Every existing pattern — cloud-native AI services, API gateways, RAG frameworks, MCP servers, IdP-only solutions, data-classification platforms — addresses a piece of the problem. None of them address the whole. Here's the gap analysis.
Every cloud has its own identity model. STS AssumeRole for AWS. Entra ID OBO for Azure. WIF for GCP. Trusted RFC for SAP. OAuth for SaaS. Today, integrating AI agents across all of these requires bespoke code per pairing. The combinatorial explosion is real and unmanageable.
Amazon Bedrock Agents for AWS. Azure OpenAI on Your Data for Azure. Vertex AI Agent Builder for GCP. Each one assumes its own cloud and bolts on a service account for everything outside.
AegisAI implements STS chains, OBO flows, WIF, Trusted RFC, OAuth flows under one consistent JWT-in / native-identity-out abstraction. Adding a new AI agent is configuration; adding a new backend is a connector module that reuses the same propagation primitives.
AWS CloudTrail. Azure Sign-in Logs. GCP Cloud Audit Logs. SAP audit dumps. Salesforce Setup Audit Trail. When the regulator asks "show me everything user alice@globex.com touched via AI in Q3," you face a 200-hour log-reconciliation project.
Splunk, Datadog, Elastic SIEM, Sentinel — they collect logs from each source and let you search across them. Better than nothing. But the schemas are different per source, attribution accuracy varies, and the audit chain isn't tamper-evident.
Every AegisAI decision is one row in one Postgres HMAC chain. SHA-256 row hash + HMAC signature. SELECT FOR UPDATE row lock. The chain is re-walkable from any machine with the key. Cloud-native audit logs still run; AegisAI's chain correlates them by trace_id.
Cloud-native AI services use cloud-native policy. AWS uses IAM + SCPs. Azure uses Conditional Access. GCP uses Org Policies. Each is excellent in its cloud. None work across clouds. RAG frameworks and "AI security" startups solve this with LLM-judged policy — which is non-deterministic and not auditable.
Several vendors ship "AI security" products that use a small LLM to decide whether each prompt is dangerous. The verdict varies between runs. The reasoning chain is opaque. The same input yields different verdicts tomorrow.
AegisAI's policy engine compiles expressions to a safe AST whitelist. Deny-by-default. Same inputs produce identical verdicts, every run, every replay. ADR 0001 enforces this as an architectural invariant. The policy is portable across clouds.
Snowflake has dynamic data masking. BigQuery has column-level masks. Salesforce has Shield Encryption. SAP has data-element classification. They're all internal-to-the-backend. The AI assistant's response can still expose a partially-masked-here / fully-masked-there inconsistency.
Each backend masks fields its own way according to its own schema. The AI response is whatever each backend returned, concatenated. No unified mask strategy. Different masking definitions can leak data via context (e.g., the salary range from one source plus the title from another infers the salary).
AegisAI tags every schema field with classification, PII kind, and mask strategy. The response firewall reconciles per-backend masking with a unified policy. Salary becomes a range; email becomes partial-mask; tenant_id drops — consistently across SAP, Snowflake, BigQuery, Salesforce.
A user querying AWS RDS twice a day from her laptop — benign. The same user querying AWS RDS + Azure Synapse + Salesforce + SAP simultaneously every 30 seconds for two days straight — possibly exfiltration. Per-cloud rate limiters don't see the cross-cloud pattern.
AWS API rate limits. Azure throttling. GCP quotas. Salesforce API limits. Each enforces its own threshold based on its own request count. None of them see the coordinated cross-system pattern.
Frequency, scope expansion, coverage growth, and cross-user coordination all evaluated at the AegisAI gateway. One signal stream sees one user's behavior across every cloud the user touches. Trust scores collapse on coordinated patterns — AegisAI tightens per-user limits before any individual cloud's rate limiter trips.
Every well-known AI security vendor offers a SaaS data plane. The customer's queries flow through the vendor's infrastructure. For SAP-customer-grade enterprises and regulated industries, that's unacceptable. For sovereign deployments, it's illegal.
Vendor-hosted control + data plane. Customer queries flow through vendor infrastructure. The vendor sees every query. Fast to deploy, easy to operate. Wrong for regulated workloads.
Three deployment modes: vendor-hosted Pilot (sandbox data only); self-hosted Helm in customer's k8s (most customers); BYOC managed in customer's cloud account (premium tier). The customer keeps the data plane. AegisAI infrastructure never sees a customer query.
Any one of those properties — deterministic, self-hosted, cross-cloud, identity-propagating — can be found in one product or another. The combination is rare. That's the gap we built to.
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.
