ai-security

Griffin AI vs Inflection Pi for Security Assistance

Does GitHub Copilot Use Your Code? IP and Licensing Questions Answered

Does GitHub Copilot steal your code, or just learn patterns from it? The honest answer depends on which setting you're using, what plan you're on, and whether the suggestion it hands back matches code it was trained on.

Enterprise LLM Budget Management Patterns

LLM spend forecasting is where finance teams meet AI engineering for the first time. The patterns that produce predictability are specific.

Griffin AI vs DeepSeek Coder for Security Review

DeepSeek Coder has become a favourite for code-focused workloads. This is how it compares to Griffin AI when the job is security review, not code generation.

Exploit Path Synthesis: Griffin AI vs Mythos

Finding a bug is not the same as proving it is exploitable. How Griffin AI synthesises concrete exploit paths and why pure-LLM scanners rarely get past the sketch stage.

RAG Pipeline Supply Chain Attacks: Vector DBs and More

RAG pipelines have six or seven supply chain surfaces, and most teams are only watching one. Here is how the attacks actually look in production.

SEvenLLM Design And Coverage

SEvenLLM set out to measure how well LLMs handle Security Event analysis, the unglamorous day-to-day work of SOCs and IR teams. A design review of what the benchmark covers, how it was built, and where the coverage maps or does not map to real operations.

Griffin AI vs Claude Haiku for Bulk Scanning

Claude Haiku is the cost-efficient model Griffin uses for high-volume scan interpretation. Here's how raw Haiku compares to Haiku inside Griffin's bulk pipeline.

Griffin AI vs OpenAI o1 for Security Reasoning

Deep reasoning models are transformative for hard logical problems. Security reasoning is only partially a logic problem—the rest is grounding, policy, and workflow.

Small-Model Distillation For Security Workflows

Distillation compresses the capability of a large model into a small one for a narrow task. For high-volume security workflows, it is often the difference between a working pipeline and an unaffordable one.

Training Data Opacity As A Trust Limit

You cannot audit what you cannot see. Frontier model training corpora are effectively opaque to their users, and that opacity is not incidental. It shapes what kinds of trust you can extend to the outputs.

Griffin AI vs Gemini Long Context for Codebases

Gemini's million-token context window is a genuinely new capability. For security analysis of large codebases, is it enough on its own?