AI is moving fast. More and more organisations are using ChatGPT-like features in web applications, customer portals, internal tooling and workflows. That brings speed — but also new risks. A classic pentest alone is often not enough. You want to know: how secure is my AI functionality in practice?
In this article you’ll learn where AI systems are vulnerable, what a good AI security assessment delivers, and how a targeted approach reduces risk without slowing down innovation.
Why AI security is directly relevant right now
Where traditional security focuses on infrastructure and application code, AI adds an extra layer: prompts, model behaviour, context data, vector databases, API integrations and automated actions. That means new attack paths that standard controls don’t always catch.
Examples of risks in AI applications:
- Prompt injection — a user manipulates instructions so the model performs unintended actions.
- Data leakage — sensitive information leaks via responses, logs or context windows.
- Broken access control — AI agents are granted overly broad rights to systems or data.
- Insecure tool integrations — the model can perform actions without sufficient guardrails.
- Model abuse — abuse of inference endpoints due to a lack of rate limiting or validation.
AI pentest vs. regular pentest: what’s the difference?
A regular pentest remains important, but an AI security test explicitly looks at the AI chain:
- Input layer: prompts, uploads, user context, system prompts.
- Model and orchestration layer: policies, prompt templates, guardrails, memory.
- Data and retrieval layer: RAG sources, vector stores, filtering, source permissions.
- Action layer: API calls, plug-ins, internal tooling, workflow automation.
- Output layer: data leaks, compliance risks, incorrect or harmful responses.
That combination makes an AI assessment far more valuable for organisations already using AI, or planning to scale it in the short term.
Practical checklist: is your AI solution enterprise-ready?
Use this checklist as a quick reality check:
- Do you have clear separation between public, internal and confidential data?
- Are prompts and system instructions protected against manipulation?
- Can you demonstrate which sources were used in AI responses?
- Are API keys, tokens and secrets fully protected?
- Does the AI agent only have minimally necessary rights (least privilege)?
- Are failed and suspicious AI interactions actively monitored?
- Can you safely roll back if an AI feature behaves unexpectedly?
Can’t answer “yes” to several of these? Then a targeted AI pentest or security assessment is immediately worthwhile.
What does an AI security assessment actually deliver?
- A faster risk picture: where’s the greatest chance of incidents?
- Clear priorities: which fixes deliver the most risk reduction immediately?
- Better decision-making: security, product and management work from the same facts.
- More customer trust: you can demonstrate AI is deployed safely and under control.
- Less rework: building in security early is cheaper than fixing it afterwards.
Who is this relevant for?
This approach is especially valuable for:
- SaaS companies with AI features in their product,
- organisations with internal AI assistants or copilots,
- teams building API-driven AI workflows,
- companies that must meet customers’ security/compliance requirements.
From AI ambition to demonstrable security
AI offers enormous opportunities — but only when security scales with it structurally. By combining a web app/API pentest, infrastructure review and AI-specific testing, you prevent innovation from turning into risk.
Want a realistic picture of your AI application’s security? Start with a short intake and determine which type of test delivers the most value.
Request a quote directly or read more about our web application pentest and API pentest services.
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