When you can’t see how an answer was formed, you can’t trust it
12 February 2026
Six Pillars of Trustworthy Financial AI – Auditability
LLMs break the model of traditional auditability.
In classic software, deterministic code, reproducible outcomes, transparent logs, 𝗮𝗻𝗱 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗮𝗯𝗹𝗲 𝗯𝗲𝗵𝗮𝘃𝗶𝗼𝘂𝗿 give you a clean chain of causality. You always know 𝘩𝘰𝘸 a result was produced.
Generative AI changes that.
LLMs don’t reveal their internal steps, and when you ask them to “show their working,” they generate a plausible story 𝘢𝘧𝘵𝘦𝘳 the answer. It’s a reconstruction, not a trace.
That’s why we can’t treat generative output as inherently trustworthy. LLMs are incredibly fluent, confident, coherent, 𝗮𝗻𝗱 𝗰𝗼𝗺𝗽𝗲𝗹𝗹𝗶𝗻𝗴 but:
• 𝗙𝗹𝘂𝗲𝗻𝗰𝘆 𝗱𝗼𝗲𝘀 𝗻𝗼𝘁 𝗶𝗺𝗽𝗹𝘆 𝗰𝗼𝗿𝗿𝗲𝗰𝘁𝗻𝗲𝘀𝘀
• 𝗖𝗼𝗻𝗳𝗶𝗱𝗲𝗻𝗰𝗲 𝗱𝗼𝗲𝘀 𝗻𝗼𝘁 𝗶𝗺𝗽𝗹𝘆 𝘁𝗿𝘂𝘁𝗵
• 𝗖𝗼𝗵𝗲𝗿𝗲𝗻𝗰𝗲 𝗱𝗼𝗲𝘀 𝗻𝗼𝘁 𝗶𝗺𝗽𝗹𝘆 𝗿𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆
Only 𝘃𝗲𝗿𝗶𝗳𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 creates trust.
LLMs sit outside the trust boundary. Their outputs must be validated, and 𝗻𝗼 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝘀𝗵𝗼𝘂𝗹𝗱 𝗿𝗲𝗹𝘆 𝗼𝗻 𝘂𝗻𝘃𝗲𝗿𝗶𝗳𝗶𝗲𝗱 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗰𝗼𝗻𝘁𝗲𝗻𝘁.
Auditability requires knowing 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲 𝘀𝘆𝘀𝘁𝗲𝗺 𝘂𝘀𝗲𝗱 in terms of sources, data, or evidence behind an output. This is why we build 𝗮𝘂𝗱𝗶𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗯𝘆 𝗱𝗲𝘀𝗶𝗴𝗻 into every step. We ensure every AI‑assisted step can be traced, checked, and justified.
Chaining multiple AI steps without verification doesn’t create more intelligence, it creates compounded uncertainty.
The principle is simple:
𝗔𝘂𝗱𝗶𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗶𝘀𝗻’𝘁 𝗮𝗯𝗼𝘂𝘁 𝘁𝗿𝘂𝘀𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹. 𝗔𝘂𝗱𝗶𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗶𝘀 𝘃𝗲𝗿𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻.
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