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On this page Where to start All prices Verification audits AI system security After an incident Control engineering AI crawl forensics Terms FAQ Contact
// where to start

Four steps. You can stop at any of them.

You do not have to commission a full audit to find out whether you need one. Each step gives a result that stands on its own and answers one question: is the next step worth it? If it is not, I say so.

  1. 01 · free

    Talk, test, sample

    Find out whether going further makes sense, with no commitment.

    Scoping call · 30 min free Audit kit, run it yourself free AI bot log sample free
  2. 02 · quick review

    Quick review

    A paid start that fits a budget without board approval. The rapid review is credited against a full audit.

    Rapid AI architecture review €1,400 Vendor claim audit from €2,300
  3. 03 · full audit

    One system, fully diagnosed

    A report for the engineers and a short one for management.

    AI system architecture audit from €4,700 Applied architecture audit from €4,700 AI agent security audit from €7,000 Verification layer review from €7,000 AI Crawl Audit from €2,500
  4. 04 · build & care

    Build the control layer

    Build what the audit found missing, and keep the result from drifting.

    Closed-loop control engineering from €14,000 Citation Architecture from €7,500 AI visibility care from €2,500/mo

Which one fits? Two questions.

Pick what you are working with and what you need first. The answer is one service, with its price.

1 · What are you working with?
2 · What do you need first?
// all prices

Every price in one table. Net, no small print.

“From” means the typical scope: one system, a standard number of integrations, access to documentation and logs. The final price is fixed in writing before work starts and does not change unless you change the scope. What moves the price.

All services · prices in EUR, net of VAT · as of 30 September 2026
ServicePriceTimeMain result
Start
Scoping callfree30 minutesWhether an audit makes sense, and which scope
Audit kit (three-prompt protocol)freeabout 10 minutes, run by youA first reading of any model you use
AI bot classification samplefreeup to 5 working daysBot classification report for seven days of logs
Rapid AI architecture review€1,4003 working daysArchitecture diagram and the ten most important risks
AI verification audits
Vendor claim auditfrom €2,300approx. 5 working daysClaimed versus observed behaviour, with transcripts
Applied architecture auditfrom €4,700approx. 10 working daysYour system against the seven-model published baseline
Verification layer reviewfrom €7,000approx. 3 weeksMap of every point where the system verifies itself
AI system security
AI system architecture auditfrom €4,700approx. 10 working daysTechnical report and a risk report for management
AI agent security auditfrom €7,000approx. 15 working daysReproducible attack scenarios and a permission matrix
After an AI incident
Urgent AI incident analysisfrom €2,3005 working days, first findings in 24 hoursTimeline of the incident and immediate actions
Full post-incident reportfrom €4,700approx. 10 working daysEvidence-based report and a repair architecture
Verification & control engineering
Closed-loop control engineeringfrom €14,00030–45 working daysA deployed verification layer and regression tests
AI crawl forensics (GEO)
AI Crawl Auditfrom €2,500approx. 10 working daysBot classification, spoofing report, citation map
Citation Architecturefrom €7,5004–6 weeksDeployed changes, measured before and after at day 30
AI visibility carefrom €2,500 / monthmonthly, cancel any monthMonthly report and one deployed fix
// line 1 · ai verification audits

A system that verifies its own output has not been verified

An agent marks a task complete. A model reports that it consulted a source. A pipeline flags an output as checked. In each case something in your stack has produced a claim about its own work — and in most stacks, nothing else is in a position to contradict it.

This is not a bug in a particular model. It is what happens when a system is optimised against approval signals rather than against measurement. A model rewarded for producing satisfactory answers will produce satisfactory answers, including about itself. That behaviour is the optimum of the objective, not a defect in it — which is why it does not disappear as models improve, and why longer reasoning chains make it harder to spot rather than easier.

Two questions decide whether you have a real problem, and both are answerable without model weights, training data, or production credentials:

  1. Reference. Is there a specification of the state the system is supposed to reach — or only a proxy that has quietly replaced it?
  2. Channel. Is there an independent path by which the system's own report can be contradicted — or does verification close back on the thing being verified?

