enforceable controls, suitable for high-assurance review.
Deployment envelope
Where it runs
On-prem: your hardware, your access governance, your monitoring discipline.
BYO cloud tenancy: GPUs and orchestration in your AWS/Azure/GCP account; you control IAM, logging, and networks.
Air-gapped / controlled: supported as a posture (subject to hardware realities and operational discipline).
Boundary
Logarchéon reduces “in-use” exposure; it does not replace physical security, EMSEC controls, or endpoint hardening.
V1 and V2 (AI): intrinsic versus exported wrapper
V1 · Intrinsic / λ-native GRAIL
Commissioned models with geometry built in
What it is: a λ-native architecture path where geometry is part of the model’s internal design.
Why choose it: maximal integration, principled semantics, and tighter control of “canonicalization creep.”
Buyer posture: suited for missions where the model itself is a long-lived sovereign asset.
V2 · Exported wrapper (NN/LLM)
Adoption-first for existing models and runtimes
What it is: a compatibility shell that wraps existing NN/LLM runtimes without requiring a full re-architecture.
Why choose it: fastest path to pilots; preserves existing toolchains and model families.
Security intent: reduce reusable plaintext exposure in “in-use” pipelines via constrained interfaces and protected representations.
What it is: a wrapper posture that brings existing OS/VM runtimes under the same “non-canonical in-use” discipline—without building a new substrate from scratch.
Core point: this is not “LLM-only”; it targets conventional Von Neumann workloads and machine-level leakage surfaces.
Adoption benefit: deployable across existing ecosystems; deepens later only if needed.
Formal framing
This architecture is designed to support high-assurance evaluations: explicit assumptions, bounded claims, and buyer-controlled governance.
Detailed evidence is available under NDA; public materials are intentionally non-enabling.
How to evaluate (high-assurance buyer workflow)
Evaluations support due diligence without publishing enabling details. Default posture: buyer-held keys, buyer-owned hardware or buyer cloud tenancy.
Stage 1
Threat-model alignment
Define the “in-use” boundary (CPU/GPU/runtime/telemetry).
Identify what must remain non-canonical in operation.
Operational reuse: replay/repurposing in a foreign runtime.
Oracle creation: leakage via logs, telemetry, debug endpoints, convenience exports.
Expanded TCB risk: environments where firmware layers cannot be fully audited.
Buyer responsibility
Key custody, interface control, and telemetry discipline are mandatory for the posture to hold in practice.
Compatibility note (V3): the exported-wrapper posture is designed to integrate with existing operating systems, hypervisors, and runtimes.
Deployment placement (user-space, kernel boundary, hypervisor boundary, or adjacent control-plane) is workload-dependent and defined by written acceptance criteria.
Security Q&A (operational, not marketing)
The Q&A below is retained verbatim unless an answer must change due to versioning updates (V1/V2/V3). New items are appended.
Q: Is this vaporware or a “scam”?
No. The operating rule is simple: no unbounded promises, and no fees justified by claims that cannot be tested. Work proceeds through written scope, explicit assumptions, and acceptance criteria.
What is sold: a bounded deliverable that can be evaluated in the customer’s environment.
What is not sold: belief, vague assurances, or guarantees untethered from deployment conditions.
What is public: non-enabling summaries and clear boundaries.
What is NDA-gated: substantiation and evaluation material appropriate for serious review.
In high-assurance work, credibility is earned the old way: clarity, restraint, and results.
Q: If someone steals the transformed index, can they still cluster it and learn “who is close to whom”?
They can compute numerical relationships inside what they stole. The missing step is what makes it useful: anchoring (IDs, labels, timestamps, filenames, provenance) and operational access (a compatible way to produce queries and validate hypotheses).
Without anchors and without operational access, they can produce patterns, but they cannot convert patterns into client-identifiable or action-driving intelligence.
Working rule: intelligence is not intelligence if you cannot anchor it.
Q: When you say “stolen storage, no interface,” what does “interface” mean?
