Reka's multimodal models offer strong image and video understanding at competitive prices. For security workflows that involve visual content — architecture diagrams, phishing screenshots, recorded incident sessions — Reka is a legitimate option. For the 90% of security workloads that are text and code, multimodal is not the binding constraint, and picking a model based on multimodal strength often optimises for the wrong dimension.
Where Reka adds value
Three specific workflows:
- Architecture diagram review. Process a diagram, identify potential security-relevant gaps.
- Phishing screenshot triage. Classify suspicious emails visually.
- Incident session replay. Process recorded sessions during investigation.
For each, multimodal is genuinely useful.
Where most security workload lives
Three categories:
- Code analysis. Text.
- Finding triage. Text.
- Remediation drafting. Text.
Multimodal does not help these. Grounding does.
How Griffin AI handles multimodal workflows
For the specific cases where multimodal is the right tool, Griffin AI calls out to multimodal-capable models (currently Claude's multimodal variants) via tool invocation. The engine routes to the right tool rather than forcing every query through a multimodal pipeline.
What to evaluate
Two questions:
- What percentage of your security workload is image or video?
- For the majority text-and-code workload, what grounding does the platform provide?
Optimising for multimodal when the workload is 90% text is a mismatch.
How Safeguard Helps
Safeguard's architecture routes multimodal workloads to appropriate models and text workloads to text-optimised reasoning. For the majority case, grounding is the differentiator. For the minority case, multimodal capability is available when needed.