Real enterprises don't pick a cloud — they end up with three. AegisAI is the cross-cloud identity layer that lets an AI agent run a single query across AWS RDS, Azure Synapse, GCP BigQuery, and SAP on-prem — each call authenticated as the actual user, each native IAM enforced, every decision in a single HMAC-audited chain.
M&A history, vendor diversification, regional sovereignty, talent constraints — every enterprise ends up on multiple clouds. The AI integration market still ships per-cloud point solutions. That gap is where AegisAI lives.
Acquired company runs on Azure. Parent company runs on AWS. Six months later, both data estates still need to feed the same Claude assistant. The naive integration is two separate codebases with two separate auth models.
EU data must stay in EU. US Federal data must stay on FedRAMP-authorized clouds. APAC subsidiary requires regional AWS. The AI assistant must respect all three boundaries simultaneously. Per-cloud point solutions can't.
Procurement requires that no more than 60% of compute spend goes to any one hyperscaler. Data services land where the deal landed. AI access has to span the result.
AegisAI sits in one location (typically the customer's primary cloud or on-prem k8s). From there it speaks each cloud's federated identity protocol natively. Identity propagation is per-cloud-correct; audit is unified.
Federation isn't a future roadmap item — it ships. The intent compiler recognizes "across AWS and Azure" or "in production systems" and fans the request out to each cloud in parallel under the user's identity at each one.
The user asks Claude: "Give me a full picture of customer 'ExampleCo' — revenue, support tickets, infrastructure spend." (Illustrative scenario.)
AegisAI fans out four parallel queries. Each one authenticated under the user's identity in that system. Results merge through the response firewall and the AI receives a single, masked, audit-attributed answer. Latency depends on the slowest backend in the federation set; the cross-cloud overhead AegisAI adds is parallel-execution-bounded.
EU customer data must not leave EU. US data must not leave US. APAC subsidiary runs Singapore region only.
One cloud is having a region outage. The other two are healthy. The user query partially succeeds.
Compliance teams cannot fly between AWS CloudTrail, Azure Sign-in Logs, GCP Cloud Audit Logs, and SAP audit dumps to reconcile a single user's behavior. AegisAI gives them one chain.
Every cross-cloud decision lands in the same HMAC-chained Postgres table. SOX, GDPR, EU AI Act, and FedRAMP all read from one source of truth.
Native cloud audit logs (CloudTrail, Sign-in Logs, Cloud Audit Logs) keep running. AegisAI's audit chain correlates by trace_id so investigators can cross-reference.
Adaptive trust signals work across cloud boundaries. A user exfiltrating slowly from AWS, Azure, and GCP simultaneously is one user pattern — AegisAI sees it as one.
The deterministic policy AST is cloud-agnostic. A policy that denies cross-tenant queries works identically against AWS, Azure, or GCP backends. One audit, one policy, three clouds.
AegisAI runs in the customer's primary cloud (or on-prem k8s). Speaks to AWS / Azure / GCP / SaaS / SAP across the network. Simplest to operate. Right for most multi-cloud customers.
One AegisAI per data-residency region. Each enforces its boundary. Audit chains can be sealed locally or aggregated centrally. Right for GDPR · FedRAMP · sovereign workloads.
One AegisAI per cloud. Each speaks its native cloud's identity protocol. Cross-cloud queries hop via service-to-service auth. Right for org-mandated cloud isolation.
30-minute architecture call. We open the operator console and run real queries through your stack — AWS, Azure, GCP, or all three. You see the audit chain tick up in real time.
