Ready, Set, Gen AI! Share Your Checklists and Protocols for Successful Integration

I am new to the AI game and learning fast. I am looking to use AI in the financial industry, using models at various level of reporting

The thing that strikes me is how similar data for LLM aligns to my own view on gathering data for myself. I want all of those same things in the data so I can have an accurate and holistic picture.

The thing that strikes me is how similar data for LLM aligns to my own view on gathering data for myself. I want all of those same things in the data so I can have an accurate and holistic picture.

Coming from a critical-infrastructure and data-centre background, I've found that readiness really starts before you pick a model. It's a data-governance and deployment question first, and a tooling question second. From a PM standpoint, I treat these as gate criteria in the plan rather than a status you tick off — readiness is a set of exit conditions each phase has to clear before it moves forward.

Here are the protocol elements I've found essential when preparing regulated workflows (law firms, banks, government) for Gen AI:

1. Start with a data classification gate. Before any data touches a model, classify it — public, internal, confidential, or regulated. That single step decides whether you can use a public API at all or whether you need a private, self-hosted deployment. Most of the readiness failures I've seen trace straight back to skipping it.
2. Make the deployment-topology call early. Map each use case to a public model, an enterprise SaaS product, or a customized private/on-prem LLM, based on how sensitive the data is and which compliance regime applies. You're trading cost and speed against control and security, and it's expensive to unwind once data has already flowed through the wrong topology.
3. Map your controls to a framework you already trust. Rather than inventing AI-specific controls from scratch, I line the workflow up against a recognized baseline — a payments use case against PCI-DSS, or a general one against ISO 27001 or NIST. It makes the audit conversations far easier later.
4. Define where a human stays in the loop. Decide up front where someone has to review the output before it's actioned, especially for anything client-facing or legally binding.
5. Check retention and residency. Confirm where prompts and outputs are actually stored and processed. For sovereign or cross-border clients, data residency is usually a legal requirement rather than a preference, and it often ends up being the deciding factor.

I'm curious whether others here are formalizing that data-classification step as a hard gate, or still handling it more case-by-case.

Coming from a critical-infrastructure and data-centre background, I've found that readiness really starts before you pick a model. It's a data-governance and deployment question first, and a tooling question second. From a PM standpoint, I treat these as gate criteria in the plan rather than a status you tick off — readiness is a set of exit conditions each phase has to clear before it moves forward.

Here are the protocol elements I've found essential when preparing regulated workflows (law firms, banks, government) for Gen AI:

1. Start with a data classification gate. Before any data touches a model, classify it — public, internal, confidential, or regulated. That single step decides whether you can use a public API at all or whether you need a private, self-hosted deployment. Most of the readiness failures I've seen trace straight back to skipping it.
2. Make the deployment-topology call early. Map each use case to a public model, an enterprise SaaS product, or a customized private/on-prem LLM, based on how sensitive the data is and which compliance regime applies. You're trading cost and speed against control and security, and it's expensive to unwind once data has already flowed through the wrong topology.
3. Map your controls to a framework you already trust. Rather than inventing AI-specific controls from scratch, I line the workflow up against a recognized baseline — a payments use case against PCI-DSS, or a general one against ISO 27001 or NIST. It makes the audit conversations far easier later.
4. Define where a human stays in the loop. Decide up front where someone has to review the output before it's actioned, especially for anything client-facing or legally binding.
5. Check retention and residency. Confirm where prompts and outputs are actually stored and processed. For sovereign or cross-border clients, data residency is usually a legal requirement rather than a preference, and it often ends up being the deciding factor.

I'm curious whether others here are formalizing that data-classification step as a hard gate, or still handling it more case-by-case.

1. Start with a data classification gate. Before any data touches a model, classify it — public, internal, confidential, or regulated. That single step decides whether you can use a public API at all or whether you need a private, self-hosted deployment. Most of the readiness failures I've seen trace straight back to skipping it.
2. Make the deployment-topology call early. Map each use case to a public model, an enterprise SaaS product, or a customized private/on-prem LLM, based on how sensitive the data is and which compliance regime applies. You're trading cost and speed against control and security, and it's expensive to unwind once data has already flowed through the wrong topology.
3. Map your controls to a framework you already trust. Rather than inventing AI-specific controls from scratch, I line the workflow up against a recognized baseline — a payments use case against PCI-DSS, or a general one against ISO 27001 or NIST. It makes the audit conversations far easier later.
4. Define where a human stays in the loop. Decide up front where someone has to review the output before it's actioned, especially for anything client-facing or legally binding.
5. Check retention and residency. Confirm where prompts and outputs are actually stored and processed. For sovereign or cross-border clients, data residency is usually a legal requirement rather than a preference, and it often ends up being the deciding factor.