AI Security & Red Teaming · Spire Digi Solution

AI Agent Security & Runtime Defense Platform

A controlled environment for finding out how your agents, RAG and MCP tools actually fail — before an attacker does.

Delivered at: Spire Digi Solution
Role: Lead architect — threat model, scenario framework and platform design.

The problem

Agent deployments open new security boundaries — instructions, identity, tools, data, memory and external protocols all interacting in ways nobody fully designed for. Most teams ship agents without ever stress-testing those boundaries. This platform exists to find the failure modes first, under explicit authorization and scope, with evidence-backed findings instead of guesswork.

Architecture

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AI Agent Security & Runtime Defense PlatformProduction architecture — request flows top → bottom; security & observability span every layerClientslayer 1Assessor consoleREST / CLI clientsEdge & networklayer 2WAF & DDoS shieldL7 filtering · bot controlAPI gatewayrouting · versioningRate limits & quotasper tenant / per keymTLS · TLS 1.3encrypted in transitIdentity & accesslayer 3OIDC / OAuth2SSO · short-lived tokensRBAC + ABACleast privilegeTenant isolationdata · vectors · toolsSecrets → Vaultno static credentialsAI security & guardrailslayer 4focusNeMo Guardrailstopical & safety railsInjection / jailbreak filterinput inspectionPII & secret redactioninbound & outboundPolicy engine · OPAallow / deny decisionsOutput validationgrounding · citationsAssessment corelayer 5Target onboarding & scopeauthorized, signed-offCapability discoveryLLM · RAG · agent · tool · MCPScenario plannerOWASP LLM · MITRE ATLASControlled test executorbounded, fail-closedEvidence & evaluatorimmutable · model-assistedCorrelation & risk enginechained attack pathsExecution & toolslayer 6Sandboxed attack workersephemeral · least-privilegeTool & MCP adaptersscoped · fully auditedHard kill switchinstant stopRemediation & retesttracked to closureModel layerlayer 7LLM gateway · routing & quotasPrimary model + auto fallbackSelf-hosted · vLLM / TritonEmbeddings serviceData & statelayer 8PostgreSQLpgvector / QdrantRedis cacheObject storageImmutable audit logcross-cutting — applied across every layer aboveSECURITY & COMPLIANCE · CROSS-CUTTINGSIEM & threat detectionruntime alertsSupply-chain scanningTrivy · Semgrep · GitleaksSBOM & image signingprovenanceAdversarial mappingOWASP LLM Top 10 · MITRE ATLASComplianceEU AI Act · ISO 42001 · NIST AI RMFOBSERVABILITY & OPS · CROSS-CUTTINGOpenTelemetry tracesprompts · steps · toolsMetricsPrometheus / GrafanaEvaluation & regression gatesblock bad releases in CICost & latency analyticsper tenant / routeAlerting & on-callSLOs · error budgetsCI/CD · IaCKubernetes · GitOpsTEN ARCHITECTURE PILLARS · REVIEWED END TO ENDScalabilitySecurityAI Security / GuardrailsReliability & ResilienceObservabilityGovernanceCost OptimizationData & RAG SecurityPerformanceMaintainability / DevSecOpsLegendSecurityGovernance / accessOrchestrationData / modelProcessing / executionObservability / storageEvery scenario runs only inside an authorized, time-boxed scope; findings are evidence-backed and mapped to the OWASP LLM Top 10 and MITRE ATLAS.

Key capabilities

Authorized, scoped assessment workflow — nothing runs without explicit sign-off

Attack-surface discovery across LLM, RAG, agent, tool and MCP layers

Scenario-driven testing mapped to the OWASP LLM Top 10 and MITRE ATLAS

Evidence-backed findings with remediation guidance and retest tracking

Risk correlation across chained, multi-step attack paths

Architecture pillars

Designed and reviewed against all ten — see how I evaluate every architecture.

ScalabilitySecurityAI Security / GuardrailsReliability & ResilienceObservabilityGovernanceCost OptimizationData & RAG SecurityPerformanceMaintainability / DevSecOps

Technology

PythonFastAPIReactTypeScriptPostgreSQLRedisQdrant / pgvectorMCP adaptersOpenTelemetryDockerKubernetes

Want something like this, built properly?

I design and ship systems like this one — from architecture through to production.

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