An interface is any channel that lets an attacker test guesses or use the system the way your users do.
Query UI / API: “Send a question → get answers, scores, or nearest-neighbor results.”
Embedding / indexing pipeline access: “I can run the same internal steps your system uses to turn a query into a compatible vector.”
Verification oracle: any feedback loop that tells the attacker “your guess was correct” (e.g., outputs match known results).
In the stolen storage, no interface case, the attacker has a file dump of transformed artifacts, but no ability to run live queries, no ability to generate compatible query vectors, and no way to validate interpretations.
Q: If the attacker somehow gets the right key/transform, can’t they undo it and recover everything?
If customer-held secrets are truly recovered, then whatever was protected by those secrets is at risk. The remaining question is operational: what else did the attacker steal besides transformed numbers?
Transformed vectors are usually not the original documents. Undoing a transform gives you vectors again—still not the PDFs/emails—unless the raw documents were also stolen.
“Actionable intelligence” still requires anchoring. Without the mapping from “vector row” to “document ID / owner / timestamp / location,” recovered vectors may not translate into named, time-bounded intelligence.
Working rule: if you cannot anchor it, it is not intelligence.
Q: Concrete example (Agency-style): when is “even with the key” still not automatically actionable?
Operational systems often separate (i) documents, (ii) the vector index, and (iii) a mapping table linking rows to Doc-IDs, compartments, timestamps, etc.
Case 1: attacker steals only the vector index file
They can compute relationships like “row 2,487 is close to row 144,000.”
But without the mapping table, those rows have no identities, no timestamps, and no storage paths.
They cannot fetch documents, and they cannot name what they are looking at.
Case 2: attacker also obtains customer-held secrets
They may be able to undo some protections on the index.
But they still have “row 2,487,” not “Report AB-1776 / source X / date Y,” unless they also stole the mapping table.
They still cannot use the system like an analyst without a query interface and access paths.
What makes it truly dangerous
Anchors leak: mapping table or joinable metadata leaks.
Oracle leaks: attacker can query a live interface or harvest transformed queries from endpoints/logs.
Bottom line: the blast radius is determined by what leaks together: artifacts alone differ from artifacts + anchors + access.
Q: Why isn’t “structure leakage” automatically a breach?
Because “structure” is not a business outcome. It becomes a breach when an attacker can connect it to something real: names, cases, patients, assets, time, or a query interface that lets them validate guesses. This is why Logarchéon treats metadata containment and query-path control as first-class security requirements.
Q: What does Logarchéon explicitly NOT claim?
No claim of “semantic secrecy” if you leak metadata. If IDs, timestamps, labels, filenames, or join keys leak, risk increases by definition.
No claim of safety under endpoint compromise. If transformed query vectors leak from endpoints/logs, you have created an oracle-like channel.
No claim that standard cryptography becomes optional. Disk encryption, TLS, access control, and auditing remain mandatory.
No claim of “firmware elimination.” Logarchéon reduces reliance on trusted CPU state; it does not remove vendor firmware subsystems from existence.
Q: Can quantum computing “tunnel through” 256-bit keys?
Not in any generic, accepted model of symmetric-key search. The conservative generic quantum advantage is Grover-style speedup (roughly a square-root speedup), which is why the default posture targets 256-bit+ effective entropy. “Tunneling” is not a separate generic shortcut that collapses symmetric-key security.
Quantum note: Generic quantum search (Grover) can reduce effective symmetric-key security by roughly half (k → k/2). For this reason, Logarchéon targets 256-bit+ effective entropy to retain ~128-bit-class security even in a conservative quantum model.
Q: What does “256-bit+” actually buy us (brute-force context)?
This is an illustrative brute-force table for an offline attacker who can try
1018 guesses/second. Real-world failures are usually dominated by
key theft, endpoint compromise, or oracle creation—not raw key search.
Effective entropy
Expected brute-force time
Deployment posture
128-bit
~ 5.39 × 1012 years
Already beyond brute force in practice; still may be insufficient margin for long-lived, multi-tenant, high-assurance postures.
