Enterprise Agent Delivery & Consulting
Client-facing architecture, agent engineering, RAG, tool use, orchestration, gateway and MCP integration, policy, Responsible AI, observability, cloud delivery, and consulting leadership.
Open Delivery Path →Secure TheCloud Labs
Guided LABs for identity, authorization, workload risk, detection reasoning, AI governance, executive readiness, and architecture validation.
Guided learning paths
Client-facing architecture, agent engineering, RAG, tool use, orchestration, gateway and MCP integration, policy, Responsible AI, observability, cloud delivery, and consulting leadership.
Open Delivery Path →Guided course for enterprise AI governance command center design.
Open Track →Searchable catalog
Start Here
New to SecureTheCloud Labs? Start with the recommended learning chain, then follow the tracks in a connected sequence from AI governance to AI security engineering.
Recommended learner flow: Start with AI Governance Command Center, continue into AI Security Engineering L2 Track, complete Cloud Security Operations L2 Track, then build toward role-based portfolio progression.
Navigation: recommended sequence, role paths, portfolio-ready progression.
Open Start Here →Deliver · Applied Client Path
Apply governed AI architecture in client-facing delivery work—from discovery and workflow design through agent engineering, integration, cloud delivery, executive communication, and evidence-backed readiness decisions.
One coherent nine-module learning path. Open the track for the complete curriculum map, or expand the catalog list below only when you need a specific LAB.
Role-architecture LAB mapping enterprise discovery, agent and RAG design, gateway and MCP interoperability, policy, Responsible AI, observability, cloud delivery, and senior-consultant execution into one governed operating model.
Track: enterprise-agent-developer
Open Lab →Design controlled conversational agents using explicit task contracts, structured outputs, state and memory boundaries, deterministic approvals, refusal paths, loop limits, and human handoff.
Track: enterprise-agent-developer
Open Lab →Design governed RAG and tool-use workflows using source authority, metadata and tenant filters, hybrid retrieval, context validation, provenance, injection defenses, schema-validated tools, least privilege, approvals, and evaluation evidence.
Track: enterprise-agent-developer
Open Lab →Design controlled routing and multi-agent workflows using deterministic rules, bounded model classification, supervisor and specialist contracts, typed handoffs, external state ownership, hard limits, fallbacks, duplicate prevention, and trace evidence.
Track: enterprise-agent-developer
Open Lab →Design governed enterprise integration using gateway policy boundaries, MCP trust and capability contracts, persistent registry lifecycle, OpenAPI and JSON Schema validation, least privilege, versioning, compatibility, limits, rollback, and trace evidence.
Track: enterprise-agent-developer
Open Lab →Design typed OPA/Rego policy decisions, deterministic enforcement boundaries, approvals, residency and rate-limit controls, provenance, explanation, fairness review, appeal, and audit evidence.
Track: enterprise-agent-developer
Open Lab →Design agent telemetry, traces, SLOs, error budgets, token and cost controls, caching, fallback, capacity, and performance evidence without executing live observability or runtime controls.
Track: enterprise-agent-developer
Open Lab →Design immutable container packaging, software-supply-chain evidence, CI/CD gates, environment promotion, workload identity, rollback, cloud deployment contracts, paved roads, and cross-team ownership without executing live delivery operations.
Track: enterprise-agent-developer
Open Lab →Lead a synthetic enterprise agent engagement from discovery and process mapping through architecture, governance, delivery planning, executive communication, mentorship, operating-model design, and evidence-backed readiness decisions.
Track: enterprise-agent-developer
Open Lab →Secure · Adversarial Validation Path
Design authorized, reviewer-safe AI red-team scenarios covering prompt injection, tool abuse, retrieval poisoning, data exposure, runaway-agent cost risk, approval bypass, evidence capture, and remediation—without executing live attacks or touching production systems.
One coherent nine-module learning path. Open the track for the complete curriculum map, or expand the catalog list below only when you need a specific LAB.
Intermediate capstone LAB combining AI red-team scenario design, evidence capture, control mapping, risk explanation, remediation planning, uncertainty handling, and executive-ready reporting without live execution.
