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S3 · AI Security Testing · L4 SCALE

Enterprise AI Security Framework

Make AI security testing an owned, reviewable operating model across teams: risk map, control library, ownership and staged adoption.

Best for: Security and engineering leaders who govern AI systems across teams and want each person's responsibility to be visible to the whole team.

$149 one-time · includes L0-L4 of S3 · AI Security Testing

L4 SCALE is $50 more than L3 and adds a security framework blueprint, a control library, an ownership matrix and an adoption plan; every level below it is included.

Get the complete series (L4 SCALE) for $149 · our storeSee the whole series

Paid purchases: 7-day money-back guarantee and at least six updates a year for five years, as set out in the terms.

What you paid for any lower level of the same series counts toward any higher level (L1 START at $19 makes L4 SCALE cost $130). To claim it, email hello@nobrainer.tech with your receipt (how the credit works).

Need copies for colleagues? Each person gets their own copy; email hello@nobrainer.tech.

S3 · AI Security Testing L4 SCALE: Enterprise AI Security Framework, product artwork

You walk away with

  • A security framework blueprint, a control library, an ownership matrix and an adoption plan, plus one HTML report.
  • Templates for a governance charter, quarterly review checklist, exception register, evidence-freshness tracker and testing-authorization record.
  • An ed-tech example run before and after a change, in English and Polish.
  • CI and pre-commit examples, an 89-page field guide and 68 automated tests.
  • The complete series: every earlier workflow of AI Security Testing is inside this level.

What the workflow does

Map risk ownership, define control and test libraries, establish monitoring gates, and stage adoption with rollback.

Bring

  • AI system portfolio
  • risk and control taxonomy
  • ownership and delivery constraints

Leave with

  • security framework blueprint
  • control library
  • ownership matrix
  • adoption plan

What is in the package

Complete series, L0-L4 of S3 · AI Security Testing: Initial LLM Boundary Review Workflow, Authorized Prompt Injection Review, LLM Security Review, AI Security Test Implementation Workflow and Enterprise AI Security Framework.

You need: Node.js 22 and a terminal. The included L1 folder runs on Python 3.10 or newer.

Each package is a working workflow with tested code, a runnable example, recovery steps and an HTML report, plus an English and Polish guidebook that explains the method. The workflow is the product; the guidebook supports it.

Prices are cumulative: each price covers that level and every level below it, so the catalog shows no sums or savings.

What the guidebook covers

  • enterprise AI-security portfolio view
  • versioned asset and dependency registry
  • decision rights and policy contracts
  • control ownership and monitoring
  • incident response and adoption

The guidebook extends the workflow with the reasoning and adaptation guidance. It is not evidence that a result has already run in your environment.

Use it safely

Use sanitized inputs inside a declared boundary and review every proposed action. The workflow stops when evidence, authority or an owner decision is missing.

The included code runs on your machine, makes no model calls and needs no AI tool. If you want an assistant to help draft the input, the onboarding guide shows how; you review the result before anything is acted on.

One exception: the authorized check runner in this package sends only the inert checks you wrote to the one endpoint you name, inside your written authorization window and boundary. By default that endpoint is a stub that ships in the package.

Where this fits

L4 SCALE is the top level of this series and includes every level below it.

You have the complete AI Security Testing series. The natural next series is AI Engineering & Orchestration, which limits what your AI agents may touch in the first place.

See all five levels of S3 · AI Security Testing · Compare all five series · See S4 · AI Engineering & Orchestration

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