AI Security

AI applications have an attack surface that traditional penetration testing was never built to cover. This is what AI penetration testing is, what it tests, and how it differs from a standard pentest and from red teaming.

AI red teaming simulates a determined adversary against your models, agents, and guardrails. This is what it involves, how it differs from AI penetration testing, and how to run it well.

When AI can take actions, a manipulated model becomes a manipulated actor. This is why agentic AI is a new attack surface, how agents get exploited, and how to secure them.

The Model Context Protocol connects AI models to tools and data, and that connection is a new attack surface. This is what MCP is, the risks it introduces, and how to secure it.

Retrieval-augmented generation gives models access to your data, and that data becomes part of the attack surface. This is how RAG systems get attacked and how to secure them.

Data poisoning corrupts what an AI system learns or retrieves, so it fails in ways the attacker chooses. This is how poisoning attacks work and how to defend against them.

A jailbreak makes a model do what its safety controls were meant to prevent. This is how LLM jailbreaks work, how they differ from prompt injection, and how to defend against them.

The OWASP Top 10 for LLM Applications is the reference list of the most critical AI application risks. This is what each of the ten risks means and how to address it.

Deepfake fraud uses AI-generated voice and video to impersonate executives and authorize payments. This is how the attacks work and how to defend your organization.

LLM apps add an attack surface that does not behave like anything before it. Better prompts will not save you. The controls that work live in the architecture. Here is how to secure them.

Teams keep trying to patch injection with better system prompts. The fix lives in the architecture, not the wording.