Track: ai-red-team-scenario-design
Open Lab ->Intermediate LAB teaching safe evidence capture for AI red-team findings: objectives, preconditions, expected controls, observed behavior, findings, uncertainty, remediation, and reviewer-safe documentation.
Track: ai-red-team-scenario-design
Open Lab ->Intermediate LAB teaching safe human approval bypass scenario design: approval gates, escalation paths, policy decisions, recommendation-versus-execution boundaries, evidence capture, uncertainty, and non-execution constraints.
Track: ai-red-team-scenario-design
Open Lab ->Intermediate LAB teaching safe agent loop and cost abuse scenario design: loop boundaries, retry controls, tool-call limits, quota risk, cost containment, evidence capture, and non-execution constraints.
Track: ai-red-team-scenario-design
Open Lab ->Intermediate LAB teaching safe data exfiltration scenario design: sensitive-data boundaries, synthetic evidence, policy controls, disclosure risk, uncertainty, and non-execution constraints.
Track: ai-red-team-scenario-design
Open Lab ->Intermediate LAB teaching safe retrieval poisoning scenario design: source authority, tenant boundaries, stale content risk, retrieval trust, evidence capture, uncertainty, and non-execution constraints.
Track: ai-red-team-scenario-design
Open Lab ->Intermediate LAB teaching safe tool-abuse scenario design: tool authority boundaries, permission scope, approval gates, unsafe delegation risk, evidence capture, uncertainty, and non-execution constraints.
Track: ai-red-team-scenario-design
Open Lab ->Intermediate LAB teaching safe prompt-injection scenario design: instruction hierarchy, untrusted input boundaries, retrieved content risk, expected controls, evidence capture, uncertainty, and non-execution boundaries.
Track: ai-red-team-scenario-design
Open Lab ->Intermediate LAB introducing safe AI red-team scenario design: scope, authorization, failure-mode reasoning, evidence capture, uncertainty, remediation, and non-execution boundaries.
Track: ai-red-team-scenario-design
Open Lab ->Operate · Detection and Response Path
Build practical cloud security operations judgment across event classification, IAM activity, workload and network signals, control-plane evidence, incident timelines, detection-rule reasoning, executive communication, and reviewer-safe evidence packages.
One coherent nine-module learning path. Open the track for the complete curriculum map, or expand the catalog list below only when you need a specific LAB.
Intermediate LAB teaching detection rule reasoning, false positive review, false negative risk, tuning thresholds, evidence quality, and safe detection explanations.
Track: cloud-security-operations
Open Lab →Intermediate LAB teaching executive security summaries that convert operational evidence into concise, bounded, non-overstated leadership-ready communication.
Track: cloud-security-operations
Open Lab →Intermediate capstone-style LAB teaching repeatable evidence packages for detection, triage, escalation, executive summaries, and portfolio-ready cloud security operations artifacts.
Track: cloud-security-operations
Open Lab →Intermediate LAB teaching cloud incident timeline and escalation narratives: event sequencing, evidence anchors, uncertainty handling, severity and confidence language, reviewer-safe summaries, escalation paths, and bounded incident communication.
Track: cloud-security-operations
Open Lab →Intermediate LAB teaching cloud control-plane incident evidence: administrative API calls, resource changes, policy edits, actor/action/resource timelines, evidence quality, escalation, and bounded incident narratives.
Track: cloud-security-operations
Open Lab →Intermediate LAB teaching workload and network signal triage: compute events, storage access, public exposure, network paths, service behavior, suspicious workload activity, evidence quality, escalation, and bounded response narratives.
Track: cloud-security-operations
Open Lab →Intermediate LAB teaching IAM activity triage: role assumption, privilege changes, access key activity, policy edits, failed access, suspicious identity behavior, evidence quality, escalation, and safe response narratives.
Track: cloud-security-operations
Open Lab →Intermediate LAB teaching cloud event and signal classification by source, actor, asset, action, severity, confidence, tenant context, and evidence quality.
Track: cloud-security-operations
Open Lab →Intermediate LAB introducing cloud security operations: detection, triage, event context, evidence collection, escalation, incident narrative, and safe production boundary language.
