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AI security · EMEA

AI security for LLM applications and agents across EMEA

Language models and AI agents read text from sources you do not control, call your internal tools and work with real data. That opens attack paths a classic test does not cover. We test your AI applications from an attacker’s position, review the guardrails and architecture around them, and help you set rules for how your staff use AI tools, so you can adopt AI without trusting it more than it has earned. Our team in Skopje is available to organizations in North Macedonia and across Europe, the Middle East and Africa, and most of the work runs remotely.

Book a consultationHow it works
  1. 01UserA request to the AI assistant or agent.
  2. 02GuardrailInput and output checks against policy.
  3. 03LLMA model with least privilege.
  4. 04Tools & dataActions only with approval and logging.
Protecting AI applications: prompt injection stopped before the model

AI security

What you get

01

Weaknesses found before attackers find them

We show where your application can be steered by prompt injection, made to leak data or its system prompt, or pushed into actions it should not take, with reproducible evidence for each finding.

02

Agents with limited reach

Tools and integrations get only the permissions they need, and high-impact actions wait for a person to approve them.

03

Clear rules for your staff

Your people know which AI tools they may use, what data may go into them and whom to ask when they are unsure.

04

Findings in a shared language

Every result maps to the OWASP Top 10 for LLM Applications 2026 and MITRE ATLAS, so developers, risk teams and auditors read the same report.

What's included

  • Security assessment of LLM applications, chatbots, retrieval-augmented generation (RAG) systems and AI agents
  • Tests for direct and indirect prompt injection, sensitive information disclosure, hidden context exposure (including system prompt leakage) and excessive agency
  • Checks for improper output handling, where model output reaches a browser, database, shell or another system without validation
  • Review of vector and embedding weaknesses, such as retrieval that ignores user permissions, and of the AI supply chain: models, datasets, plugins and third-party APIs
  • AI red teaming: goal-driven attack scenarios described with MITRE ATLAS tactics and techniques
  • Guardrail and architecture review: least privilege for tools, human approval for high-impact actions and logging you can investigate
  • Governance for secure AI adoption: a staff AI use policy, data classification rules and AI vendor risk review, built on NIST AI RMF (AI 100-1)
  • A report with each finding mapped to the OWASP Top 10 for LLM Applications 2026, rated by risk and paired with a concrete fix

How we work

  1. 01

    Scope and threat model

    We map what your AI system reads, which tools and data it can reach and who uses it. That shows where an attacker would push and what misuse would cost you.

  2. 02

    Test the application

    We test the model integration, the retrieval pipeline and the connected tools against the OWASP Top 10 for LLM Applications 2026, combining manual techniques with repeatable test sets.

  3. 03

    Red team

    When it is in scope, we pursue a concrete attacker goal based on MITRE ATLAS, for example getting an agent to send out data after it reads a planted document.

  4. 04

    Review guardrails and architecture

    We check permissions, approval steps, input and output filtering and logging, and recommend changes to the design rather than only patches to the prompt.

  5. 05

    Report and govern

    You receive risk-rated findings with concrete fixes and, if you need it, a governance package: AI use policy, data rules and vendor checks aligned with NIST AI RMF.

Who it's for

  • Banks, insurers and public institutions putting chatbots or assistants in front of customers and citizens
  • Product and engineering teams building LLM applications, retrieval systems or AI agents
  • Organizations whose staff already use public AI tools and need clear rules and data boundaries
  • Risk and compliance teams that need evidence an AI system was assessed before it went live

Frameworks and standards

  • OWASP Top 10 for LLM Applications 2026
  • MITRE ATLAS
  • NIST AI RMF (AI 100-1)
  • ISO/IEC 27001:2022

Frequently asked questions

How is testing an AI application different from a regular penetration test?
A classic test looks for flaws in code and configuration. A language model adds a new input channel: any text it reads, whether from a user, a web page or a document, can try to change its behavior. We test that channel and what the model is allowed to do with its output, alongside the usual web and API checks.
What does the OWASP Top 10 for LLM Applications 2026 cover?
It is OWASP’s list of ten key risk categories for applications built on large language models: LLM01 Prompt Injection, LLM02 Sensitive Information Disclosure, LLM03 Excessive Agency, LLM04 Supply Chain, LLM05 Data and Model Poisoning, LLM06 Unbounded Consumption, LLM07 Misinformation, LLM08 Hidden Context Exposure, LLM09 Vector and Embedding Weaknesses and LLM10 Improper Output Handling. It replaced the 2025 edition in August 2026; system prompt leakage now falls under Hidden Context Exposure. We build our test plan on it and map our findings to it.
What is AI red teaming?
It is a goal-driven exercise. Instead of working through a checklist, we take an attacker’s objective, such as extracting customer data or triggering an unauthorized payment through an agent, and try to reach it by chaining weaknesses. We describe the attack paths with MITRE ATLAS, the knowledge base of adversary tactics and techniques against AI systems.
Our staff already use public AI tools. Where do we start?
With a short, practical policy: which tools are approved, which classes of data must never be entered and how staff request a new tool. We build it with you on NIST AI RMF (AI 100-1) and its four functions, Govern, Map, Measure and Manage, connect it to your existing data classification and add AI providers to your vendor risk review.
Do you use AI in your own work?
Yes, under the same rules we recommend to you. In our SOC, AI drafts the first analysis of an alert, but people decide. Any disruptive action, such as blocking traffic or isolating a device, needs an analyst’s approval and is logged with who approved it and when.

Related services

Penetration testing & red teamingManual testing of networks, applications, cloud and people, with CVSS-rated findings, clear reports and a retest of fixes.Learn moreISO 27001 implementationAn ISO/IEC 27001:2022 management system built around your risks, from scoping and controls to the certification audit.Learn moreSecurity awareness & phishing simulationRole-based training and ethical phishing simulations that build a reporting culture, in Macedonian and English.Learn more

Let’s talk about your security

Tell us what you need: 24/7 monitoring, testing or readiness for the NIS2-aligned law. In a short call we will agree on scope and next steps.

Book a consultation+389 75 340 034

Mon–Fri 09:00–17:00 · SOC 24/7 · info@diverto.mk