SecureTheCloudSecureTheCloudEngineering PortfolioContact Ola

ENGINEERING EVIDENCE FOR ENTERPRISE AI

Ola OmoniyiPrincipal AI Platform, Trust & Security Architect

Governed systems.Built, tested, and ready to explain.

A proof-of-work portfolio for secure, observable, build-to-ship AI platforms across agentic systems, RAG, EvalOps, cloud infrastructure, identity, governance, and regulated environments.

Explore live systems Review GitHub
Principal-level architecture backed by code, tests, traces, dashboards, and explicit boundaries.
90+Engineering labs and platform implementations
15+Years across infrastructure, cloud, security, and AI
3Cloud ecosystems: AWS, Azure, and Google Cloud
1Evidence-first build-to-ship standard

LIVE ENTERPRISE SYSTEMS

Six live surfaces. One governed enterprise architecture.

Publicly inspectable platform surfaces spanning runtime operations, Kubernetes governance, agent evidence, cross-zone trust, risk intelligence, and deterministic enterprise knowledge.

Enterprise control-plane surfaceConnected preview

Platform Command Center

A platform-wide operations console for runtime posture, authorization decisions, tenant and session visibility, risk analysis, observability, and operator workflows.

  • Runtime and authorization telemetry
  • Tenant, session, risk, and operator views
  • Integrated observability and evidence surfaces
Boundary

Live preview surface; production authorization and enforcement claims depend on connected backend services and controlled rollout status.

Open live system
Kubernetes runtime governanceLive governed demo

Kubernetes Sentinel

A governed Kubernetes command center for runtime posture, OPA decision visibility, Aegis and ASZ context, SOC 2 evidence readiness, and custodian-controlled rollout boundaries.

  • OPA remains deterministic decision authority
  • Runtime posture and evidence readiness
  • Custodian-controlled rollout and approval model
Boundary

Production enforcement is intentionally blocked; no live validating or mutating webhook authority is claimed.

Open live system
Agent evidence and audit custodyLive read-only surface

Agent Blackbox

An enterprise evidence layer for AI-agent action visibility, session evidence, audit-chain integrity, backend proof, governance boundaries, and activation readiness.

  • Allowlisted read-only backend checks
  • Agent and session evidence review
  • Audit, integrity, and governance boundary visibility
Boundary

Display-only and read-only: no token issuance, session creation, secret retrieval, infrastructure mutation, or enforcement activation.

Open live system
Frontend live · backend pending

ASZ Trust Operations

Cross-zone agent trust

Open
Live static evidence console

Agent Risk Exchange

Agent risk and evidence exchange

Open
Live deterministic demo

Trust Intelligence Copilot

Governed enterprise intelligence

Open
Review the integrated architecture, authority boundaries, and system roles

CAPABILITY MAP

One portfolio. Six engineering domains.

The work connects architecture, implementation, assurance, identity, operations, and delivery rather than treating them as separate demonstrations.

01

Enterprise AI Platform Architecture

Reusable services, AI gateways, APIs, microservices, cloud-native delivery patterns, and platform standards for enterprise adoption.

Platform ArchitectureAPIsCloud Native
02

Agentic AI, RAG & Knowledge Systems

Governed orchestration, retrieval, tool use, structured outputs, state, memory, and human-review workflows across multiple frameworks.

LangGraphRAGTool Use
03

LLM EvalOps & Release Engineering

Benchmark datasets, deterministic evaluation, regression gates, safety testing, release evidence, and fail-closed quality controls.

EvalOpsQuality GatesEvidence
04

AI Trust, Identity & Governance

Delegated access, non-human identity, least privilege, policy decisions, authority boundaries, auditability, and Responsible AI controls.

IAMOPAGovernance
05

Developer Platform & Build-to-Ship Delivery

Terraform, Kubernetes, Docker, GitHub Actions, structured validation, deployment evidence, rollback planning, and reusable accelerators.

CI/CDTerraformKubernetes
06

Observability, Data & Enterprise Integration

Runtime telemetry, traces, policy decisions, data pipelines, API integration, PostgreSQL, Redis, analytics, and operational readiness.

ObservabilityPostgreSQLIntegration

FLAGSHIP SYSTEMS

Six systems that prove the operating model.

Selected for recruiter impact: platform breadth, hands-on implementation, measurable evidence, regulated-system judgment, and clear engineering boundaries.

Six systems selected from 90+ buildsThe flagship set is intentionally small. It proves full-stack platform engineering, Agentic AI, LLM quality, regulated AWS architecture, enterprise identity, and operational governance without turning the homepage into a repository index.
01 · Enterprise AI OperationsValidated

SecureTheCloud Operational Intelligence Fabric

Full-stack AI operations platform connecting telemetry, policy, AI investigation, human approval, and evidence replay.

A governed operations platform for regulated environments with a Go operational API, Python AI investigation service, Next.js workspace, OPA policy correlation, audit records, and human-in-the-loop decisions.

  • Go operational API and Python AI investigation service
  • OPA policy correlation with explicit AI authority boundaries
  • Human approval workflow, audit chain, and evidence replay
GoPythonNext.jsFastAPIOPADockerHuman ApprovalEvidence Replay
Repositorypublic · Go / Python / TypeScript
EvidencePublic repository · Verified MVP screenshots · Acceptance criteria
Open project evidence
View repository Review MVP evidence
02 · LLM Quality & Release EngineeringLive

LLM EvalOps & Release Evidence Platform

Deterministic evaluation, RAG quality, safety red-team testing, regression gates, and fail-closed release evidence.

