AI Cyber Defense / Application, API and automated abuse defense
AI Cyber Defense: Authorize GraphQL fields and batch requests consistently
Enforce authorization inside relevant resolvers and bound total work rather than request count alone. Test aliases, batches and nested relationships against tenant boundaries.
Environment and inputs: Web applications, API gateways, application authorization services and staging abuse-case test environments.
Scenario and goal
A client can combine many object requests or nested fields in one authenticated call.
Why it matters
For this scenario, assume a client can generate many well-formed variations of a request and distribute work across sessions. Authenticate the caller, authorize the exact resource and bound the total business or compute effect as separate decisions. Edge filtering can reduce unwanted requests, but the service must still enforce its own object, tenant and transaction boundaries on every supported execution path.
Evidence to collect
Capture operation identity, depth or cost evaluation, resolver authorization results and batch size with sensitive arguments redacted.
Validate with an authorized exercise
Submit benign cross-tenant and high-cost fixture queries in staging; verify no forbidden field data is returned and the work budget applies to the entire operation.
Rollout and checks
Use synthetic tenants and data in staging and include positive cases for legitimate use. Match application audit records with edge decisions using a server-generated request identifier. Deploy changes with representative latency and error-rate monitoring, plus a clear rollback. Keep sensitive values out of diagnostic output so the test itself does not create an additional disclosure path.
Scope and limits
This guide proposes a defensive test; it is not a tested production design. Check platform support and local requirements. References explain the mechanisms but do not certify the design. Behavior alone does not prove AI involvement.