AI speed. Human security expertise.
AI-assisted analysis can relate noisy observations, extract structured context and surface questions for review. Every AI-identified issue is manually reviewed by Botnet Security’s security team before it is presented to the customer.
- 01Discover
- 02Correlate
- 03Validate
- 04Prioritize
- 05Guide remediation
Inputs must carry their boundaries with them.
Each input is shown with its intended purpose and a support state. The page does not turn an architecture subject into a production claim.
Exposure evidence
Selected technical observations and their source metadata.
Customer context
Approved ownership, architecture or policy context required for the use case.
Approved model
Azure AI Foundry or another customer-approved, customer-controlled deployment where required.
Human review
Botnet Security review before an AI-identified issue is presented to the customer.
DECISION MODEL
The graph should answer a question.
- 01
What evidence is provided to the model?
- 02
Where is the model hosted and how is data handled?
- 03
Which output is advisory and which action needs approval?
- 04
Can the result be traced back to source evidence?
Evidence enters.
An owned action leaves.
- 01Structured context linked to source evidence
- 02Suggested correlation or validation questions
- 03Human-reviewed priority rationale
- 04Auditable decision state and downstream owner
What this page does not claim.
- No default transfer to an unspecified public model.
- Azure AI Foundry is an architecture option, not a universal deployment claim.
- AI output does not eliminate false positives or human review.
- Provider, residency, retention and training-use statements require confirmation.
Confirm the use case before promising the connector.
We will establish the exposure question, approved sources, data boundary and current support status before representing a deployment path.