Secure Net Design | AI Security Research & Engineering

Research & Focus Areas

Bridging the gap between theoretical AI failure modes and enterprise-grade AI resilience.

Adversarial AI & Red Teaming

Investigating prompt injection vectors, jailbreak taxonomies, indirect context poisoning, and model evaluation techniques to harden frontier architectures.

Machine Psychology & Behavior

Analyzing model persona drift, latent space manipulation, cognitive bias exploitation, and decision-path auditing across multi-agent workflows.

LLM Misalignment Defense

Developing mitigation strategies for unexpected model behaviors, unintended goal alignment, and dangerous emergent capabilities in enterprise systems.

AI SecOps Architecture

Designing secure multi-agent orchestration frameworks, inline guardrail proxies, runtime inspection gateways, and automated threat mitigation pipelines.

About

Secure Net Design combines over twenty years of extensive background in enterprise IT, networking, and cloud architecture with cutting-edge AI security engineering.

Casey Prichard

Specializing in adversarial AI mechanics, machine psychology, and systemic model misalignment. Designing enterprise AI SecOps maturity frameworks, multi-agent guardrail controls, and cloud security architectures.

Credentials: CISSP, CCSP, CCSK, Google Generative AI Leader, IBM AI Engineering Professional, Post-Quantum Cryptography (UMD).

Frontier AI Security & Model Defense

Researching Adversarial AI Mechanics, Machine Psychology, and Model Misalignment in Enterprise Systems.