Defense / Securing AI
Every AI request, observed and controlled end to end
Alethe Labs wraps AI systems in a verifiable assurance pipeline — authenticated inputs, a sanitization API server, data tagging, policy guardrails, output filtering, and continuous telemetry — with every decision written to an evidence ledger. Explore the architecture below.
Inputs
Sanitized
Dedicated API server screens every request
Data
Tagged
Sensitivity and handling labels enforced
Outputs
Filtered
Egress screening before release
Evidence
Verifiable
Append-only provenance ledger
Interactive Architecture
This is how AI is monitored across the pipeline. Select any node to inspect sample telemetry from that control point.
AI Assurance Pipeline
Select any node to inspect sample telemetry
The Assurance Pipeline
Sanitize before the model sees anything
A dedicated API server sits in front of inference. Every prompt, upload, and tool call is normalized and screened for prompt injection, jailbreaks, and obfuscated payloads.
Injection defense
Signature and heuristic screening for prompt-injection and jailbreak attempts.
Normalization
Decode and canonicalize inputs so obfuscated attacks cannot slip through.
PII awareness
Detect and redact sensitive fields before they reach the model.
Tag the data, enforce on the tag
Inputs and retrieved context are classified by sensitivity, origin, and handling caveat. Policy decisions enforce on the label — not a guess.
Classification
Automatic labeling of sensitivity and handling caveats such as CUI and NOFORN.
Provenance
Every tag records where the data came from and how it may be used.
Tag-aware routing
Sensitive requests route to the appropriate controlled boundary.
Constrain inference inside the boundary
Inference runs against an approved model within the controlled boundary, bounded by the policy decision and accompanied by the active data tags.
Approved models
Only vetted models run, in an air-gapped or controlled enclave.
Policy constraints
The decision engine's verdict shapes what the model is allowed to do.
Grounded responses
Outputs are tied to tagged source context for verifiable citations.
Filter outputs and prove every step
Model output is screened before it leaves the boundary, and every decision is written to an append-only evidence ledger ready for your authorization package.
Egress screening
Block classification spillage, leaked secrets, and unsafe content.
Continuous telemetry
A monitoring plane watches every stage for anomalies and abuse.
Audit-ready evidence
Immutable, hash-chained records suitable for OSCAL evidence.
Bring verifiable assurance to your AI systems
The same controls you explored here — input sanitization, data tagging, policy guardrails, and output filtering — deploy inside your boundary and feed Narsil's evidence pipeline, so every AI interaction arrives documented for authorization.