Those two questions are the whole method. They come from Perceptual Control Theory, the framework of William T. Powers; applying them to AI systems is my own extension, published in full. You can run the first version yourself, free, in about ten minutes.

01 // vendor claim audit

Does the model do what its provider says it does?

from €2,300 · fixed · approx. 5 working days
ForTeams evaluating a model or AI vendor before deployment, or after a capability claim stopped matching observed behaviour
What happensDocumented architectural claims are collected, then tested against the deployed model through its normal interface — clean contexts, structured questioning, no jailbreaks and no adversarial tricks
Access neededNone beyond ordinary user access to the model
You receiveFull transcripts, a claim-versus-observed-behaviour table, a written verdict, and an explicit statement of what the audit could not establish
PrecedentGemini 3.5 Thinking, launch-day audit — published in full
02 // applied architecture audit

The published protocol, run against your system

from €4,700 · fixed · approx. 10 working days
ForTeams running models in production where a wrong answer that looks confident carries a cost
What happensThe three-prompt protocol from the published study is run against your models, your system prompts and your deployment context, across clean and loaded contexts, and the results are interpreted rather than merely reported
Access neededAccess to the deployed system as an ordinary user; system prompts if you are willing to share them
You receiveSession logs, a comparison against the seven-model published baseline, the specific places where your configuration widens or narrows the gap, and prioritised findings
Free firstRun the unpaid version yourself before commissioning this. If it returns nothing that concerns you, do not buy the paid one.
03 // verification layer review

Where does your pipeline check itself against itself?

from €7,000 · fixed · approx. 3 weeks
ForTeams running agents or multi-step pipelines in which one component reports on the work of another
What happensEach verification step is traced to its source. Where a check terminates inside the same model, or inside a second model, it is recorded as a closed loop rather than an independent channel. The same two questions, applied to architecture rather than to a single model
Access neededArchitecture documentation and interviews with the engineers who built it. No code, no weights, no production credentials
You receiveA map of every point at which the system verifies itself, ranked by what a false “verified” would cost you there, plus the specific checks that would break each loop open
Method basisThe Metric Was Green — thirty documented systems, published with DOI
Worked example · 20 May 2026

Google released Gemini 3.5 with an “Extended Thinking” mode, described as deeper reasoning with internal self-correction. Asked how the mode worked, the model described a closed feedback loop with an internal critic that checks each reasoning step. In a clean context window, with no leading terminology, the same model was asked the same questions in binary form: no separate critic subprocess, no error-minimisation mechanism during inference. Every element of the first answer, denied by the same model seven minutes later.

No access to Google's infrastructure was needed. The deliverable is not an opinion about the vendor, but a transcript in which the system contradicts its own description. Read the full case study.

// line 2 · ai system security

How your AI system really works — and where control is missing

Audits of architecture, agents and data flows. The result is documents you can check, not a slide deck.

quick start // rapid ai architecture review

Where are the three biggest risks in your AI system?

€1,400 · fixed · 3 working days
ForTeams with one deployed AI system (chatbot, assistant, RAG, agent) who need a fast, independent assessment before deciding on a larger audit
What happensDocumentation review and a 90-minute technical session with your team. I map the components, trust boundaries and data flows of one system and point to the places with no control layer
Access neededArchitecture documentation (or a written description) and 90 minutes of your technical team online
You receiveArchitecture diagram, the ten most important risks with priorities, mapping to the OWASP Top 10 for LLM Applications, and a clear recommendation on whether a full audit makes sense
Payment100% upfront. The amount is credited against a full audit ordered within 60 days
04 // ai system architecture audit

How does your AI system work underneath?

from €4,700 · fixed · approx. 10 working days
ForOrganisations with a deployed AI system who do not know exactly how it works underneath, or who are preparing for the EU AI Act, a client audit or an investor
What happensA full map of architecture and data flows, trust boundaries, a permission matrix, analysis of prompt and context exposure, an attack-surface register, mapping to the OWASP Top 10 for LLM Applications
Access neededDocumentation, read access to configuration and logs (or an export), two or three sessions with your technical team
You receiveA technical report, a two-to-three-page risk report for management, a register of findings with priorities, and a repair architecture
05 // ai agent security audit