Track: cloud-security-operations
Open Lab →Secure · AI System Engineering Path
Build secure AI system engineering judgment across architecture, prompt boundaries, tool permissions, retrieval controls, output safety, runtime guardrails, abuse and cost controls, testing harnesses, and audit-ready evidence.
One coherent nine-module learning path. Open the track for the complete curriculum map, or expand the catalog list below only when you need a specific LAB.
Intermediate LAB teaching AI security testing and evidence harness design: prompt boundary tests, tool permission tests, retrieval tests, output safety checks, runtime guardrail tests, abuse/cost tests, expected outcomes, and audit-ready evidence packages.
Track: ai-security-engineering
Open Lab →Intermediate LAB teaching AI abuse, cost, and rate limit engineering: token budgets, quota controls, repeated attempts, expensive retrieval/tool paths, throttling, denial, and evidence capture.
Track: ai-security-engineering
Open Lab →Intermediate LAB teaching AI runtime guardrails and failure mode engineering: loop control, retries, timeouts, deny paths, fail-closed behavior, circuit breakers, degradation, escalation, and evidence capture.
Track: ai-security-engineering
Open Lab →Intermediate LAB teaching AI output safety and response policy engineering: response classification, sensitive data handling, grounded answers, refusal behavior, escalation, unsafe-response prevention, and evidence capture.
Track: ai-security-engineering
Open Lab →Intermediate LAB teaching secure retrieval engineering for AI systems: source authority, tenant scope, sensitivity, freshness, retrieval poisoning resistance, context packaging, and evidence capture.
Track: ai-security-engineering
Open Lab →Intermediate LAB teaching scoped AI tool permissions, action classification, read-only vs mutating tool boundaries, approval gates, self-approval prevention, and evidence capture.
Track: ai-security-engineering
Open Lab →Intermediate LAB teaching how to engineer prompt boundaries by separating trusted instructions from untrusted user input, retrieved content, tool output, and model-generated recommendations.
Track: ai-security-engineering
Open Lab →Intermediate LAB teaching secure AI application architecture patterns across frontend, backend/API, model, retrieval, tool, policy, approval, evidence, observability, and runtime boundaries.
Track: ai-security-engineering
Open Lab →Intermediate LAB introducing secure AI system engineering, AI threat surfaces, control boundaries, prompt/model/retrieval/tool/policy/evidence layers, and the relationship between AI governance and AI security engineering.
Track: ai-security-engineering
Open Lab →Govern · Authority and Evidence Path
Build enterprise AI governance judgment across intake, risk tiering, policy gates, human approval, agent workflow boundaries, retrieval risk, tool-use risk, audit evidence, observability, cost control, operational handoff, and executive communication.
One coherent twelve-module learning path. Open the track for the complete curriculum map, or expand the catalog list below only when you need a specific LAB.
Intermediate LAB teaching how to govern AI cost, token usage, runaway agent loops, repeated tool attempts, expensive retrieval calls, rate limits, budget thresholds, and operational evidence.
Track: ai-governance-command-center
Open Lab →Intermediate LAB teaching how to build audit-ready evidence trails for AI decisions, prompts, retrieved context, tool attempts, policy decisions, human approvals, blocked actions, and executive summaries.
Track: ai-governance-command-center
Open Lab →Intermediate LAB teaching how human approval gates prevent AI agents, tool-use, prompt injection, and retrieval risk from becoming ungoverned enterprise execution.
Track: ai-governance-command-center
Open Lab →Intermediate LAB teaching how RAG and retrieval systems create AI governance risk when trusted, untrusted, sensitive, stale, or poisoned context is retrieved and treated as authority.
Track: ai-governance-command-center
Open Lab →Intermediate LAB teaching how prompt injection can manipulate AI agent instructions, tool selection, policy bypass attempts, and approval-gated execution paths.
Track: ai-governance-command-center
Open Lab →Intermediate LAB teaching how AI agent tool-use becomes enterprise risk when recommendations, API calls, human approvals, and autonomous execution boundaries are not clearly governed.
Track: ai-governance-command-center
Open Lab →Learn how an enterprise AI governance command center connects intake, risk, policy, approvals, evidence, observability, and operational handoff.
Track: ai-governance-command-center
Open Lab →Learn how intake questions and risk tiers create consistent AI governance triage before a workflow reaches production.
