SERVICES · DEFENSIVE

Neural Hardening

Production-grade hardening of the infrastructure that runs your AI — sandboxing, firewalls, cost guards, and credential isolation.

Neural Hardening is the infrastructure-defense engagement. While other engagements focus on the model, the prompts, or the agents, this one focuses on the substrate underneath: sandboxes for inference processes, firewall rules around model APIs, rate limits and cost guards against runaway loops, and credential isolation between agents and downstream systems. The work is conventional infrastructure security applied carefully to AI-specific risk.

// THE PROBLEM

What we're solving when you hire us for this

AI systems share an inference infrastructure with other production workloads — and that infrastructure is often the weakest part of the deployment. A jailbroken model running in an over-privileged container can reach internal databases. An agent without rate limits can burn through API credits in hours. A model endpoint exposed without firewall rules invites scraping and abuse. These are conventional infrastructure problems that conventional security teams often miss because the system is 'an AI thing.'

Neural Hardening addresses these gaps with infrastructure-engineer rigor. We sandbox inference processes, configure least-privilege credentials, install cost guards against runaway agents, and tighten network boundaries around model APIs. The work is unglamorous and unfakeable — production infrastructure either holds up under attack or it doesn't.

// HOW WE RUN IT

The five phases of a Neural Hardening engagement

Infrastructure Audit

We document the deployment topology: where models run, how they're invoked, what credentials they hold, what networks they reach, and what cost ceilings (if any) constrain them. Read-only audit; no changes yet.

Duration 3–5 days · Output: topology map

Threat Modeling

Against the topology, we identify the realistic threat vectors: container escape, credential exposure, cost runaway, network exfiltration, supply-chain compromise of model weights or dependencies.

Duration 2–3 days · Output: threat model

Hardening Design

We design the hardening interventions: sandbox configurations, network policies, credential rotation, rate limits, cost ceilings, dependency pinning. Each intervention is justified against a specific threat vector.

Duration 3–4 days · Output: design document + approval gate

Implementation

We work with your infrastructure team to deploy the hardening — either directly or in advisory mode. Implementation includes monitoring infrastructure so your team can detect when boundaries are tested.

Duration 7–10 days · Output: deployed hardening + monitoring

Validation & Handoff

We attempt to bypass the hardening to verify it holds. Findings from validation either confirm the hardening or trigger additional tightening. Final deliverable includes a runbook for ongoing maintenance.

Duration 4–5 days · Output: validation report + runbook

// WHAT YOU RECEIVE

Deliverables, named and specific

Infrastructure Topology Map

Complete map of your AI inference infrastructure: hosts, networks, credentials, and dependencies. Useful as ongoing reference as your deployment evolves.

20–30 pages · Markdown + diagram

Threat Model

Realistic infrastructure threats against your deployment, with severity and likelihood ratings tied to your specific topology.

Threat model document

Hardening Design

Each intervention specified: sandbox configurations, network policies, credential rotation schedules, and cost ceilings — reviewed and approved before implementation.

Design document + configuration

Implementation Artifacts

Deployed configurations, policy code, and monitoring rules — committed directly to your repositories or delivered as patches your team applies.

Code + configuration

Boundary Monitoring

Alerts for hardening-boundary tests: failed escape attempts, credential misuse, rate-limit hits, and cost-ceiling approaches.

Monitoring rules + alerting

Operations Runbook

Documentation for maintaining hardening as your AI infrastructure evolves — what to review when adding new models, agents, or integrations.

Runbook + playbooks

// ENGAGEMENT SHAPE

Specific numbers, not approximations

// DURATION

3–5 weeks

Implementation phase varies

// TEAM SIZE

2 practitioners

Minimum, both senior

// CADENCE

Daily async updates

By 18:00 client timezone

// ENGAGEMENT MODE

Advisory or implementer

Per SOW preference

// SCOPE

Per-deployment

Written in SOW

// STARTING PRICE

$22,500

Advisory mode; implementation higher

// ARTIFACT DELIVERY

Continuous

Code committed as designed

// RUNBOOK HANDOFF

End of engagement

Includes 30-day support

// WHEN THIS IS RIGHT

Honest fit criteria

// THE RIGHT FIT

—

Your AI runs on infrastructure your team controls (self-hosted models, cloud VMs, internal Kubernetes) and you need it hardened for production.

—

You've passed a model-level audit but your infrastructure-level posture is unclear.

—

Cost runaways or credential exposures have happened — or nearly happened — and you need engineering-grade prevention.

—

Compliance requirements (SOC 2, ISO 27001, EU AI Act technical controls) demand documented infrastructure hardening.

// THE WRONG FIT

—

Your AI is entirely on hosted APIs (you only call OpenAI or Anthropic) — most hardening doesn't apply since the infrastructure isn't yours.

—

You haven't deployed the AI to production yet — hardening real infrastructure requires real infrastructure.

—

Your infrastructure team can't make changes during the engagement — implementation requires their active participation.

—

You're looking for general infrastructure security — generalist firms cover this work cheaper than we do.

// RELATED ENGAGEMENTS

Where this connects to the rest of our work

Agentic Guardrails

Companion engagement — Neural Hardening secures the infrastructure, Agentic Guardrails constrains agent behavior on top.

DETAILS →

RAG Perimeter

If retrieval is in your stack, this engagement hardens the document-ingest and vector-store layer specifically.

DETAILS →

Adversarial Probing

Pairs well as the offensive counterpart — probing finds weaknesses in the running system, hardening closes them at the infrastructure layer.

DETAILS →

Neural Hardening engagements start from $22,500. Reply within 24h. NDA before scope.

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