Gemini's multimodal capabilities — image, audio, video understanding — are genuinely differentiated. For a handful of security workflows (phishing screenshot analysis, architecture diagram review, video-based incident replay), multimodal is valuable. For most security workflows, the modality is code and text, and multimodal is not the binding constraint on quality or cost.
Where multimodal adds value in security
Three workflows:
- Phishing screenshot analysis. Quickly classify suspicious emails or web pages.
- Architecture diagram review. Evaluate a diagram for security-relevant gaps.
- Incident video replay. Process recorded sessions during IR.
Each is a legitimate use case. Each is a minority of overall security workload volume.
Where the core workload is
Three workloads that dominate:
- Code analysis. Text.
- Finding triage. Text.
- Remediation drafting. Text.
For these, multimodal is not relevant. The grounding layer — reachability, SBOM, policy — is what matters.
How Griffin AI handles multimodal when needed
For the specific workflows that benefit from multimodal, Griffin AI calls out to the appropriate model (Gemini or Claude's multimodal variants) as a tool. Multimodal is not the default pathway but is available when the workflow calls for it.
What to evaluate
Two questions:
- What percentage of your security workload benefits from multimodal analysis?
- For the text-and-code-dominant majority, what is the grounding architecture?
Answer both before prioritising multimodal in procurement.
How Safeguard Helps
Safeguard's Griffin AI uses multimodal reasoning where it adds value and text-based analysis where it is sufficient. For security workloads whose majority modality is code and text, the platform doesn't pay for multimodal when multimodal isn't the right tool.