Can your agent do more than it should?

from €7,000 · fixed · approx. 15 working days
ForAgent systems with access to tools, APIs, databases, email, CRM or payments
What happensLeast privilege, direct and indirect prompt-injection tests, action authorisation and approval gates, privilege escalation, behaviour in autonomous mode, auditability and telemetry, red-team scenarios
Access neededA test or staging environment for the agent, documentation of its tools and permissions, and written consent to the tests
You receiveA test report with reproducible scenarios, an “is / should be” permission matrix, a prioritised list of findings, and a design for approval gates
// line 3 · after an ai incident

Reconstructed from the records, not from the model's explanation

An agent took an action it should not have. A model disclosed data. A system gave a customer false information. I reconstruct what happened from the system's own records. A model's account of its own behaviour is not evidence: asked for a cause, a model can produce a convincing story that is false — the same model can describe its architecture one way and deny it minutes later.

In Polish Nine publicly documented AI incidents in Poland, each with an evidence card, are collected in the ais://lab incident register.

06 // urgent ai incident analysis

What actually happened — and can it happen again tomorrow?

from €2,300 · fixed · 5 working days · first findings in 24 hours
ForOrganisations where an AI system has just caused harm or a real risk: a data leak, an unauthorised agent action, false information given to a customer
What happensSecuring the material (copies of logs with checksums, before they are overwritten), reconstructing the incident from logs, prompts, context and retrieval (RAG) results, a preliminary finding on the cause and a list of immediate actions
Access neededApplication logs, agent action logs, conversation history and system configuration from the period of the incident; contact with someone who knows the system
You receiveWritten first findings within 24 hours of receiving the material; a report within 5 working days: timeline of the incident, probable cause with a stated level of confidence, list of immediate actions
StartWithin 24 hours of the signed order. No surcharge for urgency
07 // full post-incident report

Which control layer was missing — and how to build it?

from €4,700 · fixed · approx. 10 working days
ForOrganisations that must document an incident for a board, an insurer or in proceedings, and want to be sure it does not return in another form
What happensThe full urgent analysis, plus separation of hypotheses (model error, prompt injection, integration error, excessive permissions) on the basis of the records, identification of the missing control layer, and a repair architecture
Access neededAs for the urgent analysis, plus architecture documentation and two or three sessions with your technical team
You receiveA technical report with evidence and confidence levels, a summary for management, a repair architecture, and the tests that will confirm the repair works. The report is not a legal opinion
CreditThe price of the urgent analysis is credited if you order the full report within 30 days
// line 4 · verification & control engineering

A system that detects its own errors

Designing and building the layer that catches a model's errors without access to “the truth”, following Reference Signal Engineering — my own extension of Powers' Perceptual Control Theory to AI systems. This is the one engagement in which I build, not only audit.

08 // closed-loop control engineering

How do you build a system that catches its own errors?

from €14,000 · fixed · 30–45 working days
ForTeams who want to build a verification layer from scratch, where a wrong model answer carries a legal, financial or medical cost
What happensSpecification of reference signals and controlled variables, architecture of an independent comparator, independent record channels for each type of claim, error-signal design, an action-authorisation layer, failure states, adversarial tests, telemetry
Access neededWorking with your engineering team; access to a development environment
You receiveAn architecture specification, a deployed verification layer (built with your team or handed over for deployment), a regression test suite, and deployment documentation
Payment50% upfront, 50% within 30 days of acceptance
// line 5 · ai crawl forensics (geo)

Who really reads your site: AI bots, fake AI bots, attackers

Raw server logs instead of asking chatbots questions. Three services — AI Crawl Audit (from €2,500), Citation Architecture (from €7,500) and AI visibility care (from €2,500 / month) — plus a free seven-day log sample. The method, the evidence from two live domains and the full service descriptions are on their own page.

