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AI security, in depth.

Research and field-tested guidance on securing infrastructure, applications, and AI — agents, chatbots, LLMs, governance, and assurance for the enterprise.

Agent Security
Agent Security3 min read

Securing agentic AI: a threat model and reference architecture

Autonomous agents plan, call tools, and act on their own. That autonomy is exactly what expands the attack surface. Here is a practical threat model and a reference architecture for deploying agents safely.

Vaultryx AI Research
LLM Security
LLM Security3 min read

Defending against prompt injection in production LLM applications

Prompt injection is the top risk in the OWASP LLM Top 10 for a reason: there is no single patch. This is a defense-in-depth playbook for direct and indirect injection in real applications.

Vaultryx AI Research
AI Governance
AI Governance2 min read

Building an enterprise AI governance operating model

Governance fails when it is a document nobody reads. This is an operating model that turns AI policy into controls, evidence, and decisions people actually make — mapped to NIST AI RMF and ISO/IEC 42001.

Vaultryx AI Research
AI Infrastructure
AI Infrastructure2 min read

A reference architecture for secure enterprise AI infrastructure

The infrastructure under your models — inference endpoints, vector stores, GPUs, model registries, and data pipelines — is a first-class attack surface. Here is how to lay it out securely.

Vaultryx AI Research
AI Red Teaming
AI Red Teaming2 min read

AI red teaming: stress-testing LLMs and agents before attackers do

Red teaming AI is not one clever jailbreak — it is a repeatable discipline. Here is a methodology, a taxonomy of tests, and how to operationalize it as a continuous control.

Vaultryx AI Research