Sourcegraph Cody's codebase-wide context and code intelligence are among its strongest differentiators for a code-AI tool. For security review, codebase-wide context is necessary; by itself, it's not sufficient. Griffin AI adds the specifically security-oriented grounding — reachability, taint analysis, policy integration — that transforms codebase context into actionable security findings.
What Cody does well
Three strengths:
- Codebase-wide retrieval. Cody searches and retrieves from the whole codebase.
- Code intelligence integration. Leverages Sourcegraph's existing code navigation.
- Enterprise deployment. On-prem and SaaS options with enterprise governance.
For organisations already using Sourcegraph for code navigation, Cody extends the value.
Where security workflows need more
Four security-specific requirements beyond codebase context:
- Taint analysis across the call graph.
- Version-aware CVE mapping tied to the specific installed versions.
- Exploit hypothesis generation for reachable findings.
- Fix PR generation with breaking-change awareness.
Cody's code intelligence is foundational for some of this. The security-specific layers are not Cody's focus.
How they fit together
For Sourcegraph customers, the pattern:
- Cody for general code AI, codebase navigation, and developer Q&A.
- Griffin AI for security-specific review, findings, and remediation.
Some overlap exists but the tools primarily complement.
What to evaluate
Two questions:
- Is Sourcegraph the code-intelligence platform, or are you considering it?
- What security-specific analysis does the deployment need?
For Sourcegraph-centric organisations, Griffin AI layers on top. For security-primary needs, Griffin AI stands alone.
How Safeguard Helps
Safeguard's Griffin AI integrates with Sourcegraph Cody deployments for customers who have standardised on Sourcegraph. The security-specific grounding is what Griffin AI adds; codebase context is what Cody brings to the table.