192-bit
~ 9.95 × 1031 years
Conservative option for long-lived deployments.
256-bit+default target
~ 1.83 × 1051 years
Margin for worst-case assumptions, long retention periods, and compartmented multi-tenant governance.
Quantum note
Generic quantum search (Grover-style) yields an approximate square-root speedup, often summarized as
k → k/2 for symmetric-key brute-force. This is one reason high-assurance deployments
default to 256-bit+ effective entropy.
Q: What operational controls are required for the claim to hold?
Tenant-unique keys + rotation (limit correlation and blast radius).
Keep anchors separate (mapping tables, provenance metadata, join keys).
No embedding/hidden-state export outside the customer trust boundary (treat transformed representations as sensitive).
Harden endpoints like credentials (least privilege, minimal telemetry, enclaves where appropriate).
Strict metadata containment (IDs/timestamps/labels are often the real leak).
Interface discipline: prevent accidental oracle creation via debug endpoints, verbose logs, or “helpful” admin tooling.
Q: If an insider leaks a key, is everything over?
Key leakage is serious. The goal is to avoid “one mistake equals total compromise” via blast-radius control: tenant-unique keys, key rotation, and optional bulkhead mode (compartmentalized processing for extremely sensitive workloads).
Bulkhead mode is the operational analogue of watertight compartments: you can structure processing so that a single compromised key is not automatically a full historical compromise.
Q: What are V1, V2, and V3 (and why does it matter)?
V1 (Intrinsic / λ-native GRAIL): geometry is built into the model architecture; commissioned builds for maximal integration.
V3 (Exported-wrapper posture): wraps existing OS/VM/runtime stacks and conventional workloads at the runtime boundary (compatibility-first).
Why it matters: the “how you deploy” choice determines adoption time, integration depth, and which parts of your existing stack remain unchanged.
Q: Does the V3 exported-wrapper posture apply only to AI workloads?
No. V3 is explicitly framed for conventional Von Neumann workloads (processes, OS/VM boundaries, storage/memory/I/O). AI is a motivating case because it concentrates “in-use” secrets; the same leakage logic exists in non-AI computation.
Q: Do we have to replace our OS, hypervisor, or container stack for V3?
No. V3 is explicitly designed as a wrapper posture so you can keep your existing OS/VM stack and harden “in-use” exposure paths around it. The exact integration boundary is chosen to match your threat model and performance constraints.
Q: Can the exported-wrapper approach cover storage, RAM, and cache layers?
In scope, the objective is system-level containment of sensitive representations across compute and memory surfaces (storage, RAM, caches), subject to deployment constraints and explicit acceptance criteria. Implementation detail is NDA-gated.
Q: Can V3 apply to GPUs and accelerators?
Where accelerators participate in “in-use” state (VRAM, kernel launches, driver queues), the posture is to treat those surfaces as part of the boundary and constrain interfaces accordingly. The exact coverage depends on the accelerator stack and the buyer’s operational constraints.
Q: How do you prevent generative-AI-assisted reverse engineering of the runtime?
Public documentation is intentionally non-enabling; evaluation builds are NDA-gated; and production deployments are structured so that exfiltrated artifacts alone are not a drop-in, reusable system without customer-held secrets and controlled interfaces.
Q: What defense-in-depth options exist beyond key custody and rotation?
Defense-in-depth options
Two extra layers buyers actually use
Removable redundancy (optional): embeddings/models can be carried in a higher-dimensional working
representation with removable redundancy. Customers keep the subspace definition; redundancy can be stripped on
retrieval under policy. This is sold as defense-in-depth, not as a sole security guarantee.
Bulkhead mode (compartmented twins): for extreme sensitivity, customers can train and move workloads
in small, isolated increments (separate compartments, like a ship). Even under an insider key leak, the exposed portion
is bounded to the compartment involved at that step.
Blast-radius control
The goal is not perfection; it is bounded damage under realistic failure modes.
