AI Security
Griffin AI vs Sourcegraph Cody for Security Use
Cody's codebase-wide context is valuable for security review. Griffin AI adds reachability, taint, and policy grounding that Cody doesn't target.
Mar 24, 20262 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.
Cody's codebase-wide context is valuable for security review. Griffin AI adds reachability, taint, and policy grounding that Cody doesn't target.
An AI Center of Excellence is not a committee. It is the function that makes AI adoption coherent across business units. The blueprint is specific.
The honest answer to "when does this pay back?" is where sales decks and procurement reality diverge. Griffin AI and Mythos-class tools have different ROI shapes.
Open-weight models give you total deployment control. They also give you a new supply chain to secure. The tradeoff is worth being explicit about.
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.
Weekly insights on software supply chain security, delivered to your inbox.