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An auditor's desk of files, ledger and magnifying glass under a warm lamp — audit planning
GRC SUITEAGENT-NATIVE

Audit execution at scale.

AWB manages the full audit calendar — 572 audits at last count — with list and calendar views, planned-versus-actual analytics, MCP-driven evidence processing and observations routed to task owners.

What is Auditor Workbench?

Auditor Workbench (AWB) is Moderor's audit planning and execution product. It tracks audits end-to-end with planned-vs-actual analytics, processes evidence from MCP-connected sources, and routes observations to named task owners.

Observed In Production
  • 572 AUDITS UNDER MANAGEMENT
  • 510 OBSERVATIONS ROUTED
  • LIST + CALENDAR PLANNING
  • MCP-DRIVEN EVIDENCE — 823 RECORDS/AUDIT
demo.moderor.ai
Auditor Workbench dashboard in Moderor.ai

Capabilities

/ FEATURES
Ø1

Audit lifecycle tracking

475 pending and 97 completed audits tracked with status, owner and schedule in one workbench.

Ø2

Planned vs actual analytics

Dashboards compare planned and actual audit progress so slippage is visible before deadlines break.

Ø3

MCP-driven audits

Audits process records directly from connected sources — one in-progress audit was crunching 823 records from a live MCP connection.

Ø4

Observation routing

510 observations flow to task owners with lifecycle states, keeping findings out of spreadsheets.

By the numbers

/ PROOF
0
Audits managed
0
Observations routed
0
Audits completed
0
Records in one MCP audit

Questions, answered

FAQ
What is Auditor Workbench?

Auditor Workbench is Moderor's audit management product: it plans audits on a list or calendar, tracks planned-versus-actual execution, processes evidence from MCP-connected data sources and routes observations to task owners.

Can audits pull evidence automatically?

Yes. Audits can be MCP-driven — connected sources stream records into the audit for agent analysis, replacing manual evidence collection.

How many audits can AWB handle?

The reference deployment manages 572 concurrent audits with 510 routed observations, and the architecture scales with your LOB structure.

See it on your data.

Connect a source over MCP, point an agent at a control set, and watch the first findings arrive — with you in command.