During an incident
Operators ask what to check next, receive runbook-backed guidance, and see whether logs, policy, model routing, or internal knowledge shaped the answer.
AI support for CloudOps teams
AI help for CloudOps, with company controls built in.
AegisDesk gives employees one place to ask about incidents, tickets, access, and cloud cost while your policies decide what can be answered, approved, routed to a model, or recorded for audit.Ask operational questionsRedact sensitive dataRoute approved requestsAudit every action
Policy-aware CloudOps chat
Problem
Your teams already want AI during operational work. The hard part is control.
A public chatbot cannot know who the employee is, which production actions they are allowed to request, what should be redacted, when manager approval is required, or which audit record a security reviewer will need later. AegisDesk adds that governed workflow around the AI experience.
Product surfaces
The workspace for operators, approvers, governance reviewers, and control owners.
Use Cases
Give employees useful answers without bypassing how the company operates.
When access is needed
Employees request scoped production access from the same workspace. Unsafe admin access is denied, safer temporary access moves to a manager approval queue.
When spend spikes
Managers review AWS cost summaries with role-based access, cached Cost Explorer data, model-use evidence, and quota controls to reduce duplicate spend.
Why AegisDesk
Not another open chat box. A governed workflow around the answer.
Unmanaged AI answers the prompt.
It usually cannot verify the employee, apply internal policy, open an approval, cite company runbooks, or preserve a replayable audit trail.
AegisDesk controls the request.
Identity, redaction, policy, source evidence, approvals, model route, tool calls, and audit events are handled before and after the model response.
Teams keep their workflow.
Operators get practical help, managers approve only scoped actions, and reviewers can inspect exactly why each decision was made.
Workflow
Every request moves through the same control path.
1Verify identity
2Redact sensitive data
3Check policy
4Route the model
5Control tools
6Capture approval
7Write audit trail
Self-host with Docker Compose or AWS Terraform.Connect ticketing, incident context, access, and agent clients through adapter interfaces.Keep policy decisions outside the model response.
Security
The controls companies need before AI becomes part of daily operations.
SSO/JWKS verified identity and role claimsSecret and PII redaction before model routingExternal model kill switch and low-cost fallback pathDynamoDB audit trail and request replay packet
Integrations
Connect AegisDesk to the systems CloudOps teams already use.
Available in the hosted build
AWS BedrockAWS Cost ExplorerAmazon CognitoDynamoDB auditOPA/Rego policy
Adapter-ready for customer environments
CloudWatch LogsDatadogJiraServiceNowOktaMicrosoft Entra IDSlackMicrosoft TeamsMCP agent clients
Implementation path
Start with a focused self-hosted pilot, not a broad platform rollout.
Connect one identity source
Start with Cognito or a JWKS-compatible provider so policy decisions use real role and team claims.
Connect one operational workflow
Begin with incident triage, ticket creation, access approval, or cost review instead of trying to govern every workflow at once.
Prove audit-ready evidence
Review the prompt, redaction, policy decision, model route, sources, tool calls, approval state, and trace ID for every request.
Best first pilot
View buyer packetAWS CloudOps team with SSO, Bedrock, DynamoDB audit, OPA policy, and one real workflow such as Jira tickets or CloudWatch-backed incident triage.
Use AI in CloudOps without losing control of data, cost, access, or auditability.
Start with the hosted control plane, then connect your identity provider, ticketing system, logs, approvals, and model policy.