A production-style Python evaluation platform with provider abstraction, JSONL datasets, prompt boundaries, model comparison, RAG metrics, safety checks, experiment tracking, model aliases, CI gates, and a public release dashboard.

  • Automated regression, RAG, safety, and release-readiness gates
  • Controlled failure exits non-zero when required retrieval evidence is missing
  • Public dashboard distinguishes ready and blocked release evidence
PythonPytestRAG EvaluationGitHub ActionsOpenAI AdapterJSONLRed TeamingStatic Dashboard
Repositorypublic · Python
EvidenceLive dashboard · Public repository · Controlled failure runbook
Open project evidence
Open live dashboard View repository
03 · Backend AI Product EngineeringIn build

Agentic Career Intelligence Platform

A real backend product moving from job ingestion to explainable, persisted, personalized career recommendations.

An enterprise-style FastAPI platform with PostgreSQL, SQLAlchemy, Alembic, Redis, Docker, company and job services, deterministic scoring, career-profile APIs, personalized matching, persisted evidence, seed data, and smoke tests.

  • Company, job, profile, skill, certification, project, and experience APIs
  • Deterministic job scoring and persisted personalized match evidence
  • Reproducible seed flow and smoke-tested API paths
PythonFastAPIPostgreSQLSQLAlchemyAlembicRedisDocker ComposeREST APIs
Repositoryprivate · Python
EvidenceBackend validation · Persisted decision evidence · Architecture artifact
Open project evidence
Request private walkthrough
04 · Regulated Healthcare AIIn build

AI-Assisted Psychiatry Intake & Charting Platform

A clinician-controlled AWS healthcare workflow for telehealth intake, transcription, structured chart drafting, approval, and auditability.

A HIPAA-aligned architecture and active build for behavioral-health intake and clinical documentation, combining secure portals, video consultation, medical transcription, speaker separation, structured AI chart drafts, clinician review, encrypted PHI, access controls, audit logging, and EHR/FHIR integration patterns.

  • End-to-end patient, clinician, transcription, draft, review, and export workflow
  • HIPAA-oriented identity, encryption, audit, retention, and least-privilege controls
  • Explicit clinician authority boundary before final chart export
AWSAmazon CognitoHealthScribeTranscribe MedicalKMSCloudWatchFHIRHuman-in-the-Loop
Repositoryprivate · HCL
EvidenceClinical workflow architecture · AWS reference architecture · Human approval gates
Open project evidence
Request architecture walkthrough
05 · Agentic AI Framework EngineeringLab complete

Multi-Framework Agentic Evidence Lab

The same governed RAG workflow implemented four ways to compare framework behavior fairly rather than compare unrelated demos.

A completed Python engineering lab implementing a shared evidence-review workflow across LangChain, LangGraph, Strands, and ADK-style patterns with common schemas, tools, datasets, benchmark questions, trace envelopes, scoring rubrics, structured outputs, and human-review routing.

  • One canonical workflow, dataset, schema, tools, benchmarks, and rubric
  • Stateful orchestration, tool calling, structured output, and human-review routing
  • Machine-readable reports, traces, comparison matrix, and phase verification
PythonLangChainLangGraphStrandsADKPydanticRAGTrace Logging
Repositorypublic · Python
EvidencePublic repository · Comparison matrix · Phase closure evidence
Open project evidence
View repository Open comparison matrix
06 · Agent Identity & AuthorizationValidated

Brokered Agent Delegation & Enterprise API Security

Secure cross-application agent action without broad standing privileges or independent superuser authority.

A deterministic Python implementation of user-bound delegation, OAuth 2.0 token-exchange concepts, OIDC claims validation, policy-as-code authorization, scoped delegated tokens, API-side enforcement, abuse-case testing, and evidence records for allow and deny decisions.

  • User-bound, audience-bound, scope-limited delegated access
  • Independent downstream API validation and deny-by-default policy
  • 43 validation tests plus full-chain allow and deny evidence records
PythonOAuth 2.0OIDCPolicy as CodeRegoPytestJSON EvidenceEnterprise APIs
Repositorypublic · Python / Rego
EvidencePublic repository · Threat and security model · Validation evidence
Open project evidence
View repository Review security model

EVIDENCE MODEL

Credibility comes from precise claims.

The portfolio separates deployed systems, validated builds, active builds, completed engineering labs, reference architectures, and roadmap capabilities. That makes the work easier to trust and evaluate.

01

Implementation status

Every project uses one controlled status: deployed, production-smoke-tested, built and validated, active build, completed engineering lab, reference architecture, or roadmap.

02

Engineering artifacts

Repositories, schemas, APIs, tests, dashboards, traces, diagrams, evidence packages, release records, and runbooks support each claim.

03

Explicit boundaries

Project records state what is implemented, simulated, private, planned, and specifically not being claimed as production operation.

04

Operational proof

The strongest builds show validation, observability, repeatability, failure handling, governance, human authority, and a credible path from prototype to deployment.

THE BUILDER BEHIND THE WORK

Principal AI Platform, Trust & Security Architect.

I design and build governed, observable, secure enterprise AI platforms that connect intelligence to identity, policy, evidence, APIs, cloud infrastructure, and operational controls.

This site is the technical proof layer for SecureTheCloud: repositories, system architecture, evaluation results, runtime evidence, and clear boundaries for what is implemented versus planned.

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