AI Governance Command Center Track
Guided course for enterprise AI governance command center design.
Open Track →Secure TheCloud Labs
Guided LABs for identity, authorization, workload risk, detection reasoning, AI governance, executive readiness, and architecture validation.
Guided learning paths
Guided course for enterprise AI governance command center design.
Open Track →Searchable catalog
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 →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 →