Table of Contents
An examiner asks: "Why wasn't this message flagged?"
You have two options.
Option one: "Our AI determined it was low-risk."
Option two: You show them the documented reasoning - template match percentages, sender verification, keyword analysis, and historical precedent from 500 similar cleared messages.
The first answer creates examination risk. The second demonstrates documented oversight.
That's the difference between black-box AI and glass-box AI.
Why Explainability Matters in Regulated Industries
Compliance teams operate under documentation requirements that haven't changed with AI adoption. FINRA 3110 and SEC 17a-4 still demand thorough review and documented rationale for supervisory decisions. When your system dismisses an alert - whether through human review or automated filtering - examiners expect you to explain why.
Many vendors added "explainable AI" to their compliance tools. The problem is that they provide summaries without complete reasoning. "High confidence this is safe" or "Risk score: 0.12" tell you the outcome, not the logic behind it.
What compliance teams need is the full picture: confidence scores and AI recommendations backed by documented rationale. It's not enough to know the system flagged or filtered something - you need to show what it saw, what it compared against, and why it reached that conclusion.
Without that documentation, automation becomes a regulatory liability instead of an efficiency gain.
How Sentinel AI Documents Every Decision
Sentinel AI reviews every alert before it reaches your team, automatically filtering obvious false positives while documenting the rationale behind each decision. Every alert - including those filtered automatically - is archived with complete reasoning that shows examiners exactly why the system took that action.
When Sentinel AI processes a message, it generates examination-ready documentation that compliance teams can reference during audits. This isn't about adding documentation as an afterthought; the reasoning is built into every automated decision from the start.
What Explainability Means for AI Compliance Decisions
Here's what Sentinel AI's glass-box approach looks like:
"Message was filtered because it matches 98% with template ID-4521 (monthly newsletter), sent by verified internal address, contains no flagged keywords, and 500 identical messages were previously cleared."
Let’s break down how each component serves a specific documentation purpose:
Template match (98%): Shows pattern recognition based on message structure and content, not just keywords.
Verified internal address: Demonstrates sender validation, a basic supervisory requirement.
Keyword analysis: Confirms no regulatory language triggered scenarios despite template match.
Historical precedent (500 messages): Establishes consistency with past supervisory decisions.
Compare this to a black-box equivalent: "Message scored 0.12 on risk scale - below threshold for review."
What does 0.12 mean? What factors contributed? How do you explain that number to an examiner? The score might represent sophisticated analysis, but without documented reasoning, it's examination risk disguised as efficiency.
Glass-box AI means showing your work, not just your results.
Regulatory Requirements for AI Systems
AI filtering decisions require the same documentation rigor as human dismissals. The compliance officer who bulk-actions 50 newsletter alerts must document that decision. The AI system that automatically filters 5,000 similar messages must meet the same standard.
Glass-box AI turns this from potential liability into examination advantage. When regulators review your supervisory procedures, documented AI reasoning demonstrates thorough oversight. When they question specific messages, you provide concrete rationale.
As AI regulations develop, transparency becomes the baseline expectation. Glass-box intelligence already meets it.
Meeting Examiner Expectations with Documented AI Decisions
Let's return to that examiner question: "Why wasn't this message flagged?"
Black-box AI leaves you defending technical capabilities - model confidence, risk thresholds, algorithmic decisions. Glass-box AI lets you show documented supervisory rationale that happens to be automated.
Sentinel AI processes your entire alert stream automatically while maintaining complete documentation for every decision. The number of alerts reaching your team drops by 98%. Review time falls from 18 hours per week to 90 minutes. When examiners ask questions, you have answers.
Confidence scores with context. AI recommendations backed by rationale. Documented reasoning for every message.
Glass-box AI means compliance automation that stands up to scrutiny.
Want to see glass-box AI in action? Schedule a demo and we'll walk you through Sentinel AI's documented decision-making.
