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AI Security / Munich

AI Security Consultant in Munich

AI security testing for Munich's insurers, manufacturers and enterprise IT.

Munich combines insurance, automotive, industrial and enterprise software headquarters, each rolling out AI assistants over internal documents, claims, engineering data and customer service. These are large organisations with formal security reviews, and AI systems have to pass them.

I provide AI-specific red team testing that fits into that review: structured, documented, mapped to recognised frameworks and to the EU AI Act, with fixes and retests.

Munich
AI security testing for organisations across Munich. Delivery is remote — the map shows coverage, not office locations.

AI security testing in Munich

Data regime
GDPR with the German federal data protection act layered on top. Works councils may also have a say in systems touching employee data.
Working hours
CET. Identical working day.
Languages
German for business and staff-facing systems, English for technical work.
Delivery
Remote testing against your staging or production endpoint, with scoping and findings sessions scheduled in the Munich working day.
Frameworks
OWASP Top 10 for LLM Applications, MITRE ATLAS, NIST AI RMF, EU AI Act Article 15.
Tools
Garak, PyRIT, promptfoo, Giskard, Burp Suite and custom Python harnesses.

Authorised testing only

Every assessment runs under a written scope and authorisation from the owner of the system, agreed before any test is run.

Why AI security matters in Munich

Insurance and financial services

Insurers are subject to DORA and to close supervisory attention on AI in claims and underwriting. Manipulation and data leakage in those assistants are tested directly.

Industrial knowledge assistants

RAG systems over engineering documents and supplier data hold intellectual property. Retrieval access control and exfiltration paths are the focus.

The EU AI Act

The AI Act applies in stages: prohibited practices from February 2025, general-purpose model obligations from August 2025, and high-risk system obligations from August 2026 onward, with some high-risk deadlines subject to the Commission's proposed postponement. Article 15 requires high-risk systems to be robust against attacks such as data poisoning, adversarial inputs and model evasion.

Formal security review

Findings are mapped to OWASP LLM Top 10 and MITRE ATLAS so they can be tracked in the same risk process as any other finding.

What gets tested

The six attack classes behind most serious findings. The full list, and the tooling used for each, is on the main AI security page.

Direct prompt injection and jailbreaks

Role-play, instruction override, payload splitting, encoding tricks (Base64, leetspeak, invisible Unicode) and multi-turn escalation, to see whether the model can be argued out of its instructions and guardrails.

Indirect prompt injection

Instructions planted in the content your system reads rather than in the chat box: an uploaded PDF, an inbound email, a web page an agent browses, a product review, a CRM note. This is the attack most production systems are least prepared for.

System prompt and configuration leakage

Extracting the hidden instructions, internal URLs, API structure, business rules and occasionally the credentials that developers put in a system prompt on the assumption nobody would see it.

Data exfiltration through output

Markdown images, auto-unfurled links and tool calls that quietly send conversation data or retrieved documents to an attacker-controlled server once a malicious instruction lands.

Excessive agency and tool abuse

Agents persuaded to send emails, issue refunds, change records or call internal APIs outside their intended purpose. Tested against the real tool permissions, including MCP servers and poisoned tool descriptions.

RAG and vector store weaknesses

Cross-tenant document leakage, retrieval that ignores the user's access rights, poisoned documents that steer answers, and embeddings that reveal more than the source permissions allow.

How the assessment runs

1. Scope and threat model

Map what the AI system can read, what it can do, who talks to it and what would hurt most if it went wrong. Written authorisation and rules of engagement are agreed before any testing.

2. Automated scanning

Garak, PyRIT and promptfoo run thousands of known attack patterns against the live or staging endpoint to establish a baseline quickly and cheaply.

3. Manual adversarial testing

The part that finds the serious issues: multi-turn manipulation, indirect injection through your real document and email flows, and chained attacks that scanners cannot plan.

4. Agent and integration testing

Every tool, API and permission the model can reach is tested for abuse, including privilege boundaries between users and tenants.

5. Report and fixes

Each finding comes with a reproduction, a severity, the OWASP LLM and MITRE ATLAS mapping, and a concrete fix — architecture first, filters second.

6. Retest and regression suite

Fixes are retested, and the successful attacks become a promptfoo suite in your pipeline so they cannot quietly come back with the next model upgrade.

Areas served

AI security testing is available across Munich, including Maxvorstadt, Schwabing, Bogenhausen, Sendling, Garching, Neuperlach, Werksviertel. There is no local office — testing is delivered online, which is how AI endpoints are attacked in practice anyway.

AI risk in Munich's key sectors

Where AI is being deployed fastest here, and the risk tested first in each sector.

AI security for regulated financial AI

Assistants over accounts, claims and policies are tested for data leakage, manipulation into actions and unsafe advice, with results documented for DORA, the AI Act and your regulator.

AI security for operational assistants

Assistants over shipment, customer and pricing data must not leak one customer's data to another or be steered into changing records. Documents and emails the system reads are tested as injection vectors.

AI security for confidential knowledge systems

RAG assistants over client files and matters must respect confidentiality between clients and between teams. Retrieval access control and exfiltration through rendered output are the priority tests.

AI security for shopping and support assistants

AI support agents can be talked into refunds, discount codes and policy exceptions, and product-page content can carry indirect prompt injection. Both abuse paths are tested against the tools the assistant can actually call.

AI security for patient-facing assistants

A booking or patient-support assistant connected to records can be manipulated into revealing another patient's appointments or history. Health data is special-category data under GDPR, so assistants are tested for leakage and for unsafe medical advice before patients use them.

Frequently asked questions

Can you work with our internal security team?

Yes. Most enterprise engagements are run alongside the internal security team, who receive the full reproductions and the regression suite.

Do you test German-language assistants?

Yes, in German and English.

Related pages

AI security in other markets

Get in touch

Tell me what needs automating

Describe the process that is costing you time and roughly how much. I reply to every enquiry personally, usually within one working day.

Response
Usually within one working day, Mon–Fri CET
Delivery
Remote across Europe, the Nordics and the Gulf
Or email inquiries@artenisalija.com