AI systems don't just process data. They interpret instructions, call tools and act on behalf of users. That creates attack paths traditional controls were never designed for: manipulated prompts that bypass safeguards, agents tricked into misusing their permissions, and sensitive data leaking through model outputs. This paper maps the AI-specific attack surface and explains how to test and secure it before it reaches production. It also covers what the EU AI Act means for your governance.

Organizations are moving quickly from AI experiments to production: customer-facing assistants, internal copilots connected to company data, and agents that take real actions in business systems. Each step adds capability and risk. An AI system that can read emails, query databases or trigger workflows can be steered by an attacker who never touches your infrastructure directly. A carefully crafted document, web page or message can be enough. Traditional application security testing doesn't catch these issues, and many teams aren't sure what "secure" looks like for an AI feature. Meanwhile, the EU AI Act is setting new expectations for governance, transparency and risk management. This paper helps technical and business leaders understand the risks clearly, without hype.

Inside the paper:

• The main AI attack techniques: prompt injection, tool and agent abuse, data leakage

• Why connecting AI to internal data and tools multiplies risk

• How to test AI systems under realistic adversarial conditions

• Guardrails and architectural patterns that limit the damage of a compromised model

• Aligning AI security with emerging EU AI Act obligations

Fill in the form to access the full paper. If you're about to launch or extend an AI feature, we'd be glad to discuss how to test it under realistic attack conditions first.