ai-security

Training Data Provenance: The Regulatory Wave

Regulators across three continents are converging on a single demand: show where your training data came from. The engineering implications are larger than most labs have admitted.

Call Graph Depth Compared: Griffin AI vs Mythos

Shallow call graphs miss real exploits; deep graphs surface them. We examine how Griffin AI and Mythos-class tools differ on depth, and why it matters.

Hypothesis Quality: Griffin AI vs Mythos

Two AI bug hunters can both generate hypotheses. Only one can defend them. A field study of grounded versus ungrounded hypothesis generation in zero-day discovery.

Vulnerability Scanning for AI Models: A New Frontier

AI models ship with dependencies, use vulnerable libraries, and introduce novel attack surfaces. Traditional scanning is not enough.

Securing Claude Code MCP Server Deployments

Claude Code MCP servers run with the privileges of the developer who invoked them. That makes deployment posture the entire security model.

Griffin AI vs xAI Grok for Security

Enterprise AI Security Rollout: The Governance Gap

Most enterprises rolled out AI-for-security tools faster than their governance processes could keep up. The resulting gap is where most of the pain from 2025 deployments lives.

Griffin AI vs Llama 3 for Security Workflows

Llama 3 is a powerful open-weight foundation model, but security workflows demand more than raw inference. Here is how Griffin AI compares.

CyberSecEval Reviewed: What It Measures

A working engineer's review of CyberSecEval, the Meta-originated benchmark that has quietly become the default sniff test for AI-for-security claims. What it actually measures, what it misses, and how to read its scores without fooling yourself.

Griffin AI vs Raw Claude for Security Workflow

Griffin AI runs on Anthropic's Claude models under the hood. Here's what the engine context, eval harness, and workflow scaffolding actually buy you over calling Claude directly.

Griffin AI vs Pure GPT-5 for Security Workflows

Frontier models are remarkable reasoners, but security workflows demand more than raw intelligence. Here's how Griffin AI grounds frontier reasoning in real tenant context.

Reachability Analysis: Griffin AI vs Mythos

Reachability-grounded reasoning produces actionable findings. Ungrounded LLM reasoning produces speculation. We explain the methodology gap.