Q: What are the most common ways a deployment accidentally breaks its own posture?
Telemetry overreach: logging transformed intermediates, debug dumps, or embedding exports.
Oracle creation: exposing a query endpoint that lets attackers validate guesses at scale.
Anchor leakage: leaking joinable metadata (IDs, timestamps, mapping tables) that makes patterns actionable.
Key sprawl: shared credentials, weak rotation discipline, or unmanaged backups of secrets.
Legal note
This site is informational and not legal advice. Any deliverable, performance expectation, or security posture is governed solely by a written agreement, including stated assumptions and acceptance criteria.
Why this is different from typical “private GPT” offerings
Common patterns
Cloud LLM SaaS: Data is “protected by policy,” but runs in plaintext inside someone else’s stack.
On-prem legal/enterprise AI: Runs inside your network, but the vendor still sees a canonical model and often plaintext data when supporting you.
DIY local LLMs: Full control, but you own all the complexity and there is no formal obfuscation or twin-deployment story.
Logarchéon’s posture
Single-GPU friendly: Compact and mid-scale λ-secure deployments that work on a single RTX-class GPU or modest server, with a clear scaling path as your hardware grows.
Model-agnostic: The λ-secure runtime is designed to wrap the LLMs you already rely on—open-source or licensed—and can also support λ-native models trained from scratch.
Zero-knowledge vendor stance: Logarchéon designs the geometry and runtime; you keep the keys, own the cloud accounts, and control both weights and logs.
By-design BYO-cloud: Everything runs in your AWS/Azure/GCP tenancy or on-prem. Production access is your choice; the default is that Logarchéon does not see your workloads.
Under the hood: CEAS, finite lift, GRAIL, and MIA
The landing page is simple on purpose. Underneath, the stack draws on original work in geometry, symbolic dynamics,
and secure computation. The emphasis is: interpretable dynamics, encrypted-in-use execution, and invariant-first hardware.
Core pillars
CEAS: controlled attention scaling aimed at fewer training steps and improved stability, especially in long-sequence regimes.
Finite-machine lift: decomposes model behavior into interpretable components for traceability and safe edit-without-full-retrain.
GRAIL: geometry-native secure execution aimed at cryptomorphic (function-identical, weight-distinct) twins and encrypted-in-use computation.
MIA: invariant-first architecture suitable for FPGAs and mature-node silicon.
Where to read more
If you are a technical reviewer, cryptographer, or ML researcher and want the math, proofs, and prototypes:
Visit the Research page for notes, slides, and code snippets.
Or email for non-enabling technical briefs and NDA-gated materials where appropriate.
Expectation
Public write-ups are intentionally non-enabling. Detailed materials are shared under NDA and additional review where appropriate.
Who is behind Logarchéon?
I’m William Huanshan Chuang, a mathematician and founder of Logarchéon Inc.,
a one-human C-Corporation structured as an IP-first research lab. My work sits at the seam
of geometry, control, cryptography, and AI. I use recursive teams of AI agents to explore design space;
proofs, counterexamples, and national-security ethics decide which ideas survive.
If you want the full story—formation, research, teaching, and vocation—see the
About page, Research index, and
resume.
Next steps
If you work in national security, defense, systemic finance, critical infrastructure, or run a high-confidentiality
environment—and you want private AI that respects both your threat model and your conscience—the next step is simple:
start a quiet conversation. The same applies if you are a law firm or privacy-first founder who wants to be an early sandbox.
Typical starting points
A 30–45 minute briefing on your mission, privacy constraints, and hardware; then a scoped, single-GPU proof-of-concept (under NDA)
that lives on your hardware or in your own cloud tenancy.
IP, review, and boundaries
Some materials relate to active or planned U.S. filings. Public descriptions on this site are
non-enabling summaries. Technical evaluations proceed under NDA and additional
compliance review where appropriate. Independent cryptographic review and red-teaming are welcome under
appropriate safeguards and with attention to national-security implications.