sectum-ai tackles a problem that's only going to get more painful as multi-tenant AI products mature: how do you actually prove that Tenant A's data isn't bleeding into Tenant B's model outputs, RAG retrievals, embeddings, agent memories, or fine-tunes? The repo bills itself as a multi-tenant AI verification platform that provisions synthetic tenants, hunts for cross-tenant data leakage across every AI surface, and emits tamper-evident evidence of what it found.

That framing is unusually concrete for an AI-safety adjacent project. Most "AI red team" tooling stops at prompt injection and jailbreak corpora. Sectum is aiming a layer deeper — at the isolation boundary itself, which is where regulators, enterprise security teams, and SOC2/ISO auditors are starting to ask hard questions that few vendors can answer rigorously.

What makes the approach interesting:

• Synthetic tenant provisioning means you can run leakage probes without touching real customer data — a big deal for production safety.
• "Every AI surface" implies coverage across chat, RAG, embeddings, agent state, and possibly fine-tunes — the surfaces auditors don't yet know to ask about.
• Tamper-evident evidence hints at cryptographically signed audit artifacts, which is exactly what compliance workflows need but rarely get from AI tooling.

Who should care: platform security engineers at SaaS companies bolting LLMs onto multi-tenant products, AI infrastructure teams building shared RAG or agent systems, and GRC/compliance folks trying to map traditional tenant-isolation controls onto stochastic systems. Independent consultants doing AI assurance work could also use it as a structured test harness rather than rolling bespoke probes per engagement.

At zero stars and a fresh push, this is clearly early — but the problem statement is sharp, the scope is honest, and the niche is genuinely underserved.