AI Security
Before attackers test your AI, I do — authorized, evidence-backed, mapped to the OWASP LLM & Agentic Top 10 and MITRE ATLAS.
What AI security means
AI security architecture is the design of controls — guardrails, authorization, gateways, runtime monitoring — that protect LLMs, RAG pipelines, agents and tools from prompt injection, data leakage and misuse, built into the system rather than bolted on afterward. It differs from conventional cybersecurity in one key way: an AI system's own reasoning is part of the attack surface, not just its code.
How I work
Authorized engagements only. No claim of absolute security — the goal is measurably reducing risk, evidenced by findings and retests.
What's covered
Specialized area
When the system in question is autonomous — making decisions and taking actions, not just answering questions — the security model changes again. See Agentic AI Security.
Evidence
FAQ
What is AI security architecture?
The design of controls — guardrails, authorization, gateways, runtime monitoring — that protect LLMs, RAG pipelines, agents and tools from prompt injection, data leakage and misuse, built into the system rather than bolted on afterward.
How is AI security different from conventional cybersecurity?
Conventional AppSec assumes deterministic code. AI systems add a new attack surface: the model's own reasoning can be manipulated through its input, and agents can be tricked into misusing legitimate tool access. AI security requires both traditional controls and AI-specific ones.
Do you perform AI red-teaming yourself?
Yes — authorized, scoped AI red-team assessments mapped to the OWASP Top 10 for LLM and Agentic Applications and MITRE ATLAS. See the AI Security Assessment.
Ready to find out what your AI system is exposed to?
Authorized, evidence-backed, mapped to OWASP and MITRE ATLAS.