AI crawl forensics (GEO) Free log sample

// price and terms

What moves the price. And how I work.

What raises the price above “from”

  • Number of systems or agents. The base price covers one.
  • Number of integrations and tools the system can reach: CRM, email, payments, databases.
  • Missing documentation, when the architecture has to be reconstructed from code and configuration.
  • No test environment. Testing in production needs additional safeguards.
  • A report in two languages (English and Polish).

Terms

  • Net prices. VAT is added where it applies.
  • Fixed price, agreed in writing before work starts. It changes only if you change the scope. Never hourly.
  • Payment: 50% upfront, 50% by bank transfer within 30 days of the report. Rapid review: 100% upfront. AI visibility care: monthly in advance.
  • Credits: the rapid review is credited against a full audit ordered within 60 days; the urgent incident analysis against a full post-incident report ordered within 30 days.
  • NDA on request, before any material is shared.
  • Confidentiality: findings from commercial work are never published without your written consent, and then only anonymised.
  • Tests only with the system owner's written consent.
  • Evidence commitment: every commercial audit ends with a dated, signed commitment to its evidence: a single hash (the root of a Merkle tree) over all transcripts and logs. With your consent it is published; it reveals no content and names neither you nor the system. Any single item can later be shown, with proof that it belongs to the audited set.
If it will not help, I say so

If during scoping it becomes clear the engagement will not tell you anything useful — no logs, a closed architecture, a question outside my competence — I say so and we stop there. That conversation is free.

// what i don't do

The boundary is part of the offer

An offer that promises everything cannot be checked on any of it. If one of these is what you need, I will say so and point to the right kind of supplier rather than take the work.

// intellectual honesty

What the underlying framework cannot yet do

A framework that hides its weaknesses is not science. These are the three known gaps in Perceptual Control Theory, published here because anyone considering an engagement deserves them upfront rather than after signing.

Gap 1 — Neural mapping is incomplete

Powers proposed an eleven-level perceptual hierarchy. Only levels 1–4 have been experimentally validated with tracking tasks (Marken, 2014). The higher levels are theoretically coherent but lack direct neurophysiological mapping. The hierarchy works behaviourally; the brain scans proving each level maps to a distinct circuit do not yet exist.

Status: active research area

Gap 2 — Reorganization is under-specified

PCT explains how you control perception once the hierarchy exists. It does not yet have a complete mathematical model of how the hierarchy forms. Powers named this reorganization and described its principles, but the formal dynamics are still being developed.

Status: the single biggest gap in the framework

Gap 3 — Scaling the full hierarchy

Modelling simple tracking tasks with PCT is precise, above 95% prediction accuracy. Modelling a full human decision with all eleven levels active requires computational resources and experimental designs that do not yet exist at scale. Whether the framework scales cleanly to the top remains an open empirical question.

Status: open

None of these gaps affect the audits above, which operate on the two structural questions rather than on the full hierarchy. They are published because the alternative is that you discover them later and reasonably wonder what else was left out.

// faq

Questions clients ask before the first call

What does an AI verification audit actually check?

Two structural questions. First, whether the system holds a reference — a specification of the state it is supposed to reach — or only a proxy that has replaced it. Second, whether any independent channel exists through which the system's own report can be contradicted. Both are answerable without access to model weights or training data.

How much does it cost?

Fixed prices, net of VAT, agreed in writing before work starts, never hourly. Rapid AI architecture review €1,400. Vendor claim audit from €2,300. Applied architecture audit and AI system architecture audit from €4,700. Verification layer review and AI agent security audit from €7,000. Urgent AI incident analysis from €2,300; full post-incident report from €4,700. Closed-loop control engineering from €14,000. AI Crawl Audit from €2,500, Citation Architecture from €7,500, AI visibility care from €2,500 per month.

Why do you publish prices?

Because the price is information a client needs before a conversation can even start. Hiding it saves the seller's time, not the client's. I also publish what the price depends on and what is out of scope, so that a comparison with another offer is fair.

Where do I start on a small budget?

