Question 1

How is generative AI being used to automate vulnerability discovery and exploitation?

Accepted Answer

Generative AI is increasingly being used to automate code analysis, vulnerability discovery, exploit development, phishing content creation, malware obfuscation, and social engineering campaigns. Threat actors are leveraging AI to accelerate reconnaissance, identify insecure code patterns, and scale cyber attacks more efficiently across enterprise environments.

Question 2

What are prompt injection attacks and how do I defend against them?

Accepted Answer

Prompt injection attacks manipulate AI or large language model (LLM) inputs to bypass safeguards, expose sensitive data, execute unintended actions, or manipulate model behaviour. Defending against prompt injection requires strong input validation, context isolation, access controls, output filtering, retrieval security, and continuous monitoring of AI system interactions.

Question 3

What are the most common AI-enabled cyber fraud techniques targeting organisations in 2026?

Accepted Answer

In 2026, common AI-enabled cyber fraud techniques include deepfake impersonation, AI-generated phishing emails, synthetic identity fraud, voice cloning, automated business email compromise (BEC), fake support scams, and AI-assisted credential theft. Attackers increasingly use generative AI to scale highly convincing social engineering attacks targeting employees, executives, and customers.

Question 4

How do I secure AI models and LLM pipelines against adversarial attacks?

Accepted Answer

Securing AI models and LLM pipelines requires strong access controls, model isolation, secure training data management, prompt filtering, adversarial testing, API protection, monitoring, and governance controls. Organisations should also implement supply chain security, retrieval-augmented generation (RAG) safeguards, and continuous validation to reduce the risk of model manipulation and data leakage.

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