Track: ai-governance-command-center
Open Lab →Learn how policy gates convert AI governance rules into deterministic allow, deny, or approval-required decisions.
Track: ai-governance-command-center
Open Lab →Learn how governed agent workflows separate recommendation, approval, and execution boundaries.
Track: ai-governance-command-center
Open Lab →Learn how traces, cost controls, guardrails, SOPs, and runbooks make AI workflows operable after demo or deployment.
Track: ai-governance-command-center
Open Lab →Learn how to present an AI governance command center to executives while preserving case-study, demo, and production boundaries.
Track: ai-governance-command-center
Open Lab →AWS · Identity and Security Foundations
Build AWS security judgment across IAM evaluation, permission boundaries, service control policies, resource policies, public-access controls, encryption authority, secrets access, workload identity, detection evidence, cross-account trust, and privilege-escalation paths.
One coherent thirteen-module AWS learning path. Start with IAM fundamentals, then expand the catalog only when you need a specific AWS security LAB.
Foundational AWS IAM lab focusing on identity evaluation, least privilege, and interview-ready reasoning.
Open Lab →Intermediate LAB teaching AWS effective-permission reasoning across identity policies, resource policies, permission boundaries, session policies, SCPs, and explicit deny.
Open Lab →Intermediate LAB teaching how AWS permission boundaries constrain maximum principal authority without granting access by themselves.
Open Lab →Intermediate LAB teaching how AWS Service Control Policies define organization-level maximum permissions without granting access by themselves.
Open Lab →Intermediate LAB teaching how AWS resource-based policies participate in authorization decisions alongside identity policies, boundaries, SCPs, and explicit deny.
Open Lab →Applied L2 LAB teaching how S3 public access risk emerges from bucket policy, Block Public Access settings, ACL history, identity permissions, resource policies, and organization guardrails.
Open Lab →Intermediate LAB teaching how AWS KMS key policies, IAM permissions, grants, encryption context, and organization guardrails combine to control cryptographic access.
Open Lab →Intermediate LAB teaching how AWS Secrets Manager access depends on IAM permissions, resource policies, KMS decrypt authority, explicit deny, and organization guardrails.
Open Lab →Intermediate LAB teaching how Lambda execution roles create workload identity risk when function update authority, iam:PassRole, Secrets Manager access, KMS decrypt, and resource permissions combine.
Open Lab →Intermediate LAB teaching how CloudTrail evidence supports detection reasoning for iam:PassRole, sts:AssumeRole, Lambda updates, Secrets Manager access, KMS decrypt, and workload identity activity.
Open Lab →Principal LAB modeling deterministic AWS cross-account trust reachability through sts:AssumeRole, Shield finding linkage, and Aegis Runtime identity signal mapping.
Open Lab →Principal LAB modeling deterministic AWS privilege escalation through iam:PassRole combined with compute service creation or update capability.
Open Lab →Principal LAB modeling deterministic AWS privilege expansion through chained sts:AssumeRole paths across multiple IAM roles.
Open Lab →Starter lab introducing Azure Entra ID identity flow, Conditional Access, and RBAC concepts.
Open Lab →Starter lab covering Google Cloud IAM principals, policy bindings, and request-time authorization.
Open Lab →▣ Complete Learning Path
A planned SecureTheCloud Labs intermediate track for secure AI system engineering: prompt boundaries, tool permissions, retrieval controls, runtime guardrails, abuse controls, testing harnesses, and evidence packages.
Real-world labs and exercises that build production-ready security skills.
Planned learning paths that progress from foundational to advanced.
Security and governance practices for modern cloud and AI systems.
Education-first platform with no backend exposure and no live enforcement.
Complete Learning Path
Completed downstream path after AI Governance, AI Security Engineering, and Cloud Security Operations. Learn how to design safe AI red-team scenarios without live exploit execution, customer data access, credential handling, or production mutation.
Completed learning path for prompt injection scenario design, tool-abuse reasoning, retrieval poisoning review, data exposure scenarios, agent loop/cost abuse, approval bypass testing, evidence capture, and capstone reporting.
Status: complete track - 9 of 9 modules implemented - no live red-team execution.
Open AI Red Team Scenario Design Track →