With the free 30-minute call, the free audit kit, or the free seven-day bot log sample. The cheapest paid start is the rapid AI architecture review at €1,400, which is credited against a full audit ordered within 60 days.

Do you need access to our code or infrastructure?

For the vendor claim audit, no — it runs against the deployed model through its normal interface. For the applied architecture audit, ordinary user access to the deployed system is enough. The verification layer review works from documentation and interviews. The security audits need documentation and read access to configuration and logs; the agent audit needs a test or staging environment and written consent. None of them need model weights or training data.

What should we do in the first hours after an incident involving an AI system?

Secure the records: application logs, agent action logs, conversation history and configuration, before log rotation overwrites or deletes them. Do not treat the model's explanation as evidence: asked for a cause, a model can produce a convincing but false story. I start an urgent incident analysis within 24 hours of the order and send written first findings within 24 hours of receiving the material. For an incident, call or message on WhatsApp at any hour: +48 733 123 915.

Can the incident report go to our board, an insurer or a regulator?

The report is technical documentation: the course of the incident, the evidence, the confidence level of each finding and the missing control layer. It can support your legal team, an insurer or a board, but it is not a legal opinion and does not replace any breach notification you may be required to make.

How does payment work?

For audits and engineering work: 50% upfront on signing, 50% by bank transfer within 30 days of the report or of acceptance of the work. The rapid review is paid 100% upfront. AI visibility care is paid monthly in advance. Prices are net; VAT is added where it applies. No hourly billing.

Do you sign an NDA?

Yes, on request, before any material is shared. Findings from commercial work are never published without your written consent, and then only anonymised.

Can we prove the audit happened without revealing our data?

Yes, within limits. At the end of each commercial audit I hash every transcript and log, combine the hashes into a Merkle tree and sign its root with the date. With your consent the root is published; it reveals nothing about the content and names neither you nor the system. Later you can show any single item to a regulator, an insurer or your board, together with a short proof that it belongs to the audited set, without disclosing the rest. What this proves: that the evidence existed in that form on that date and has not been altered since. What it does not prove: that the findings are right. That rests on the published method and on the report.

Is this a regulatory conformity assessment?

No. This is not a conformity assessment under the EU AI Act, not a certification, and not a legal opinion. It is an independent technical audit. If a formal conformity assessment is what is required, an accredited body is the correct supplier and will be recommended.

Do you have client references?

Not yet. The commercial practice starts in 2026, and I do not publish references that cannot be checked. Instead I show evidence: seven publications with DOIs, a public method, and experiments on my own domains, whose server logs, Google Search Console and Bing Webmaster Tools data I can show on request.

What do I receive at the end?

A written report containing the complete method used, the raw transcripts or logs, the findings with their evidence attached, and an explicit statement of what the work could not establish. Everything is reproducible by your own team from the report alone.

Can I check the method before hiring anyone?

Yes. The three-prompt protocol is published verbatim and free to run on any model in about ten minutes, and the full study is an open-access preprint with a permanent DOI. Run it first. A paid audit is worth buying only if the free version returns something that concerns you.

Do you work in English and outside Poland?

Yes. Reports are written in English or Polish, and all services are delivered remotely. Clients in Poland can also use the Polish-language offer at aisecurity.org.pl.

// contact

Start with a question about your system

Write what the system is, what worries you, and what you have already checked. I reply within 12 hours. The first call, 30 minutes, is free. If it is something I can help with, I will quote a fixed price. If it is not, I will tell you that instead, and where to look.

Not sure yet? Run the audit kit on a model you are responsible for, or send seven days of server logs for the free bot classification sample.

Email me WhatsApp +48 733 123 915 Audit kit — free

Łukasz Diener · Kraków · independent, no institutional or vendor affiliation · reports in English or Polish, all work remote · seven published audits · ORCID 0009-0006-6103-8514 · OpenAlex A5136472501

"I do not ask you to believe me. I ask you to test it. Copy the prompt. Run it on any model. If you get a different result, I will retract."

— Łukasz Diener, RLHF Reward Hacking, 2026