Build Robust Enterprise AI Vaults

:Hey High Stakers,:

Good morning and welcome to the 12th issue of High Stakes!

Why now? Because AI’s shift from hype to reality is demanding a data foundation that’s built to last. 

What's new? Because enterprises are blending governance, privacy, and cost control into a single AI-ready vault. 

This piece shows how.

1‑Min Briefing 

GenAI flops when the data plumbing behind it leaks. 

Cue the “AI‑ready vault”: an organised, policy‑driven store of clean, well‑labelled, traceable data. Some call it old‑school governance rebranded, others see new needs like token‑level cost tracking.

Either way, mid‑market lender Stonebrook Capital (fictional) shows why the vault matters, and how six practical moves turn their data sprawl into an AI growth engine.

Stonebrook at a Glance

(*all names changed)

Birmingham‑based Stonebrook Capital manages a £6 billion loan book with 1 100 employees and ambition to become “AI first.” 

The CTO had promised faster credit approvals by plugging GenAI into underwriting. 

But pilots soon hit turbulence.

• Governance lived in slide decks, never in code.

• Analytics teams chased unlabelled CSVs morphing into risk features with no breadcrumb trail.

• Raw customer data surfaced in test sets - PII regulatory red flags.

• Storage and compute costs jumped 25 % YoY with zero charge‑back.

• Unvetted samples skewed fraud models, causing nearly £800 k in false‑positive losses.

• 15‑year‑old core systems clashed with new AI APIs, delaying 10 % of loan approvals and stretching underwriting cycle time by 20 %.

The CTO hit pause and set a nine‑month clock to rebuild data foundations.

Here then are some ways we turned around (still on the job, 6 months in) the situation. Do the six pillars below ring true to your situation?

Six Pillars of an AI‑Ready Vault (Stonebrook fixes)

1️⃣ End‑to‑End Data Lineage

Stonebrook fix: deployed open‑metadata scanners (those free crawlers that read schemas and pipelines) and visual lineage graphs in 90 days. 

Tip: Start with high‑risk tables - loans, KYC, payments and build lineage checks into CI so bad pulls fail fast.

Proof: Morgan Stanley case study was our inspiration where they used lineage‑rich evals to hit 98 % advisor adoption; Lowe’s too lifted tagging accuracy by 20 %.

2️⃣ Ingest‑to‑Feature Maps

Stonebrook fix: published a "yellow‑brick‑road" diagram (a one‑pager showing each hop from source systems to model features) within six weeks.

Quick win: appoint a data “traffic cop” who approves new feature‑store columns before they ship.

Proof: Mercado Libre aligned 17 k devs; Vodafone saves 3 h per employee weekly.

3️⃣ Automated Privacy Tagging

Stonebrook fix: added column‑level sensitivity tags (think Excel columns stamped "Contains SSN" or "DOB - Encrypt") via dbt tests; build fails if new PII appears unmasked.
Pro‑tip: two sprints are enough when you focus on top‑20 critical tables first.
Proof: BBVA built 2 900 safe GPTs; Klarna resolves 2.3 m chats compliantly.

4️⃣ Transparent Cost Allocation
 Stonebrook fix: piped Snowflake & S3 tag data into FinOps dashboards (or even simple PowerBI cost widgets), billing usage back to business units - they cut spend 12 % in one quarter.
Tip: surface “cost‑per‑answer” for every LLM; execs will trim prompts fast.
Proof: Klarna’s SaaS clean‑up apparently saved $10 m 

5️⃣ Synthetic Data Guardrails
 Stonebrook fix: gated generation behind an approval flow and stored provenance links (real rows ↔ synth rows) so bias audits take minutes, not days.
Best practice: benchmark model drift on real vs synth sets before go‑live.
Proof: BBVA’s active‑learning loop underpins safe credit data; regulators now expect such logs.

6️⃣ Integrated Tech Stack
 Stonebrook fix: wrapped the core banking system with a lightweight gRPC API layer (think translator between COBOL and modern microservices), holding latency under 150 ms.
Quick win: schedule a "compatibility day" every sprint, so that is pairs of devs test legacy + new APIs side by side.
Proof: Lowe’s hybrid stack and Indeed run legacy + GPT smoothly while boosting KPIs. 

I am convinced that building an AI-ready vault is a robust way to avoid 2 steps forward and 3 steps backwards! 

Especially after seeing the ground realities and the “messy middle” that enterprises have that can stop the most determined CTO on their tracks.  

Before I go, here’s how the Stonebrook story is chugging along. 

Stonebrook tackled the six pillars as parallel work‑streams:

• 90 days in: lineage scanners live, top‑risk tables mapped; FinOps dashboard exposing real‑time cost‑per‑query.
  

• 6 months in: four pillars (lineage, cost, privacy tags, ingest‑to‑feature maps) are production‑grade. Cloud spend down 18 %, loan‑approval latency cut by 12 %, and false‑positive fraud alerts slashed by 40 %. Synthetic‑data guardrails and full gRPC rollout are tracking for Q4.
  

One last thing. 

If you want to do something like this, remember - treat the vault as a product. Assign an owner, publish a backlog, and score progress in fewer incidents, lower token‑per‑decision cost, and faster go‑lives.

Best,
Srini

P.S. The AI vault isn’t just IT hygiene, it’s the difference between pilot fatigue and production wins. Start small, but start with ownership.