Approval-Driven Workflow Orchestration for Trust and Safety
Workflow management system that captures approver inputs as structured variables for downstream conditional execution and supports modular trust-and-safety workflows such as fake account detection, account takeover response, content moderation, and anti-scraping.
The Problem
“Approval-Driven Workflow Orchestration for Trust and Safety”
Organizations face these key challenges:
Approver decisions are captured in unstructured notes and cannot reliably trigger downstream logic
Monolithic abuse scoring systems are hard to evolve as products and attack patterns change
Operational teams rely on manual handoffs across tools and queues
New trust-and-safety workflows require engineering-heavy custom integrations
Impact When Solved
The Shift
Human Does
- •Review abuse cases across queues and gather evidence from multiple tools
- •Record approvals, decisions, and rationale in tickets or case notes
- •Manually hand off cases for escalation, enforcement, or follow-up actions
- •Apply domain-specific judgment for fake accounts, ATO, moderation, and anti-scraping cases
Automation
- •Run monolithic abuse scoring to flag suspicious activity
- •Apply hard-coded business rules for basic routing and enforcement
- •Generate fragmented alerts or signals used by analysts during review
Human Does
- •Approve sensitive enforcement actions and set structured decision variables
- •Review AI recommendations and resolve ambiguous or high-risk cases
- •Handle policy exceptions, escalations, and cross-workflow conflicts
AI Handles
- •Classify abuse patterns and route cases into the right trust-and-safety workflow
- •Extract structured signals from analyst inputs and case artifacts
- •Pre-fill approval forms with recommended actions, rationale, and missing evidence prompts
- •Execute deterministic branching, downstream actions, and monitoring based on approved variables
Operating Intelligence
How it works
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 take sensitive enforcement action without approval from a trust-and-safety reviewer or designated approver. [S1][S2]
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
Real-World Use Cases
CASAL modular workflows for fake account, account takeover, content moderation, and anti-scraping
Instead of one giant anti-abuse system, LinkedIn built a shared library where teams can plug in separate AI workflows for different threats like fake accounts or scraping.
Approver-provided variables for downstream conditional execution
When someone approves, they can fill in fields that later steps use to decide what to do next.