AI Security
In-depth guides and analysis on ai security from the Safeguard engineering team.
676 articles
Griffin AI vs Mythos: Architecture Deep Dive
An architectural comparison of Griffin AI's engine-grounded reasoning stack against the pure-LLM pattern that Mythos-class products rely on.
MCP Transport Layer Security Options
MCP supports stdio, streamable HTTP, and a handful of experimental transports. Each has distinct security properties, and the choice of transport constrains every other security decision you make about the deployment.
Multi-Modal AI Supply Chain Considerations
Multi-modal models bring image, audio, and video into the AI supply chain. Each modality introduces provenance and integrity challenges that text-only pipelines never had to face.
AI-Generated Dockerfile Vulnerability Patterns
LLM-generated Dockerfiles repeat the same six or seven mistakes. Here is the pattern catalog and how to catch them before they ship.
Griffin AI vs OpenAI Function Calling: Scoping
Function calling gives models the ability to act. Acting safely on behalf of a specific user, in a specific context, within specific policy is a different problem.
LLM Selection Cost-Quality Tradeoff For Security
LLM selection is ultimately a cost-quality optimisation under workflow constraints. The curve is not smooth, and the right point on it depends on where errors land in your pipeline.
Unbounded Output Space And Security Contracts
A function whose output space is finite and enumerable can be secured by testing. A function whose output space is every string of tokens up to some length cannot. That difference quietly invalidates most classical security contracts.
Bring-Your-Own-Model: Griffin AI vs Mythos
Model lock-in is the quiet liability of pure-LLM vendors. Safeguard's bring-your-own-model story gives enterprises the option Mythos-class competitors cannot match.
Griffin AI vs Vertex AI Safety for Enterprise
Vertex AI Safety is Google's approach to enterprise AI controls. For security-specific workflows, Griffin AI adds grounding the Safety layer doesn't.
EU AI Act Enforcement: Year One Review
The first enforcement window under the EU AI Act has closed. The actual pattern of enforcement looks different from the one vendors and advocacy groups predicted.
Patch Minimality: Griffin AI vs Mythos
A minimal patch is easier to review, safer to merge, and cheaper to roll back. Griffin AI enforces minimality; Mythos-class tools treat it as optional.
MCP Server Rate-Limiting Patterns
A practical look at rate-limiting patterns for Model Context Protocol servers, covering per-tool quotas, token budgets, burst control, and abuse-resistant designs.