AI Security
AI Scaffold Prompts: Enterprise Governance
System prompts that scaffold AI assistants are now load-bearing enterprise assets. A framework for versioning, reviewing, and governing them as seriously as source code.
Mar 22, 20268 min read
Deep dives, practical guides, and incident analyses from engineers who build Safeguard. No fluff, no vendor FUD — just what you need to ship secure software.
System prompts that scaffold AI assistants are now load-bearing enterprise assets. A framework for versioning, reviewing, and governing them as seriously as source code.
A release gate that fails on regression is the most important operational control for AI-for-security tools. The design patterns are specific and worth copying.
Claude's citations feature makes the model say where its claims come from. Griffin AI uses it for advisory workflows where traceability is the entire point.
Per-token pricing on the OpenAI API looks cheap on a single call and expensive on a year-long security workload. Griffin AI's pricing reflects the architecture.
Small language models aren't a worse version of large ones. For specific security workflows, they're the right tool — if you know which workflows.
A senior engineer's threat model for Claude MCP tool poisoning in 2026, covering malicious servers, description hijacking, and the authorization patterns that actually help.
Fine-tuning to improve one task frequently regresses others. Without eval harnesses, the regressions ship. The measurable drift is larger than vendors admit.
Gemini on-device models are fast and cheap. For the developer-tool layer, they're useful. For the engine-plus-LLM layer, on-device is not the right fit.
The difference between grounded reasoning and hallucinated reasoning is not eloquence — it's citation. A look at how Griffin AI anchors every claim.
Weekly insights on software supply chain security, delivered to your inbox.