SAR Guard
Responsible AI governance and risk management for Suspicious Activity Reporting, helping Federal Reserve Board staff use AI safely while meeting AML, governance, and federal compliance requirements.
The Problem
“Governed AI for Suspicious Activity Reporting in a Federal AML environment”
Organizations face these key challenges:
Frequent policy changes are difficult to operationalize quickly across monitoring systems
AML investigators face large alert volumes and limited capacity
SAR review practices vary across institutions and examiner teams
Third-party AI compliance evidence is fragmented and manually collected
Approval workflows for regulated AI use cases are slow and inconsistently enforced
AI systems may be brittle, vulnerable, or insufficiently tested before deployment
Audit and model risk teams require explainability, lineage, and reproducible evidence
Federal compliance constraints limit use of opaque or weakly governed AI tooling
Impact When Solved
The Shift
Human Does
- •Review proposed SAR-related AI uses against governance, AML, privacy, security, and records policies
- •Interpret whether a use case is allowed, restricted, or prohibited and identify required approvals
- •Compile risk assessments, control evidence, and approval records in manual review documents
- •Escalate unclear or high-risk cases to compliance, legal, or model risk reviewers
Automation
- •No AI-driven governance triage or policy decision support is applied
- •No automated mapping of use cases to risk tiers or required controls is available
- •No continuous checking of prompts, outputs, or usage patterns against policy occurs
- •No automated generation of audit-ready documentation or decision logs is performed
Human Does
- •Approve, restrict, or reject proposed AI use cases based on risk tier and required controls
- •Review escalated exceptions, ambiguous policy conflicts, and high-risk SAR workflow decisions
- •Provide required sign-off for sensitive actions, approvals, and policy deviations
AI Handles
- •Classify proposed AI use cases by risk, data sensitivity, operational impact, and policy fit
- •Retrieve and summarize applicable governance, AML, privacy, and records requirements with citations
- •Identify mandatory controls, approval steps, and prohibited uses and generate draft review packets
- •Monitor prompts, outputs, access, and usage patterns for policy violations or emerging risks
Operating Intelligence
How SAR Guard runs once it is live
AI runs the first three steps autonomously.
Humans own every decision.
The system gets smarter each cycle.
Who is in control at each step
Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.
Step 1
Assemble Context
Step 2
Analyze
Step 3
Recommend
Step 4
Human Decision
Step 5
Execute
Step 6
Feedback
AI lead
Autonomous execution
Human lead
Approval, override, feedback
AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.
The Loop
6 steps
Assemble Context
Combine the relevant records, signals, and constraints.
Analyze
Evaluate options, risk, and likely outcomes.
Recommend
Present a ranked recommendation with supporting rationale.
Human Decision
A human accepts, edits, or rejects the recommendation.
Authority gates · 1
The system must not approve, restrict, or reject a proposed AI use case without designated human review and sign-off. [S2][S5][S7]
Why this step is human
The decision carries real-world consequences that require professional judgment and accountability.
Execute
Carry out the approved action in the operating workflow.
Feedback
Outcome data improves future recommendations.
1 operating angles mapped
Operational Depth
Technologies
Technologies commonly used in SAR Guard implementations:
Key Players
Companies actively working on SAR Guard solutions:
Real-World Use Cases
Concise no-SAR decision documentation support
Compliance teams can use AI-assisted drafting to create short internal notes explaining why an alert did not become a SAR, but only if the bank chooses to keep such records.
Battlefield AI robustness testing and red-teaming program (SABER)
A government program to stress-test military AI so it keeps working, stays secure, and does not fail in dangerous battlefield conditions.
AI governance dashboard for third-party compliance evidence
A financial firm can build a dashboard that automatically shows whether outside vendors tied to AI projects are properly registered, qualified, or excluded, so governance teams have evidence in one place.
AI-based SAR triage prioritization aligned to law-enforcement value
Instead of treating every alert the same, AI helps rank which suspicious cases are most worth human attention and reporting.
AML/CFT national priorities and special measures alerting
AI helps banks quickly update monitoring when regulators highlight new money-laundering threats or impose special measures on certain risks.